The financial services industry is undergoing a period of profound change, driven by a dynamic economic landscape, increased regulatory scrutiny, changing consumer behavior and rapid technological advances.
Banks operating in this environment are under increasing pressure to transform their risk modeling and decision-making ecosystems in order to remain competitive. This blog post examines the key factors shaping this transformation, the role of automation and AI and strategies banks can use to modernize their risk management practices.
Why banks must rethink risk modeling
Several important factors are forcing banks to re-evaluate and modernize their risk modeling and decision-making processes. The global economy is rife with volatility, influenced by the rising cost of living, inflation, interest rate uncertainty and potential unemployment affecting household cash flows worldwide. This pervasive turbulence requires financial institutions to adapt quickly and recalibrate risk models more frequently.
At the same time, new and complex regulations are increasing compliance costs and demanding more sophisticated risk management practices, which are increasingly driven by regulatory requirements. Another driver is the generational change and growth of fintech companies, which are reshaping customer expectations and increasing the demand for better connectivity, automation and faster service delivery. Rapid advances in generative AI (genAI), AI, machine learning and analytics and cloud computing also are opening up new opportunities to improve risk modeling and decision making.
The growing role of AI and automation
To meet these challenges, financial institutions are investing more in technology and automation, with a focus on modernizing their risk modeling capabilities using AI and machine learning. They are diversifying their data sources, developing nontraditional models and training existing models with real-time and transactional data.
At the same time, banks are focusing on developing adaptable and flexible risk models that can respond quickly to new regulations and compliance requirements. The use of forward-looking scenario analysis for stress testing and long-term planning for operational resilience is becoming increasingly important. Risk modeling also is being refined at the level of individual market segments to improve accuracy, reliability and explainability.
Automation plays a critical role in these shifts, offering productivity benefits, operational efficiencies and the ability to redeploy human capital to higher-value tasks. While automation has long been used in the front office to optimize the customer journey, its value in the back office – particularly for regulatory compliance – is now gaining greater recognition.
Maintaining governance and compliance at scale
Artificial intelligence is emerging as a key driver of this automation. Financial institutions are focusing on integrating AI into risk management processes and automating various elements of the model lifecycle, such as data exploration, preparation, validation and documentation. Cutting-edge technologies also are automating data collection and pre-processing, speeding up the preparation phase. Real-time data integration with machine learning models enables continuous updates, which helps reduce model validation and deployment times and ensures model reliability.
While AI and automation offer significant benefits, they also pose governance challenges. It is essential to ensure interpretability, fairness and compliance with regulations, such as the AI Act, through robust governance frameworks. Automation of model lifecycle steps, including data preparation, model development, backtesting and lineage, is essential for effective governance. Collaborative governance and oversight of each step are also becoming increasingly important.
Automation strategies across banking sectors
Automation is being adopted across all banking sectors, though priorities and adoption rates vary. In retail banking, opportunities for standardization, the availability of large data sets and economies of scale make retail banking a natural fit for automation. Automation is easier to implementation is often easier in this sector because data quality tends to be higher and individual models consistently deliver better results across the board.
Corporate and investment banking, on the other hand, involves complex materials such as financial reports and legal documents. These must be converted into structured data for automated processing – a situation in which technologies like large language models (LLMs) play a crucial role.
Balancing compliance requirements with business decisions is a significant challenge for banks. Financial institutions must meet strict regulatory expectations for model development and deployment, while aligning their strategic objectives not only with compliance, but also with business growth. Pre-built accelerators and solutions that provide full traceability, auditability and comprehensive documentation can help reduce the compliance burden. At the same time, improving core capabilities and modernizing foundational systems enables banks to focus more effectively on innovatiion and long-term strategies.
Laying the groundwork for long-term success
A recent risk management report from SAS and FT Longitude highlights three critical drivers for retail banking automation: Total cost of ownership, software rationalization and strategic investment. Reducing operating costs and increasing efficiency remain central motivations for investing in automation technologies. Increasingly, automation is seen as a strategic imperative for firms looking to remain competitive, optimize operations and enhance customer service.
In this context, it is critical to align technology investments with long-term strategic goals and ensure that risk management systems support and promote this vision. Developing a flexible system to support higher-value use cases also requires greater integration and interoperability across the risk management ecosystem.
To ensure that risk models are efficient, accurate and future-proof, banks and financial institutions should increasingly rely on automation for monitoring and backtesting. A strong foundation that supports both automation and scalability is essential. Banks must also ensure that their systems can interact seamlessly with multiple applications, while maintaining robust governance and versioning processes.
Looking beyond current requirements, banks and financial institutions should plan for scalable management of a growing number of decision strategies and address inefficiencies in data management, validation and governance implementation. Risk managers should explore advanced machine learning and AI modeling techniques to build more resilient models, while Chief Risk Officers may need to advocate for increased investment in these critical areas.
Transformation as strategic imperative
Modernizing risk modeling and decision making is no longer simply an option for banks, but a necessity for success in a complex and competitive environment. With the help of automation, AI and strategic technology investments, financial institutions can build resilient, agile and future-ready risk management systems. This transformation will empower firms to make smarter, faster decisions, enhance the customer experience and achieve sustainable long-term growth.
