If you have ever tried to plan a long road trip, you know the challenge. Do you map out every single rest stop for the next 2000 kilometers? Or do you just plan the major cities you will pass through? The answer depends on how much detail you need and how far ahead you are looking.   Business Evolution Plans (BEP) in SAS Asset and Liability Management on Viya work the same way. The horizons you choose determine how granular your balance sheet projections will be and how far into the future you can see. Get this wrong, and you are either drowning in unnecessary detail or missing critical turning points in your portfolio evolution.   This post will walk you through everything you need to know about horizons in BEP’s, from choosing the right interval type to some lesser known settings that can save you headaches down the road.     Understanding horizon basics: Where does your journey begin?   Before we look into the different interval types, let us talk about where every BEP starts: Horizon Zero.   When you create a Business Evolution Plan and select your planning data and target variable, the solution automatically populates a column called Horizon 0. This is your starting point, your "you are here" marker on the map. It shows the current value of your target variable, whether that is ending book balance, par balance, or market value, pulled directly from your portfolio data.   Think of Horizon 0 as the baseline against which all your growth or liquidation targets will be measured. If your mortgage portfolio shows 500 million euros in Horizon 0, and you set a 10% growth target for Horizon 1, the system knows exactly what that 10% means. Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   Pro tip: If you want market value as your target variable, you need to use the Portfolio Summary table as your planning data. The regular portfolio table does not include market value calculations because those happen during the Preview Portfolio step.     The four interval types: Choosing your planning granularity   SAS ALM gives you four options for how to slice up your planning horizon. Each serves a different purpose, and choosing the right one depends on what you are trying to achieve.   Monthly Intervals   Monthly intervals give you the finest level of control for standard business planning. You specify how many months you want to project, and the system creates that many horizons. If you enter 24 in the interval count field, you get two years of monthly projections.   This works well for detailed operational planning, near term liquidity analysis, and when your business strategy involves month by month targets. The downside is that managing 24 or 36 individual horizon values can get tedious, especially if your growth assumptions do not actually change much from month to month.   Quarterly Intervals   Quarterly is often the sweet spot for many banks. It aligns nicely with financial reporting cycles and board presentations. Most strategic plans operate on a quarterly cadence anyway, so your BEP naturally mirrors how leadership thinks about the business.   With quarterly intervals, an interval count of 8 gives you two years of projections in a much more manageable format than 24 monthly buckets.   Yearly Intervals   Yearly intervals are best for long term strategic planning. If you are modeling a five year business transformation or assessing the impact of a major market shift over an extended period, yearly horizons keep things simple.   The trade off is precision. A lot can change in twelve months, and yearly intervals smooth over all that variation. Use this when the big picture matters more than the quarterly details.   IRRBB Intervals: The Regulatory Option   This is the special one. When you select IRRBB as your interval type, you do not specify an interval count. Instead, the system automatically creates a predefined set of horizons based on Basel regulatory requirements.   The IRRBB interval structure looks like this: ON (overnight), 1M, 3M, 6M, 9M, 12M, 18M, 2Y, 3Y, 4Y, 5Y, 6Y, 7Y, 8Y, 9Y, 10Y, 15Y, 20Y, and 20Y+.   Notice how the granularity changes over time. The near term is sliced finely because that is where interest rate risk has the most immediate impact. As you move further out, the intervals widen because longer term projections naturally carry more uncertainty.   If you are running IRRBB compliance calculations, this is the interval type you need. It ensures your BEP aligns with the standard Basel time bucket specification.       The BEP Horizon Start Interval   Here is a feature that not everyone knows about but can be incredibly useful. When you configure your ALM calculation parameters for a dynamic growth run, you will see a field called BEP horizon start interval.   The default value is 1, which means your analysis starts from the first period in your Business Evolution Plan. But you can set this to any number, even a number greater than the total horizons in your BEP.   Why would you want this? Imagine you created a quarterly BEP back in January with 8 quarters of projections. It is now July, and you want to run an analysis. Setting the horizon start interval to 3 tells the system to skip the first two quarters, which are now in the past, and start from Q3.   This saves you from having to recreate your entire BEP every time you run a new cycle. You built the plan once with a longer time horizon, and now you can "slide forward" through it as time progresses.     Rollover Horizon Date: The performance lever   When running static or dynamic growth analyses that involve rollovers, you will encounter the rollover horizon date setting. This one is primarily about performance, but understanding it helps you avoid confusion in your results.   The rollover horizon date sets a cutoff point for generating rollover transactions. Once this date is reached, the system stops creating new rollover positions. The balance calculation shifts from including current business plus rollover business plus new business, to just current business plus new business.   Why does this matter? Generating rollover transactions is computationally expensive. If you are projecting 20 years into the future, do you really need the system to create detailed rollover positions for year 19? Probably not. Setting a reasonable rollover horizon date, maybe 5 or 10 years out, can significantly speed up your analysis without meaningfully affecting your results for the periods you actually care about.   Here you can see all the settings mentioned above:     Practical tips from the field   After working with Business Evolution Plans across different implementations, here are some lessons learned.   First, match your interval type to your decision cycle. If your ALCO meets monthly, monthly intervals make sense. If strategic planning happens annually, do not create unnecessary complexity with monthly buckets.   Second, remember that homogeneous intervals are required except for IRRBB. You cannot mix monthly and quarterly horizons in the same BEP. If you need different granularity for different time periods, IRRBB intervals handle this automatically, or you may need separate BEPs for different purposes.   Third, use the Excel export feature for complex BEPs. When you have multiple segments with different growth assumptions across many horizons, populating everything in the UI becomes painful. Export to Excel, set up your formulas and references there, validate your numbers, then import back. The solution runs validation checks on import to catch formatting issues before they cause problems downstream.   Fourth, always test in Developing state first. You cannot edit a Production BEP, so make sure everything works correctly before you flip that switch. Use the Testing state for integration checks with your allocation schemes and calculation runs.     Conclusion   Getting horizons right can make the difference between a BEP that actually helps you make decisions and one that collects dust. Think about it the same way you would plan that road trip. How far are you going? How many stops do you need to plan? And most importantly, what will you actually do with the map once you have it?   Whether you go with IRRBB intervals for regulatory work, monthly buckets for operational detail, or yearly projections for the long view, pick what fits how your team actually works. There is no point building a 36 month monthly forecast if your ALCO only looks at quarterly numbers anyway.   For more detailed guidance on configuring Business Evolution Plans, check out the SAS Asset and Liability Management documentation on the SAS support site.   For more information on SAS Risk Management Solutions visit the software information page here. For more information on curated learnings paths on SAS Solutions and SAS Viya, visit the SAS Training page. You can also browse the catalog of SAS courses here.   Abbreviations and terms   ALM: Asset and Liability Management ALCO: Asset and Liability Committee BEP: Business Evolution Plan EVE: Economic Value of Equity IRRBB: Interest Rate Risk in the Banking Book NII: Net Interest Income ON: Overnight (the shortest IRRBB time bucket)     Find more articles from SAS Global Enablement and Learning here. ... View more

Think of a bank risk analysis like making a big family dinner. You cannot just throw ingredients into a pot and hope for the best. You need a recipe, you need to check if the food is fresh, and you need to follow steps in the right order. That is exactly what the SAS Asset and Liability Management (ALM) workflow does for your risk calculations.   The purpose of this post is to use every day analogies to walk you through the complete journey of transforming raw portfolio data into polished risk reports that executives can trust. We will cover each step of the workflow in plain language, showing you how everything connects from data ingestion to final approval.     But what is a cycle anyway?   Before we start cooking, we need to understand what a cycle is. A cycle is like planning one complete dinner from start to finish. It has a specific date (your reporting date), specific ingredients (your portfolio data), and specific guests (your target audience). Each cycle follows the same recipe but can use different ingredients based on what you need to analyze.   The beauty of cycles is consistency. Every time you run a cycle, you follow the same steps. This creates a clear paper trail that makes auditors happy and keeps everyone on the same page. Below you can see a complete cycle workflow as it appears in the SAS ALM Solution. Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.     Step 1: Getting your ingredients ready (Process data inputs)   Every good meal starts with checking your ingredients. You would not use spoiled milk in your recipe, right? Same idea here.   This first phase has four mini steps:   Ingest data is like bringing all your groceries into the kitchen. You tell the solution where your portfolio data lives (in what we call a CASLIB) and which tables you want to use. The solution grabs your portfolio data and your synthetic instruments and gets them ready for checking.   Quick note on synthetic instruments: these are hypothetical transactions you plan to add to your balance sheet in the future. Think of them as future business you expect to write. For example, if you know you will issue 50 million euros in new loans next quarter, you model those as synthetic instruments to see how they will affect your risk profile. And here is a helping visual to understand synthetic instruments better. Run data quality is your quality inspection. The solution comes with built in rules that check for problems. Think of it like checking if your eggs are cracked or if your vegetables are fresh. The solution looks for missing values, weird dates, and amounts that do not make sense.   