From raw data to risk reports: A journey through the SAS Asset and Liability Management workflow

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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.

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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.