Contact: Darço Akkaranfil
Country: Türkiye
Award Category: Innovative Problem Solver
Tell us about the business problem you were trying to solve.
Burgan Bank was undergoing a digital transformation to shift towards retail banking, and faced a pressing need to modernize legacy systems and processes within just nine months. The bank’s primary challenges were:
● Enhancing customer engagement in a highly competitive market
● Mitigating risk related to credit allocation, collections, and fraud prevention
● Managing multiple parallel projects with governance and prioritization challenges. These issues were slowing the bank’s ability to scale and respond to evolving market demands.
What SAS products did you use and how did you use them?
Burgan Bank used several SAS solutions to address these challenges:
● SAS Intelligent Decisioning is the key product in enabling decision flows and rule-based approaches for all projects.
● SAS Visual Analytics was employed for data visualization for all projects, enabling better analysis, reporting and decision-making.
● SAS Visual Investigator and SAS Intelligent Decisioning were leveraged to enhance application fraud detection
● For the Next Best Action project, by using SAS Model Studio, Machine Learning models were developed to enhance customer engagement and provide personalized offers.
● SAS Model Manager for deployment, monitoring, and updating of models, helping to streamline decision-making processes and optimize real-time decision-making within the bank.
What were the results or outcomes?
The implementation of SAS solutions led to:
● Consolidation and standardization of the bank's analytical data platform, improving efficiency and reducing reliance on legacy systems like SPSS and Experian.
● Scalability improvements in key areas such as collections, credit allocation, and fraud prevention, enabling the bank to meet growth targets.
● A more advanced, yet seamless, solution for application fraud detection, enhancing user experience while strengthening security.
● Creation of an analytical CRM environment and the development of a Next Best Action system, supporting aggressive new customer acquisition goals by offering real-time personalized offers to customers.
● Overall, the solutions enabled faster, more informed decision-making, allowing the bank to be more competitive in the market.
Why is this approach innovative?
The approach is innovative because it enhances the bank's existing AI capabilities by integrating machine learning models into a unified architecture with SAS Viya, enabling real-time, intelligent decision-making that was not possible with previous legacy systems. The Next Best Action system utilizes machine learning to offer personalized customer experiences, improving engagement and acquisition rates. The bank is planning to migrate existing models from SPSS to SAS Viya, which will further modernize decision-making processes, improve scalability, and eliminate current limitations. Additionally, the hands-on training and knowledge transfer from the local partner SADE empowered the business teams to independently manage and optimize decision flows, ensuring long-term sustainability and faster ROI from SAS solutions.