Review data quality is when you look at the inspection report. You get a visual dashboard showing you what passed and what failed. Here you make a choice: approve it if it looks good, reject it if there are too many problems, or adjust it if you can fix the issues.   Apply corrections is your chance to fix problems. Maybe some interest rates are missing or some dates are formatted wrong. You can create automated rules that clean up common problems. Once you fix things, you run the data quality check again to make sure everything is clean. Here is a quick look at how that works.       Step 2: Taking a good look at what you have (Preview portfolio)   Now that your data is clean, you want to know what you are working with. This is like laying out all your prepped ingredients on the counter and checking quantities.   Run portfolio summary takes your clean portfolio data and applies the latest market data. It calculates the market value of every instrument as of your reporting date. The results get grouped by product type so you can see the big picture.   Review portfolio data lets you look at this summary and decide if it makes sense. Is like you suddenly notice you have 5 lemons on your counter top while the recipe only calls for half a lemon squeeze, so something is off.     Step 3: Planning different scenarios (Configure assumptions)   Here is where things get interesting. You are not just cooking one version of the meal. You are planning for different possibilities. What if interest rates go up? What if your deposit base shrinks?   Create scenario sets is like planning different versions of your recipe. Maybe one scenario assumes rising rates, another assumes falling rates, and another assumes stable rates.   Create business evolution plans is planning how your balance sheet will change over time. What new business will you write? What will mature? Think of it like planning what groceries you will buy next week and the week after.   Create allocation schemes tells the solution how to spread new business across different products and segments. If you get 100 million euros in new deposits, where do they go?     Step 4: Running the calculations (Run ALM calculations)   Now you are cooking. This is where all your prep work pays off.   You configure your parameters (telling the solution exactly how to run the analysis), pick your scenarios, and hit go. The solution generates cash flows, calculates interest rate risk measures, projects your balance sheet forward, and produces earnings forecasts.   When it finishes, you review the results. Are they reasonable? Do they match your expectations? If yes, you approve them. If no, you reject them and adjust your assumptions.     Step 5: Final approval (Sign off)   This is the final taste test. A manager or senior analyst (such as your mother in law) reviews everything you cooked and gives the final approval. Once approved, the cycle is locked. No more changes. No more ingredients. This creates accountability and ensures your results are official and the dinner can be served.     The magic of visual reports   Throughout this entire journey, SAS ALM generates visual reports that make complex data easy to understand. You get:   A data quality report showing what passed and failed inspection A balance and cash flows report showing where your money is going An economic value measures report showing your interest rate risk An earnings based measures report showing projected income A dynamic balance sheet forecast report showing how your balance sheet evolves A scenario assumptions report showing what assumptions you used   These reports are not just fixed tables of numbers in a pdf, they are interactive dashboards where you can click around, drill down, and explore your data. See for yourself an example below.       Why this matters?   This structured workflow makes your risk analysis reliable, repeatable, and defensible. When a regulator asks how you calculated something, you can show them the exact path the data took from ingestion to final report.   Think of it like having a GPS for your risk analysis. You always know where you are, where you have been, and where you are going next.   Conclusion   The SAS ALM solution workflow transforms raw data into trusted insights. By following the same path every time, you build confidence in your results and make your risk management program something you can actually depend on.   For more information on SAS Risk Management Solutions visit the software information page here. For more information on curated learnings paths on SAS Solutions and SAS Viya, visit the SAS Training page. You can also browse the catalog of SAS courses here.     Find more articles from SAS Global Enablement and Learning here. ... 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Have you ever found yourself waiting hours for your Asset Liability Management (ALM) analysis to complete, wondering if there’s a faster, more efficient way to get the insights you need? In the fast-paced world of financial risk management, time is a critical resource. The choice of your ALM analysis methodology can be the difference between spending your day waiting or making timely, data-driven decisions.   The purpose of this post is to guide you through the different types of calculations you can perform in SAS ALM on Viya. We will explore the three main analysis types, Run-off, Static Balance, and Dynamic Growth, and provide a comprehensive framework for choosing the right approach for your specific needs. By the end of this post, you will have a clear understanding of how to optimize your ALM analysis for both performance and accuracy, enabling you to unlock the full potential of SAS ALM on Viya.   In this post, you will learn:   The three main analysis types: Run-off, Static Balance, and Dynamic Growth. Practical strategies for optimizing performance and resource utilization. A decision framework to help you select the right analysis type for your business needs.     Why your choice of analysis type matters   The power and flexibility of SAS ALM on Viya mean that you have a wide range of analytical capabilities at your fingertips. However, with great power comes the need for careful consideration. The type of analysis you choose to run will have a significant impact on both the performance of the system and the business value of the results. Choosing an analysis type that is too complex for your needs can lead to unnecessarily long processing times and high resource consumption. Conversely, an analysis type that is too simplistic may not provide the depth of insight required for effective decision-making.   This is why understanding the different analysis types available in SAS ALM on Viya is so crucial. By matching the right analysis type to your specific business objective, you can strike the optimal balance between performance, accuracy, and business relevance. In the next section, we will take a deep dive into the three main analysis types, exploring their characteristics, use cases, and performance implications.     The three analysis types   Each method offers a different perspective on your balance sheet, and understanding their nuances is key to effective risk management.     1. Run-off Analysis: The "Let It Be" approach   Run-off analysis is the most straightforward of the three analysis types. It provides a view of your balance sheet as if you were to cease all new business activities and simply let your existing assets and liabilities run their course until maturity. This approach is akin to watching your current bank account slowly deplete without making any new deposits. The focus is solely on the cash flows generated by your existing portfolio.   In SAS ALM on Viya, a run-off analysis is configured by specifying that no new business should be generated. The system then calculates the contractual cash flows for all instruments in the portfolio, taking into account prepayments and other behavioral assumptions. The analysis continues until all instruments have matured or the analysis horizon is reached.   Performance Characteristics   Given its simplicity, run-off analysis is the fastest of the three analysis types. The system only needs to process the existing portfolio, without the computational overhead of generating new business or rollover transactions. This makes it an ideal choice when you need a quick assessment of your current risk position.     2. Static Balance Analysis: The "Steady State" method   Static balance analysis takes a step up in complexity from the run-off approach. It assumes that as existing assets and liabilities mature, they are replaced with new instruments of similar characteristics, thus maintaining a constant balance sheet size. This is like having a monthly budget that you stick to indefinitely; as you spend money, you replenish it to maintain the same overall balance.   A key concept in static balance analysis is the rollover horizon. This setting determines the period during which maturing instruments are replaced. When an instrument matures within the rollover horizon, the system generates a new instrument with the same notional value and repricing characteristics. The interest rate on the new instrument is determined by the prevailing market rates at the time of rollover, as defined by the scenario being run.   Rollover Horizon Optimization   The choice of rollover horizon is a critical performance lever. A longer rollover horizon will result in more calculations, as the system will need to generate more rollover transactions. It is important to choose a rollover horizon that is appropriate for your analysis objectives. For most standard risk reporting, a rollover horizon of 5 to 10 years is sufficient. Setting the horizon too far into the future can significantly increase calculation times with little to no added analytical value (as can bee seen in the graph below).   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   Performance Characteristics   The performance of a static balance analysis is primarily driven by the length of the rollover horizon and the number of scenarios being run. By optimizing the rollover horizon and being selective with your scenarios, you can achieve a good balance between performance and analytical depth.     3. Dynamic Growth Analysis: The "Full Planning" approach   Dynamic growth analysis is the most sophisticated and comprehensive of the three analysis types. It allows you to model the evolution of your balance sheet over time, taking into account new business growth, changes in customer behavior, and the introduction of new products. This is akin to creating a detailed business plan that projects your financial position into the future.   The Business Evolution Plans (BEP)   The core of dynamic growth analysis in SAS ALM on Viya is the Business Evolution Plan (BEP). A BEP is a structured framework that allows you to define your growth projections for different segments of your portfolio. You can specify target variables such as Book Balance, Par Balance, or Market Value, and define how these variables will change over time. More information on how to create a BEP can be found in an earlier post here.   Performance Considerations   Given its complexity, dynamic growth analysis is the most resource-intensive of the three analysis types. The performance will depend on the number of segments in your BEP, the complexity of your growth projections, and the number of scenarios you are running. It is important to keep your BEPs as simple as possible while still meeting your business needs.     Performance Optimization Strategies   While SAS ALM on Viya is a high-performance platform, there are several strategies you can employ to further optimize your analysis and reduce processing times. By following these best practices, you can ensure that you are getting the most out of the platform without sacrificing analytical rigor.   Portfolio Selection: Whenever possible, use adjusted portfolio data for your analysis. While it may seem counterintuitive, adjusted data often runs faster because data quality issues have already been resolved. It’s like using ingredients that you’ve already cleaned and prepped versus starting with raw materials. Scenario Management: Be selective with the scenarios you run. For regular monitoring, 2-3 core scenarios are often sufficient. Reserve comprehensive scenario sets for deep-dive analysis or regulatory submissions. Rollover Horizon Tuning: As discussed earlier, the rollover horizon is a critical performance lever. Set your rollover horizon to match your analysis needs, not your maximum time bucket. A European bank was able to reduce their analysis time by 40% simply by adjusting their rollover horizon from 15 years to 7 years, with no meaningful impact on their results. BEP Complexity Management: Keep your Business Evolution Plans as simple as possible while still meeting your needs. Use fewer segmentation dimensions when possible and choose appropriate target variables.   The following table provides a general indication of the best practices of selecting parameters for each analysis type, based on a typical mid-sized bank portfolio.       Analysis type decision tree   The following decision tree provides a step-by-step guide to selecting the right analysis type. Start by identifying your primary purpose for the analysis, and then follow the branches to determine the most suitable approach.       Conclusion and Action Items   In this post, we have explored the different types of calculations you can perform in SAS ALM on Viya and provided a best practices framework for choosing the right analysis type for your specific needs. We have seen that the choice of analysis type has a significant impact on both the performance of the system and the business value of the results. By following the best practices and using the decision framework outlined in this post, you can optimize your ALM analysis for both performance and accuracy, enabling you to unlock the full potential of SAS ALM on Viya.   Now that you have a better understanding of the different analysis types in SAS ALM on Viya, here are a few action items to get you started:   Review Your Current Setup: Take a look at your current ALM analysis setup. Are you using the right analysis type for each use case? Could you optimize your rollover horizons? Start Simple: If you are new to SAS ALM on Viya, start with a simple run-off or static balance analysis to get a feel for the platform. Experiment: Don’t be afraid to experiment with different analysis types and configurations to see what works best for your institution.   By taking a thoughtful and strategic approach to your ALM analysis, you can ensure that you are getting the most out of your investment in SAS ALM on Viya and making better, more informed decisions for your institution.   For more information on SAS Risk Management Solutions visit the software information page here. For more information on curated learnings paths on SAS Solutions and SAS Viya, visit the SAS Training page. You can also browse the catalog of SAS courses here.     Find more articles from SAS Global Enablement and Learning here. ... View more

Meet Sarah, a risk manager at a mid-sized bank. Every month, she spends her first week collecting data from 12 different systems, manually copying and pasting numbers into spreadsheets. By the time she finishes her Asset and Liability Management (ALM) analysis, the market has moved, and her insights are already outdated.   Sarah's story isn't unique. A lot of banks are still running their ALM processes like it's 1995, and the hidden costs are enormous. The purpose of this post is to reveal the true costs of manual ALM processes and show how SAS ALM on Viya can transform your risk management from a time-consuming burden into a strategic advantage.     The monthly ALM nightmare   It is month end, and Sarah's team is pulling 60-hour weeks. They are manually extracting data from the core banking system, the loan origination platform, the deposit system, and nine other sources. Each extraction requires different login credentials, different export formats, and different data cleaning and manipulation procedures.   The real cost? Sarah's bank spends north of €50,000 in overtime every month, and their regulatory reports are consistently two weeks late. When interest rates jumped 75 basis points last quarter, they were making critical decisions based on three-week-old data.   But here is the kicker: Sarah's team isn't analyzing risk or developing strategy. They are glorified data entry clerks, and they know it.     The Three Hidden Costs Killing Your ALM Efficiency   The Time Drain Research shows that 40% of ALM staff time is spent on data collection, not analysis. Think about that for a moment. Your most skilled risk professionals are spending nearly half their time doing work that a software could do in minutes.   Sarah's team of five risk analysts spends 80 hours each month just gathering data. That's two full work weeks where no actual risk analysis happens. Meanwhile, markets are moving, customer behaviors are shifting, and competitive threats are emerging.   The Error Risk Manual processes create a 15% error rate in critical calculations. When humans copy and paste thousands of numbers, mistakes happen. A misplaced decimal point in a duration calculation can throw off your entire interest rate risk assessment.   Last year, a European bank discovered they had been using the wrong maturity dates for their mortgage portfolio for six months. The error? Someone had manually entered "30" instead of "360" in a key formula. The regulatory fine was substantial, but the reputation damage was worse.   The Opportunity Loss While your team is busy collecting yesterday's data, your competitors are making decisions based on real-time insights. In today's fast-moving markets, decisions made on week-old data are more like guesses than strategic choices.   During the March 2023 banking crisis, successful institutions were able to model and respond to deposit flight patterns within hours. Banks relying on manual processes were still calculating their exposure days later.     SAS Asset and Liability Managment on Viya: Your Automation Solution   Here's where SAS ALM on Viya changes everything. Instead of spending weeks collecting data, your team can focus on what they do best: analyzing risk and developing strategy.   Automated Data Integration SAS ALM on Viya connects directly to all your systems. No more manual extractions, no more copying and pasting, no more version control nightmares. Your data flows automatically from source systems into your ALM calculations.   Sarah's bank implemented this integration this year. What used to take five people a week now happens automatically overnight. The system pulls data from their core banking platform, applies data quality rules, and loads clean, validated information into their ALM models.   Real-Time Calculations Instead of monthly snapshots, you get continuous risk monitoring. Interest rate sensitivity, liquidity gaps, and capital adequacy calculations update as new transactions flow through your systems.   When the European Central Bank announced an unexpected rate change last month, Sarah's team had updated risk assessments within hours, not weeks. They could model the impact on their net interest margin and adjust their strategy immediately.   Built-In Validation SAS ALM on Viya includes automatic error checking and data quality controls. The system flags anomalies, validates calculations, and ensures consistency across your entire ALM framework.   Those decimal point errors that plagued manual processes? They're caught automatically. The system validates that duration calculations make sense, that cash flows balance, and that risk metrics fall within expected ranges.   Instant Reporting Generate regulatory reports in minutes, not days. IRRBB reports, liquidity coverage ratios, and stress test results are produced automatically from your integrated data.   Sarah's team now generates their monthly board report in 30 minutes instead of three days. The report includes current risk metrics, scenario analysis, and forward-looking projections that help executives make informed decisions.     The Transformation: Before vs. After   Let's look at the numbers from Sarah's bank:   Before SAS ALM on Viya:   3 weeks to complete monthly ALM cycle 5 people working 60-hour weeks during month-end 15% error rate in manual calculations Regulatory reports submitted 2 weeks late Strategic analysis limited to quarterly reviews   After SAS ALM on Viya:   3 days to complete monthly ALM cycle Team focused on strategic analysis and scenario modeling Less than 1% error rate with automated validation Regulatory reports submitted on time with detailed analysis Continuous risk monitoring with real-time alerts   The Bottom Line: 80% reduction in processing time, 95% fewer errors, and real-time insights that enable proactive risk management.   But the real transformation isn't just in the numbers. Sarah's team went from being frustrated data collectors to strategic risk advisors. They're now building sophisticated behavioral models, running complex scenario analyses, and providing insights that drive business decisions.     Your Next Step: Start Your Automation Journey   The banking industry is at a turning point. Institutions that embrace automation will have a significant competitive advantage, while those clinging to manual processes will struggle to keep up.   SAS ALM on Viya provides the complete solution for modern ALM. The platform handles data integration, risk calculations, scenario modeling, and regulatory reporting in a single, integrated environment.   Your Implementation Roadmap:   Start with data integration: Connect your core systems to eliminate manual data collection Implement automated calculations: Replace spreadsheet-based models with integrated risk analytics Build real-time monitoring: Set up alerts and dashboards for continuous risk oversight Expand to advanced analytics: Add scenario modeling, stress testing, and predictive capabilities   The ROI is typically realized within six months, but the strategic benefits will last for years.     The Future Belongs to the Automated   Sarah's story has a happy ending. Her bank now leads their market in ALM sophistication. They make faster decisions, take smarter risks, and consistently outperform their competitors.   More importantly, Sarah and her team love their jobs again. They're doing what they were trained to do: analyse risk, develop strategy, and protect their institution's future.   The question isn't whether to automate your ALM processes. It's whether you'll lead the transformation or be left behind by competitors who moved first.   In the world of banking, speed and accuracy determine who stays in business. As one risk executive recently told me: "We can either spend our time collecting and fixing data or we can spend it making strategic decisions. We cannot do both."   Which one are you going to pick?   Ready to transform your ALM processes? SAS ALM on Viya is the platform that can take you from manual processes to strategic advantage. The technology is proven, the ROI is clear, and your team is ready for the change.   For more information on SAS Risk Management Solutions visit the software information page here. For more information on curated learnings paths on SAS Solutions and SAS Viya, visit the SAS Training page. You can also browse the catalog of SAS courses here.     Find more articles from SAS Global Enablement and Learning here. ... View more

You are planning a dinner party and you have decided to order pizza for everyone. You know you need 5 pizzas total, but how do you decide what types to get? Maybe 2 pepperoni, 2 veggie, and 1 Hawaiian?   That decision-making process is exactly what allocation schemes do in banking, they help you decide how to split up your planned growth across different products.   The purpose of this post is to walk you through the step-by-step process of creating allocation schemes in SAS Asset and Liability Management on Viya, using simple non-jargon language and practical examples that make this complex banking concept easy to understand and implement.     What is an Allocation Scheme anyway?   Consider your bank's future growth plan like that pizza order. Your business evolution plan tells you "we need 5 more pizzas for our party" (or in banking terms, "we need $100 million more in loans"). But the allocation scheme is what decides "2 should be pepperoni, 2 veggie, and 1 Hawaiian" (or "50% should be mortgages, 30% auto loans, and 20% personal loans"). Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   In SAS Asset and Liability Management on Viya, allocation schemes work hand-in-hand with business evolution plans. The business plan says how much you want to grow, and the allocation scheme says exactly where that growth should go.     The setup   Before you can create an allocation scheme, you need a business evolution plan already set up. Just like needing to plan a party before you can decide what pizza to order.   More information on how to setup a Business Evolution Plan can be found in an earlier post here.   Anyway, back to our allocation scheme. Here's how to create one:   Step 1: Navigate to Allocation Schemes screen   Open SAS ALM on Viya Select "Allocation Schemes" object on the left panel Click the "+" button to create a new one   Step 2: Basic information Just like naming your dinner party, you need to give your allocation scheme a name and description. Keeping it simple by using something like "Q1_2025_Growth_Plan" works great.   You also need to pick whether this is for testing (Developing), or ready to use (Production). If you're just learning, stick with Developing for now.   Step 3: Link to your business evolution plan This is where you connect your allocation scheme to your business plan. It's like saying "this pizza order is for Saturday's party, not Sunday's game night."       The fun part: setting up your allocation   Now comes the interesting bit which is deciding how to split things up. In the Weights tab of the allocation scheme screen, you can see all the different segments from your business evolution plan that need growth. Think of these as different groups of friends at your party, some want meat lovers, others want veggie.   Understanding segments Your business evolution plan might have created segments like:   Fixed rate mortgages Variable rate mortgages Auto loans Personal loans   For each segment that's set to grow, you need to decide how that growth gets spread across specific products.  You can do this in the Allocation Type column.   Making your choices You will see two main ways to allocate:   Equal distribution - Split everything evenly, like cutting a pizza into equal slices Custom weights - You decide the exact percentages, like giving your vegetarian friends more veggie pizza   Most banks start with equal distribution and then adjust based on what makes sense for their strategy.   The Segments Overview   This table view style shows you which parts of your business evolution plan need allocation decisions. You'll see columns like:   Segment Name: Shows combinations like "USD/Fixed Rate Mortgages" or "EUR/Auto Loans" Target Type: Displays "Growth/Liquidation" for segments that need allocation Planning Currency: Shows the currency for that segment Target Variable: Indicates what's growing (like "Ending Book Balance") Allocation Type: Select the allocation type     Some tips from the field   Start simple: Don't try to get fancy on your first allocation scheme. Pick a few main products and split growth between them evenly. You can always create more detailed versions later.   Think seasonal: Just like you might order more hearty pizzas in winter, banks often adjust their allocation schemes based on market conditions. Create different schemes for different scenarios.   Test first: Always create your allocation scheme in "Developing" mode first. Run some test scenarios to make sure the results make sense before moving to production.   Document everything: Future you (and your colleagues) will thank you for clear names and descriptions. "Mortgage_Growth_Q1" is much better than "Test_123".     Common pitfalls to avoid   The 100% Trap: Your percentages don't have to add up to exactly 100%. SAS ALM will normalize them for you. So if you put 30%, 30%, and 20% for three products, it'll automatically adjust to make the math work.   The zero weight problem: Make sure at least one product in each segment has a weight greater than zero. Otherwise, you're basically saying "grow this segment but don't actually add any products" - which doesn't make much sense!   The forgotten connection: Always double check that your allocation scheme is connected to the right business evolution plan. It's like making sure you are ordering pizza for the right party.     Conclusion   Creating allocation schemes in SAS ALM on Viya is about being smart about how to grow your bank's business. Start with the basics, test run your plan, and don't be afraid to adjust as you learn more about what will work best for your organization.   The greatest thing about SAS ALM is that you can create a number of different allocation strategies and compare. Try different approaches, see what the figures tell you, and pick the technique that suits your bank's needs best.   Imagine it this way: there is no perfect allocation plan, no more than there is a perfect pizza order for any party. The idea is to understand your audience (your market), your goals (profitable growth), and your constraints (risk), and based on that, make the best of what you got.   Ready to start the party? Then start SAS ALM on Viya and start testing. The pizza isn't going to order itself!   For more information and documentation of SAS Asset and Liability Management on Viya please access the following link     Find more articles from SAS Global Enablement and Learning here. ... View more

Creating accurate projections for your bank's balance sheet can be challenging when different departments have varying growth expectations. Without a structured method, this process often becomes based on educated guesses rather than data.   SAS ALM on Viya offers the Business Evolution Plans (BEP) functionality as a solution to this exact problem. A BEP lets you build a framework for projecting balance sheet changes over time, adding structure and analytical precision to your planning process.   The purpose of this post is to guide you through creating a Business Evolution Plan in SAS ALM on Viya, explaining both the steps and the reasoning behind them.     What is a Business Evolution Plan?   A Business Evolution Plan specifies how target variable values (such as book value or par value) change over specific time intervals for each segment of your portfolio.   BEP’s help you:   Project balance sheet growth or reduction over time Assess how changing portfolio composition affects risk metrics Model new business strategies and their impact on your balance sheet Combine business changes with risk factor changes (when used with scenarios)   The BEP is basically a structured blueprint that transforms strategic plans into concrete projections you can analyze for risk, profitability, and compliance.     What are target variables?   Target variables in a Business Evolution Plan are the specific financial metrics you want to track and project over time. They are the vital signs you monitor when planning your balance sheet's future.   Imagine you are planning a garden. You could focus on different aspects such as:   The number of plants (similar to tracking number of accounts) The height of your plants (similar to tracking par balance) The value of produce you will harvest (similar to tracking book balance) The value of your entire garden (similar to tracking market value)   Each of these measurements tells you something different about your garden's growth. Similarly, in SAS ALM, you might choose:   Book Balance - The accounting value of your instruments Par Balance - The face value of your instruments Market Value - The current worth based on market conditions   The target variable you select becomes the focus of your growth projections. For instance, if you choose Book Balance, all your growth rates will apply to this value, just as a gardener might focus specifically on increasing the total number of vegetables harvested rather than just the number of plants.     Creating a Business Evolution Plan in SAS ALM on Viya   Step 1: Access the Business Evolution Plan business object   Log in to your session of SAS Asset and Liability Management on Viya Go to Business Evolution Plans in the left menu Click the "Create New" button     Step 2: Define the Basic Parameters   In the Details tab, provide:   Business evolution plan ID: A unique identifier Name: A descriptive name (e.g., "Mortgage Growth 2025-2030") Description: Context about the plan purpose and assumptions Interval: Select the time unit (Monthly, Quarterly, Yearly, or IRRBB)   Choose an interval that fits your analysis needs:   Monthly for detailed short-term forecasting Quarterly for medium-term planning (common for banking analysis) Yearly for long-term strategic planning IRRBB for regulatory reporting using standard time buckets   If you select Monthly, Quarterly, or Yearly, you will need to set an Interval count to determine the projection timeframe.     Step 3: Select Your Planning Data and Currency   Planning currency: The currency for growth projections (typically your main operating currency) Planning data: The portfolio data for your starting values Hierarchy data: Usually synthetic instrument data defining available segments   For planning data, you can choose:   The original or adjusted portfolio table from your data quality process The Portfolio Summary table from your Portfolio Preview task   Tip: If you want to use Market Value as a target variable, select the Portfolio Summary table, as it contains market values calculated at time zero.     Step 4: Define your segmentation strategy   This step determines how you will segment your portfolio:   In Variable 1, select your primary segmentation variable (e.g., Product Type, Currency) Add optional secondary segmentation variables in columns 2-5 for more detail Select a Target Variable for each segment (e.g., Current Book Balance, Market Value) Choose a Target Type for each segment: Growth/Liquidation: For segments with new business Growth/Liquidation and Rollovers: For segments with both new business and maturing positions Runoff: For products being phased out (no new business) Static Balance: To maintain consistent balance Select a Target Definition (Incremental Change or Annualized Percent Change)   The strength of this approach is flexibility because you can set different strategies for different portfolio segments.   For example:   Static Balance for established products Growth/Liquidation for expansion areas Runoff for discontinued products   Note: At least one segment must use Growth/Liquidation or Growth/Liquidation and Rollovers for a valid plan.     How to make your life easier using import/export functionality   After the initial setup:   Click export to download your BEP structure as an Excel file Enter growth projections for each segment across all time periods Save the Excel file Return to the BEP screen in SAS ALM and click import to bring in your completed projections Save and close the BEP   This Excel workflow makes it easy to gather input from various business units.     Few tips to consider while creating your Business Evolution Plans   Align with strategic plans: Your BEP should reflect your bank official growth strategy. Start simple: Begin with basic segmentation (by product type and currency), then add detail as needed. Document assumptions: Use description fields and naming conventions to record key assumptions. Create multiple plans: Develop several BEPs (conservative, base case, aggressive) for stress testing. Pair with risk scenarios: Combine your BEP with interest rate scenarios to see how growth plans perform in different rate environments. Update regularly: Review and update your plans quarterly or at least annually.     Common pitfalls to avoid   Too much segmentation: Using all five segmentation variables often creates overly detailed projections that become hard to maintain. Mixed target variables: Using different target variables across similar segments can produce inconsistent results. Missing or wrong allocation schemes: A BEP defines segment-level projections, but you still need an allocation scheme to distribute growth within segments. (we will discuss Allocation Schemes in the next post) Extreme growth rates: Unrealistic projections can lead to questionable risk metrics. Check your projections against historical data and market conditions.     Conclusion   Business Evolution Plans provide a structured approach to balance sheet forecasting in SAS ALM on Viya. By following these steps, you can transform growth plans into clear projections that feed directly into your risk analysis.   A good BEP combines your institution's strategic vision with solid analytical practices for proper risk management.   Have you used Business Evolution Plans in your ALM processes so far? What results have you seen?   Share your experiences below!   For more information, see the SAS ALM documentation.     Find more articles from SAS Global Enablement and Learning here. ... View more

Are you sometimes standing in front of your refrigerator on a Saturday night, hoping food magically appeared since you last checked 10 minutes ago?   That's what I imagine some bank treasury managers feel about their liquidity challenges.   The purpose of this post is to share some interesting insights about bank liquidity management by comparing it to something we all deal with daily: managing our refrigerator inventory.   So let me show you how your refrigerator management skills might actually make you a better banker than you think!   The Refrigerator-Bank Connection Looking inside my own refrigerator, I can see some parallels with bank liquidity management:   I keep essential items (milk, eggs, butter) always in stock = Banks need high-quality liquid assets (HQLA) I maintain emergency frozen pizzas (don't judge) = Banks need emergency funding plans I regularly check expiration dates = Banks monitor maturity profiles I plan for both regular meals and unexpected guests = Banks forecast both expected and stress scenarios I keep some cash ready for grocery shopping = Banks maintain market access   ...however, Last week, I completely failed at refrigerator management (ordered takeout three times), which reminded me of an institution I once worked with. Here's how our stories are a bit similar:   My Failed Refrigerator Strategy: Forgot to check expiration dates Relied on a single grocery store nearby No meal planning Emergency cash spent on impulse purchases   That Bank's Failed Liquidity Strategy: Irregular maturity monitoring Single funding source dependency No clear funding strategy Buffers used for opportunistic trading   The difference?   My failure ended with an expensive takeout bill. Their failure involved some very uncomfortable board meetings.   How SAS Asset and Liability Management on Viya Makes Life Easier for Banks While I'm still waiting for a smart refrigerator that automatically orders and deliveries my groceries, banks already have access to sophisticated tools like SAS Asset and Liability Management on SAS Viya already today:   Real-Time Monitoring Tracks your liquidity position continuously Alerts you before issues become critical Provides detailed dashboards and reports Forecasts future liquidity needs Identifies potential stress points Combines liquidity risk with interest rate risk analysis   And soon, with our integrated balance sheet management solution, you'll be able to:   Manage your entire balance sheet in one place Optimize across multiple risk dimensions Automate regulatory reporting Implement dynamic strategies Make data-driven decisions in real-time   Conclusion Just as refrigerators have evolved from basic igloos to smart appliances, banks must upgrade their liquidity management solution and keep up with the world they are functioning in.   SAS ALM on SAS Viya provides the sophisticated tools needed for this evolution, from advanced analytics to integrated balance sheet management.   The question is: Is your bank ready to move beyond manual processes and complicated spreadsheets?   With today's market complexities and regulatory demands, having the right tools in place isn't just an advantage, it is a necessity.   To learn how SAS Asset and Liability Management on SAS Viya can transform your bank's liquidity management, explore our technical documentation or request a demo today.     Find more articles from SAS Global Enablement and Learning here. ... View more

Do you ever get that scary feeling when you find out your well-thought-out ALM analysis is built on questionable data? Totally been there. It is like putting up a beautiful house on quicksand, no matter how great your design is, things are definitely going to get shaky. The purpose of this post is to explain some of the data quality features of SAS ALM on Viya and also give you some tips on how to set up strong data quality controls in your organisation.   Whether you're an advanced ALM user or just starting out, you will see how you can take advantage of these cool tools to keep your ALM analysis reliable.   The Data Quality Challenge   Based on my experience, banks spend about 80% of their ALM implementation time wrestling with data issues. Yet surprisingly, many organizations still treat data quality as an afterthought. It is like showing up to a Michelin-star restaurant with ingredients from a gas station, you can't expect miracles.   But here's the good news: SAS ALM on Viya comes with a powerfull data quality framework that's like having a team of obsessive-compulsive data inspectors working for you 24/7.   The Data Quality Dashboard: Your Command Center   Remember playing "Where's Waldo?" as a kid? Finding data issues without a proper dashboard is just as frustrating. SAS ALM's data quality dashboard gives you a birds-eye view of your portfolio's health. It's like having X-ray vision for your data!   Key features of the Visual Analytics DQ dashboard include:   Real-time data quality metrics Trend analysis Drill-down capabilities Automated alerts Multi dimensional slicing and dicing and much more   Out-of-Box Rules will get you started   The solution ships with an extended set of data quality rules that can catch common issues quite fast.   These rules cover:   Missing or invalid values Date consistency checks Balance validations Currency verifications Relationship rules   Creating Your Own Rules: Because One Size Doesn't Fit All   When the standard rules are not enough, you can go ahead and create your very own, because every bank has unique data quirks. That's why SAS ALM lets you create custom DQ rules from basic to extremely advanced.   Here's a simple example:   if balance < 0 and product_type = 'ASSET' then flag_error("Negative balance detected for asset product");   You can go as advanced as you like because the user interface allows you to be very creative and flexible, basically the sky is the limit, or the data in this case.   Here is an example of a more elaborate rule built in the UI:     Tips from the Field and Best Practices   I will share a story that shows these tips in action. A few years ago, I was working with a large European bank implementing SAS ALM. Their initial approach was to create hundreds of data quality rules right from the start. That didn't go well in the long run. Here's what we learned and how you can avoid the same pitfalls.   1. Start Small but Think Big   Think of building your data quality framework like cooking a complex meal. You don't start by juggling five pots on the stove while baking a souffle. Instead, you begin with the basics and build up.   Here's what worked for us: We started with just three critical rules for their mortgage portfolio:   Validate that interest rates fall within historical ranges Ensure remaining term is less than original term Check that payment frequency matches product specifications   Once these were working smoothly, we expanded to more complex rules and additional portfolios. The key was having a clear roadmap of where we wanted to go while taking manageable steps to get there.   2. Document Your Rules (Your Future Self Will Thank You)   I once had to debug a rule that someone had helpfully named "Important_Check_V7_Final_FINAL_Use_This_One".  Does it sounds familiar?   Here's a better way to document a rule:   /* Rule ID: DQ_MORT_IR_001 Purpose: Validates mortgage interest rates against historical ranges Created: 2024-01-15 by Sarah Updated: 2024-02-01 - Added exception for promotional rates   Logic: - Check if interest rate is between 0.1% and 15% - Flag promotional rates (<1%) for review - Exclude government-subsidized products   Dependencies: - Requires PRODUCT_TYPE column - Uses HISTORICAL_RATES reference table */   3. Test Thoroughly (Preferably Not in Production)   The bank discovered their data quality rules weren't catching negative prepayment rates... during a regulatory audit!   To avoid such excitement, we developed a testing approach I call the "Break it Before You Take it" method:   Create a small test portfolio with known issues Run your rules against it Verify all issues are caught Then deliberately introduce new errors to ensure they're caught as well   In SAS ALM, you can use the test environment to run these checks without affecting your production cycles. For example:   # Create test cases test_portfolio = { 'good_record': {'rate': 3.5, 'term': 360, 'remaining': 240}, 'bad_rate': {'rate': -1, 'term': 360, 'remaining': 240}, 'impossible_term': {'rate': 3.5, 'term': 360, 'remaining': 361}}   # Run through data quality rules results = run_dq_rules(test_portfolio)   # Verify results match expectations assert results['bad_rate'].has_error('NEGATIVE_RATE') assert results['impossible_term'].has_error('TERM_MISMATCH')   4. Monitor and Adjust   Data quality isn't a set it and forget it process. It's more like taking care of your garden, it needs regular attention. In SAS ALM, you can set up monitoring dashboards to track:   Number of issues detected over time Time to resolution Most frequent error types Impact on downstream processes And much more   The bank I worked with noticed a spike in data quality issues every quarter. After investigation, we discovered it coincided with their core banking system updates. This led to creating specific rules for post-update validation, preventing downstream issues in their ALM analysis.   Pro Tip: Set up automated notifications in SAS ALM to alert relevant teams when specific patterns emerge. For instance, if more than 5% of your portfolio fails validation, you might want to pause the cycle and investigate before proceeding further.   Conclusion is that Quality In = Confidence Out   Data quality doesn't have to be difficult. With SAS ALM on Viya, you have the tools to transform data quality from a necessary evil into a competitive advantage. Think of it as your portfolio immune system that is working silently to keep everything healthy.   Are you ready to take your ALM data quality to the next level? SAS ALM on Viya might be just what you need. And unlike my attempts at home baking sourdough bread, this actually works!     Find more articles from SAS Global Enablement and Learning here. ... View more

Once upon a time, there were two little banks.   Same city, same regulations, same pool of clients but completely different stories. One was thriving, the other,... well,.... their risk manager's LinkedIn status was changed to "Open for Work."   What made the difference?  Simply how they managed their balance sheets.   The purpose of this post is to tell a story of modern Asset and Liability Management (ALM) through the tales of two imaginary banks: SafetyFirst National and YOLO Financial. We'll explore how different approaches to ALM can lead to drastically different outcomes, and peek into what the future holds for this critical banking function.     A Tale of Two Strategies   Meet SafetyFirst National, a mid-sized bank that approaches risk management like a chess grandmaster, always thinking several moves ahead.   Then there's YOLO Financial, whose strategy could best be described as,... well,.... optimistic. Both banks faced the same market challenges in 2023, but their paths diverged dramatically.     Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.     When traditional ALM mets modern reality   YOLO Financial's traditional approach seemed fine during stable times. Every month, they'd run their reports, check their interest rate gaps, and celebrate with coffee and donuts. But then March 2023 arrived, bringing the fastest rate hikes in recent history.   Pop Quiz: What happens when you have long-term fixed-rate assets funded by short-term deposits in a rising rate environment?   YOLO Financial learned the hard way - when your funding costs rise immediately while your asset yields stay fixed, your margins get squeezed fast. Their once-reliable monthly reports suddenly felt like reading yesterday's weather forecast to plan today's picnic. The market was moving too fast, customer behaviors were shifting dramatically, and their traditional ALM toolkit simply couldn't keep up with the pace of change.     The SafetyFirst difference   Meanwhile, SafetyFirst National had implemented something different. Not just newer technology, but a fundamentally different approach to understanding their balance sheet. They developed sophisticated behavioral models that could predict:   How different customer segments respond to rate changes Which deposits are truly "sticky" versus rate-sensitive When and how customers might prepay loans How digital banking patterns influence deposit behavior   The secret sauce? Advanced analytics that turned mountains of customer data into actionable insights. Not through magical "AI" promises, but through careful analysis of actual customer behavior patterns.     The Evolution of ALM   Think of it as the difference between weather forecasting in 1950 versus today. Back then, you looked at the sky and made an educated guess of what piece of clothing to wear. Today's meteorologists use sophisticated models processing millions of data points to give you the best in class weather forecasts so you are rarely in for a surprise. Banking risk management is undergoing the same transformation, hopefully.       From Insight to Action   Here's where the plot thickens. After their close call, YOLO Financial decided to upgrade their capabilities. Their approach? Buy every new tool and system they could find. The result? A dozen different systems that don't talk to each other, kind of like having a dozen weather apps on your phone, one for cloud coverage, one for wind and two more for precipitation and sunshine.   SafetyFirst National, however, focused on integration. Their risk managers insisted on building capabilities that could connect:   Customer behavior patterns Market risk assessments Liquidity monitoring Capital planning Regulatory requirements     The Future is Knocking   While no single solution today offers everything banks need for truly integrated balance sheet management, the industry is clearly moving in that direction. Leading technology providers are investing heavily in research and development, pointing toward a future where:   Risk analytics run in real-time, not monthly Behavioral patterns are automatically identified and incorporated into forecasts Scenario analysis considers multiple risk types simultaneously Decision-making is supported by comprehensive, connected data     The Million Dollar Question   Here's what I think keeps bank executives up at night: "In a world where interest rates change as unpredictably as the weather during climate change, how do we keep our banks both profitable and safe?"   The answer isn't adding more sophisticated tools to your arsenal. Instead, it's about moving beyond traditional ALM toward an integrated approach to balance sheet management.   It is a bit like an orchestra, having the best and most beautiful instruments won't create beautiful music unless they're all playing in harmony by people who know what they are doing. The same applies to your risk management tools, they need to work together seamlessly to create a complete picture of your bank's risk position.     Conclusion   As we wrap up our tale of two banks, the moral of the story is clear: The future belongs to institutions that can adapt, integrate, and analyze.   YOLO Financial survived their wake-up call and is slowly rebuilding their capabilities, even do they are paying the price of acting late.   SafetyFirst National continues to thrive, always looking ahead and trying to anticipate the next challenge.   The banking industry stands at a crossroad where one path leads to increasingly fragmented risk management, with more tools but less clarity, and the other leads to integrated, forward-looking balance sheet management that can turn market challenges into opportunities.   Before I close, I just want to leave you with this question:   Which path do you think your bank will choose?   Did this story change how you think about bank risk management? Share your thoughts in the comments below!     Find more articles from SAS Global Enablement and Learning here. ... View more

Asset and Liability Management is an important practice for financial institutions, which enables them to manage the risks associated with mismatches between assets and liabilities. With the advanced capabilities of SAS ALM on Viya, organizations can implement both static and dynamic approaches, each offering distinct advantages and considerations.   The purpose of this post is to explore the key differences between Static and Dynamic Asset and Liability Management (ALM) within the context of SAS Viya.   Understanding these key differences is mandatory for financial professionals looking to optimize their risk management strategies.   What is Asset and Liability Management (ALM)?   Asset and Liability Management (ALM) is the process of managing financial risks that arise due to mismatches between the assets and liabilities of an institution. It is an important practice for banks, insurance companies, and other financial entities to ensure long-term profitability and solvency. ALM strategies typically involve monitoring interest rate risk, liquidity risk, and other financial risks, and taking actions to mitigate these risks over time.   Static ALM: A Snapshot in Time   Static ALM refers to a risk management approach where the analysis is based on a fixed set of assumptions at a particular point in time. It is often seen as a more traditional method, focusing on the current balance sheet without considering future changes in the economic environment or the institution’s financial position.   Key Characteristics of Static ALM: Fixed Time Horizon: The analysis is conducted based on the assumption that the current balance sheet will remain static over a specified period. Assumptions: Interest rates, cash flows, and other variables are assumed to remain constant during the analysis period. Simplicity: The approach is generally easier to implement and understand, making it suitable for institutions with less complex financial structures or those seeking a quick snapshot of their risk exposure. Limitations: Since it does not account for future changes, static ALM may not provide a comprehensive view of long-term risks. This can lead to suboptimal decision-making in a rapidly changing market environment.   Dynamic ALM: Adapting to Change   Dynamic ALM, on the other hand, is a more sophisticated and flexible approach that considers the potential changes in the economic environment, interest rates, and other factors over time. This method involves continuous monitoring and adjusting the ALM strategy based on new information and evolving conditions.   Key Characteristics of Dynamic ALM: Variable Time Horizon: Dynamic ALM operates over a longer time horizon and continuously updates assumptions as new data becomes available. Scenario Analysis: It often includes scenario analysis and stress testing, allowing institutions to evaluate the impact of various hypothetical scenarios on their balance sheet. Flexibility: The approach is adaptable, making it suitable for institutions operating in volatile markets or those with complex financial structures. Advanced Risk Management: By considering future changes and incorporating various risk factors, dynamic ALM provides a more holistic view of risk, enabling better-informed decision-making.   Static vs. Dynamic ALM in SAS Viya   SAS ALM on Viya offers tools for both static and dynamic ALM, allowing institutions to choose the approach that best fits their needs. Here’s how these approaches can be implemented within SAS Viya:   Static ALM: SAS Viya can model a static ALM approach by using historical data and fixed assumptions to analyze the current balance sheet. Users can generate reports that provide a snapshot of the institution's risk exposure based on the existing financial position.   Dynamic ALM: For dynamic ALM, SAS Viya’s advanced analytics capabilities come into play. The platform supports scenario analysis, stress testing, and continuous monitoring of the balance sheet. Institutions can simulate various future economic conditions and adjust their strategies in real-time, ensuring they remain aligned with their risk management objectives.   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   Choosing the Right Approach   The choice between static and dynamic ALM depends on several factors, including the complexity of the institution’s balance sheet, the volatility of the markets in which it operates, and the institution’s overall risk management goals. In practice, many institutions use a combination of both approaches, leveraging the strengths of each to build a comprehensive ALM strategy. Here are just 2 examples of how you can decide what is best for your organization:   Small Local Bank with Simple Balance Sheet   A small, community bank with relatively simple operations and a straightforward balance sheet might opt for Static ALM. The bank’s primary assets may consist of long-term, fixed-rate mortgages, while its liabilities are mostly customer deposits with predictable withdrawal patterns. Reason: Since the bank operates in a stable local market with limited exposure to interest rate fluctuations or volatile assets, it might not need to continuously adjust its strategy. Using a static ALM approach, the bank can conduct regular, periodic assessments to ensure that its assets and liabilities are aligned, focusing more on immediate conditions rather than future changes. Benefit: Static ALM here provides simplicity, lower costs, and ease of implementation, as there’s little need for continuous monitoring or complex scenario analysis.   Large National Bank with Complex Investments   A large national or international bank with a more diverse portfolio, including variable-rate loans, derivatives, and international investments, would benefit from using Dynamic ALM. Reason: This bank operates in multiple markets and faces a higher degree of uncertainty due to fluctuating interest rates, foreign exchange risks, and varying credit qualities. By using a dynamic ALM approach, the bank can model different scenarios (e.g., interest rate increases, changes in inflation, or economic shocks) and adjust its asset and liability positions in real time. Benefit: Dynamic ALM allows the bank to be more flexible and responsive to changing market conditions, helping to mitigate risks and optimize profitability in a dynamic and constantly changing financial environment.   Conclusion   Static and Dynamic ALM represent two different approaches to managing financial risk, each with its own advantages and limitations. SAS Viya provides the tools necessary to implement both strategies, enabling institutions to tailor their ALM practices to their specific needs. Whether you need a fast snapshot of your risk exposure or an adaptive strategy that's forward-looking, knowing the differences between static and dynamic ALM becomes quite important for proper risk management.   For further learning, consider exploring the SAS Viya documentation on ALM, which offers in-depth guidance on implementing all these strategies.   Find more articles from SAS Global Enablement and Learning here. ... View more

In the complex world of Asset and Liability Management (ALM), certain concepts can seem as puzzling as organizing a lifetime's worth of clothing. Today, I am going to simplify three key elements of ALM using an analogy everyone can relate to: managing your own wardrobe.   The purpose of this post is to give you a simple and clear understanding of these key elements; namely the Business Evolution Plans, Allocation Schemes and Synthetic Instruments, even if you have never set foot in a bank's strategy department.     Your Wardrobe of the Future (The Business Evolution Plan)   Imagine your entire wardrobe as a bank's portfolio of financial products. A Business Evolution Plan (BEP) is like planning how your wardrobe will evolve over the coming seasons. It's not just about what you have now, but what you'll need in the future.   Growth projections: Just as you might plan to add more business casual attire for a career change, a bank might project growth in certain product areas. For instance, if the housing market is booming, they might plan to offer more mortgage loans. This is like you anticipating needing more suits for a new office job. Reduction plans: Similar to deciding to dial down your collection of formal wear because you attend fewer galas, a bank might plan to decrease its offerings in certain product lines or markets. Perhaps they're reducing their focus on personal loans in favor of business loans. New additions: Like introducing a new style or trend to your wardrobe (think adding sports wear for a more active lifestyle), banks use BEPs to plan the introduction of new financial products. This could be a new type of savings account or an innovative investment product. Time horizons: BEPs typically cover multiple future time periods. It's like planning your wardrobe for the next month (you need a new raincoat for spring), the upcoming season (summer vacation means more beachwear), and the following year (you're attending a wedding next winter). Banks similarly plan for short-term, medium-term, and long-term changes in their product offerings. Balance and strategy: Just as you'd want a balanced wardrobe for various occasions - work, leisure, formal events - BEPs help banks maintain a strategic balance in their product portfolio. This ensures they're prepared for different economic scenarios, just as your varied wardrobe prepares you for different social situations.   Your Personal Stylist (The Allocation Scheme)   If the BEP is the plan for your future wardrobe, the Allocation Scheme is like having a personal stylist who decides exactly what pieces to buy and in what quantities. This stylist doesn't just say "buy more casual wear," but specifies "add three pairs of khakis, two blue button-shirts, and one pair of brown loafers."   Detailed distribution: Your stylist (the Allocation Scheme) determines how many of each type of clothing item you should add or remove. In banking, this translates to deciding not just to increase mortgage loans, but specifying how many of each type (fixed-rate, adjustable-rate, 15-year, 30-year, etc.) should be added to the portfolio. Prioritization: Like deciding whether to invest more in quality basics or trendy pieces, the Allocation Scheme helps banks prioritize growth in specific areas. Maybe you need more versatile pieces that can transition from day to night, just as a bank might prioritize products that serve multiple customer segments. Risk management: A good stylist ensures your wardrobe remains versatile for various occasions and unexpected events. Similarly, the Allocation Scheme helps banks manage risk by ensuring they're not overly reliant on any one product type, just as you wouldn't want a wardrobe full of only formal wear.   Your Custom Tailor (Synthetic Instruments)   Synthetic Instruments are like having a skilled tailor who can create unique, custom clothing by combining elements from existing pieces. This is not just about small alterations; it's about creating entirely new items that serve very specific purposes.   Custom creation: Banks can create new financial products by combining features of existing ones. Imagine a tailor creating a jacket with the comfort of a sweater, the water-resistance of a rain jacket, and the professional look of a blazer. In banking, this might be a savings account with the high interest of a Certificate of Deposit, the flexibility of a checking account, and the investment options of a money market account. Risk management tools: These custom pieces often help banks better manage their risk exposure. It's like creating adaptable clothing for unpredictable weather - perhaps a jacket with removable layers for unexpected temperature changes. In banking, a synthetic instrument might combine aspects of different products to hedge against interest rate fluctuations or credit risks. Competitive advantage: Just as custom-made clothing fits perfectly and stands out, Synthetic Instruments allow banks to offer unique products that may not be available elsewhere. This could be a mortgage product that automatically adjusts its terms based on the borrower's changing financial situation, providing a unique selling point in a competitive market.   Putting It All Together:   The Business Evolution Plan outlines the bank's strategic vision for its future product portfolio. It's like your overall plan for an ideal wardrobe - "In five years, I want a versatile, professional wardrobe with a touch of creative flair." The Allocation Scheme provides the detailed roadmap for achieving this vision, specifying exactly what to buy or remove. It's the shopping list from your personal stylist - "Buy these specific items in these quantities to achieve your wardrobe goals." The Synthetic Instruments offer flexibility, allowing banks to create custom products that fit precisely into their strategic plan. This is your tailor creating unique pieces to complete your wardrobe - pieces that serve specific needs and can't be found off the shelf.   These tools, working together, allow banks to make informed decisions about their future, manage risks effectively, and stay competitive in an ever-changing financial landscape. It's like having a wardrobe strategy that ensures you're always appropriately dressed, no matter the occasion, season or how fashion trends change.   Conclusion   While these banking concepts may initially seem as daunting as organizing a chaotic closet, they're fundamentally about strategic planning and smart resource allocation - ideas we can all relate to in our daily lives. By viewing these complex financial strategies through the lens of wardrobe management, I hope you can better appreciate the thoughtful planning that goes into maintaining a healthy, balanced, and forward-looking banking system.   For those interested in exploring how these concepts are implemented in SAS ALM on Viya, the official documentation is a good start and provides a wealth of detailed information.     Find more articles from SAS Global Enablement and Learning here. ... View more

The purpose of this post is to guide you through the process of loading and ingesting data in Asset Liability Management (ALM) on SAS Viya. Whether you’re new to SAS Viya or looking to improve your data ingestion workflow, this guide will provide you with practical tips and best practices to simplify your data management process. By the end of this post, you’ll have a clear understanding of how to efficiently handle your data, making it readily available for your financial analysis and decision-making.   1. Understanding the Data Requirements Before ingesting data, it's important to identify the types of data required for ALM processes. These can be in the form of:   Financial Instruments Data: Information about fixed and floating rate instruments, derivatives, loans, deposits, etc. Market Data: Yield curves, interest rates, exchange rates, economic indicators. Transaction Data: Detailed records of financial transactions. Credit Risk Data: Credit ratings, default probabilities, exposure data.   2. Preparing your Data Sources Ensuring that your data sources are prepared and accessible is an important step in the data ingestion process. This involves understanding the various sources your data can come from and the best practices for preparing these sources for integration with SAS Viya. Some of the most common data sources are:   Databases: Relational Databases: These include Oracle, SQL Server, PostgreSQL, MySQL, and more. They are commonly used to store structured data. NoSQL Databases: Databases like MongoDB, Cassandra, and Redis are used for unstructured or semi-structured data. They are highly scalable and can handle large volumes of data. Flat Files: CSV (Comma-Separated Values): used for data exchange due to its simplicity and compatibility with various tools. Excel Files: Common in business environments for data analysis and reporting. JSON (JavaScript Object Notation): Often used for data interchange between web applications and servers due to its lightweight and human-readable format. APIs (Application Programming Interfaces): APIs allow access to data from various platforms, including financial data providers, market data feeds, and internal enterprise systems. They are used in real-time data integration and dynamic data retrieval. Data Lakes: Data lakes are centralized repositories that allow you to store all your structured and unstructured data at any scale. Technologies like Hadoop and cloud-based data lakes (e.g., AWS S3, Azure Data Lake) are popular choices.   3. Best Practices for preparing Data Sources Data Quality Assurance: Validation: Ensure that the data meets the required standards and formats before ingestion. This includes checking for missing values, outliers, and other inconsistencies. Cleansing: Clean the data to remove any errors or irrelevant information. This may involve filling missing values, correcting data types, applying rules and removing duplicates. Data Transformation: Normalization: Standardize the data to a common format or structure. This helps in maintaining consistency and simplifies the analysis. Aggregation: Summarize the data at required levels to meet the needs of ALM models. For instance, aggregate transaction data by date, instrument type, or geographical region. Data Security: Encryption: Ensure that sensitive data is encrypted both at rest and in transit. This protects the data from unauthorized access and breaches. Access Control: Implement role-based access controls to restrict data access to authorized personnel only. Documentation: Metadata: Maintain comprehensive metadata for all data sources. This includes information about the data origin, structure, and any transformations applied. Data Lineage: Track the data flow from the source to the final destination. This helps in understanding the data transformations and ensures transparency.   4. Using SAS Data Connectors SAS Viya provides various data connectors that facilitate the integration and ingestion of data from multiple sources. These connectors are essential for importing data into the SAS environment, ensuring compatibility, and enhancing the efficiency of your data workflows. Here is an in-depth look at some of the most commonly used data connectors and how they can be utilized in the context of ALM.   Types of SAS Data Connectors SAS/ACCESS: SAS/ACCESS engines provide seamless read, write, and update access to a variety of relational and non-relational databases. This allows you to interact with your data directly from within the SAS environment. Here are some of the commands you can use to connect to different databases: Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   SAS Data Connectors: These connectors are designed to work with big data platforms and cloud storage solutions, enabling high-performance data access and manipulation. Hadoop: Hadoop is widely used for storing large datasets in a distributed environment. SAS Viya’s Hadoop connector allows you to efficiently read and write data to and from Hadoop. SAP HANA: SAP HANA is an in-memory database and application development platform. The SAS connector for SAP HANA provides high-speed data access. Cloud Storage such as Amazon S3 or Azure Data Lake.   Setting Up and Using SAS Data Connectors Install and Configure Connectors: Ensure that the necessary SAS/ACCESS engines and data connector modules are installed and configured in your SAS Viya environment. Configuration details can often be found in the SAS documentation or provided by your database administrator. Establish Connections: Use the appropriate libname or filename statements to establish connections to your data sources. Each connector may require specific connection parameters such as server addresses, user credentials, and database names. Data Access and Manipulation: Once the connection is established, you can use standard SAS procedures to access and manipulate the data. This includes procedures like PROC SQL, DATA STEP, and others for data transformation, analysis, and reporting.   Example of connection strings to different databases:   Hadoop: libname myhadoop hadoop server='hadoopserver' schema='hadoopschema';   SAP HANA:ibname saphana hana user='username' password='password' server='saphanaserver' schema='saphanaschema';   Amazon S3: filename s3file s3 's3://mybucket/mydata.csv' access_key_id='your_access_key' secret_access_key='your_secret_key';   Azure: libname adls azure storage_account='your_storage_account' storage_key='your_storage_key';     Example Workflow with SAS Data Connectors Here we have an example of using a SAS data connector to integrate data from an Oracle database:   Step 1: Establish Connection libname oradb oracle user='username' password='password' path='oracleserver';   Step 2: Load Data into SAS Viya proc sql; create table work.financial_data as select * from oradb.financial_instruments; quit;   Step 3: Data Transformation and Cleansing data work.cleaned_data; set work.financial_data; /* Example transformations */ if interest_rate = . then interest_rate = 0; effective_date = input(date, yymmdd10.); format effective_date date9.; run;   Step 4: Store Data in Viya CAS proc casutil; load data=work.cleaned_data casout="financial_data" replace; quit;   Example Workflow with Excel proc import datafile="/mnt/data/financial_instruments.xlsx" out=work.financial_data dbms=xlsx replace; sheet="Sheet1"; run;   data work.cleaned_data; set work.financial_data; /* Transformations as needed */ run;   proc casutil; load data=work.cleaned_data casout="financial_data" replace; quit;   5. Conclusion Ingesting data into SAS Viya for ALM involves connecting to data sources, loading and transforming the data, and then storing it in CAS for in memory analytics. This process ensures that your financial and risk management models, business evolution plans, allocation and segmentation schemes, all have accurate and up-to-date data for effective calculations, analysis and reporting.   For more information, please check out the official Asset and Liability Management Documentation here     Find more articles from SAS Global Enablement and Learning here. ... View more

  If you’re looking to deepen your understanding of risk management tools available in SAS Viya, you’re in the right place. The purpose of this blog post is to explore the advanced configuration options available for setting up shocks in SAS Risk Factor Manager. We’ll look into some of the more sophisticated features that can help you refine how shocks are applied within your models, ensuring that they accurately reflect complex financial scenarios.   Why Advanced Configuration Matters   Setting up shocks properly is important because it affects the precision and effectiveness of your risk assessments. Advanced configurations can help simulate realistic economic impacts by allowing for detailed adjustments based on specific conditions and parameters.   First let’s begin by accessing the Configuration Screen by navigating to the SAS Risk Factor Manager on your SAS Viya platform and then open the Shocks tab where you can create, view, and modify shocks.   Creating or Editing a Shock  To create a new shock, click on the ‘New Shock’ button and fill in the basic information such as name and description.  To edit an existing shock, double click it from the list to access its detailed configuration settings. Now you are presented with the advanced configuration screen for your shock, which should look something like this:   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   Let's now explore the settings on this screen one by one.   Caps and Floors   Caps and Floors are used to set upper and lower limits on the shock values to prevent unrealistic scenario outcomes.   Cap = The maximum value a shock can reach.   Floor = The minimum value a shock can reach   Example: Ensuring that an interest rate increase shock doesn’t accidentally push rates above a certain threshold of 5% but also not going below zero.   Fade-In and Fade-Out    Fade-In and Fade-Out settings are used for applying shocks gradually over time:   Fade-In: Allows the shock to increase gradually from an initial value to the specified maximum over a selected number of time periods.   Example: To model a gradual market downturn, set a fade-in to increase a shock from 0% to -5% over 3 months.   Fade-Out: Lets the shock decrease back to normal or a new state over time.   Example: After reaching -5%, a fade-out could gradually reduce the shock to -2% over the next 3 months.   Configuring Perturbations   Perturbations define how shocks affect different variables: Choose the variable you want to apply the shock to. Specify the value of the shock and, if applicable, set up the cap and floor values as described above. Set the type of shock as seen below.   Setting Horizons   By navigating to the 3 dot ellipsis menu (⋮) you can setup multiple horizons and types for your shock as seen below.     Now that we have put all these together, let's see a complete practical example of this advanced configuration:   Configuration Parameter Value Horizon Shock Type Relative - Fade in 0-5% 3 months Hold at -5% 2 months Fade out -2% 3 months Cap 5% - Floor -6% - Applies to Interest rate -     Conclusion   As you can see, advanced shock configuration in SAS Risk Factor Manager allows for precise fine-tuning and realistic modeling of various economic scenarios. By understanding these settings, you can enhance your risk management practice and provide more accurate forecasts.   Do you have questions or tips on configuring shocks? Share them in the comments below. Let’s learn from each other and refine our risk management strategies together!   To learn more about SAS Risk Factor Manager please visit the official documentation here. ... View more

SAS Risk Factor Manager on Viya is a powerful module that comes installed with your SAS Risk Solution. It is designed to help companies manage, simulate, and analyze risk factors across different scenarios and shocks. By facilitating the import and export of these scenarios, SAS Risk Factor Manager streamlines the process of risk management, allowing for a more robust and accurate financial risk assessment. The purpose of this post is to guide you through this import/export process at a high level, ensuring you can efficiently manage risk factors within your SAS Risk Solution.   Understanding Scenarios and Shocks   Before we dive into the technicalities, let's clarify what we mean by scenarios and shocks. In the context of financial risk management, a scenario represents a set of conditions that might affect the financial market, such as changes in interest rates or stock prices. A shock, on the other hand, is a sudden, unexpected event that can have a significant impact on the market, like a financial crisis. Managing these elements effectively is crucial for predicting and mitigating financial risks.   Importing Scenarios and Shocks   Importing scenarios and shocks into SAS Risk Factor Manager is a simple general import process. You can define scenarios, shocks, and sets in an external file (csv, xls, sasb7dat), and then import them via the user interface. SAS Risk Factor Manager supports various data formats, including expanded format, date-based format, and set format, catering to different needs and preferences. Let's highlight how the import process works:   Specify the source location of your scenario/shock file (this can be from a server or your local machine) Select the file to import (for server imports, select from in-memory tables from caslibs you have access to. For local files, ensure the data file follows the format guidelines outlined in the SAS documentation.) Select import options (Specify additional details such as the as-of date, version, and how you wish to import the objects, as a standalone or part of a set)   Exporting Scenarios and Shocks   Exporting allows you to work with scenarios or shocks outside of SAS Risk Factor Manager. You can export to various formats, including .csv and .xlsx, though it's important to note that the export process does not support sets. Let's now highlight how the export process works:   Specify the target location (decide between exporting to a server or downloading to a local machine) Select scenarios or shocks to export (choose all items you wish to export from the SAS Risk Factor Manager interface) Select export options (for server exports, select the CAS server and library, for local files, choose the file format and name)   Here are the file formats available in SAS Risk Factor Manager   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   To complete the full picture, here is a matrix showing which file types and object combinations are supported during the import/export process     Conclusion   Importing and exporting scenarios and shocks in SAS Risk Factor Manager on Viya are essential skills to have for effective risk management. By following the steps outlined above, you can ensure that your risk factors and shocks are accurately tested and reflected in your risk models, allowing for better decision-making and risk assessment.   For a more detailed explanation and specifics of each file format, be sure to check the official SAS documentation at https://support.sas.com/en/documentation.html.     Find more articles from SAS Global Enablement and Learning here. ... View more