Banking risk management: What does history teach us?

Where were you when the global banking crisis of 2008 hit? I was in banking – marketing, to be specific – and I supported our mortgage department. I vividly recall our bank’s chief risk officer repeatedly refusing to enter the adjustable-rate mortgage subprime lending business. 

Many banks were eating our lunch and our mortgage lenders were upset that we weren’t participating in this lucrative market. But as our CRO stated, it wasn’t in the best interest of our clients or the bank’s risk profile. I don’t need to tell you how it unfolded for the industry and the world.

Thanks to our CRO’s prudent decision, our bank was one of only three U.S. banks to remain profitable through the Great Recession.

It has been a couple of years since the 2023 banking crisis, which saw three of the four largest U.S. bank failures in history and a forced tie-up between Switzerland’s megabanks. However, the largest bank failure occurred in 2008. History tends to repeat itself, but what lessons can we learn from the past as we look to the future of risk management?

Our new report, Transforming Risk Management, summarizes key findings from a recent global survey of 300 banks. Produced in collaboration with FT Longitude, this report reveals the top challenges facing risk management in banking and how banks can accelerate the pace of change with strategic investments in key areas.

As a follow-up to our first survey on this topic in 2021, this new report repeats many key questions, making it easy to see what’s changed in just a few short years. I’ll hit the highlights here, but I encourage you to read the full report.

Top factors affecting risk modeling

The factors shaping banks’ approach to risk modeling have expanded. The top factor, a volatile macroeconomic environment, heavily impacts credit risk modeling and decisioning. This volatility requires urgent model updates and more granular models to stay competitive. Yet current lead times – often months – remain too long.

Regulation is the second most important variable, flagged by 59% of banks surveyed, up from 37% in 2021. Banks must contend with evolving financial services regulations such as the ever-changing Basel Endgame and the EU AI Act. The surge in new rules has led 56% of the banks we surveyed to declare that regulatory requirements rather than business goals drive risk management – up from 46% in 2021.

Risk modeling modernization

Development, validation and deployment are the phases of the model management lifecycle most in need of transformation. All three topped the list in 2021 and grew even more important in this year’s report.

To improve model quality and speed up development and deployment, 67% of the banks we surveyed said they plan to upgrade their risk modeling capabilities in the next two years, up from 54% in 2021. AI and machine learning are the top investment priorities, followed by data analytics and cloud.

Talent remains a challenge, with 67% of banks surveyed saying they struggle to recruit staff with the right skills – up a whopping 20 points from 2021. Easier solutions like low-code, no-code and automation exist, and interest in generative AI for model development is growing.

Integrating balance sheet across the bank

Poor balance sheet management was a key factor in the 2023 bank failures. The resurgence of interest rate risk in recent years has exposed weaknesses in integrating asset and liability management (ALM), liquidity and credit risk systems. Siloed systems hinder the assessment of interdependencies and balance sheet impact.

Many banks claim their ALM function is integrated with key finance processes, but few have fully integrated systems and automated processes spanning ALM, liquidity and credit risk. This hinders sophisticated scenario analysis and stress testing.

Investment is underway, with 68% of banks surveyed without cloud deployment planning immediate investments in ALM and liquidity risk capabilities. Of these, 41% say they will enhance existing systems, while 38% plan to deploy new, next-generation systems designed to support integrated balance sheet management.

AI investment is only beginning

Banks are increasingly optimistic about AI’s potential in risk management. AI investment for risk modeling is a priority for 62% of banks surveyed, up from 44% in 2021. Over the past three years, banks’ comfort level with and confidence in AI and the need to automate regulatory compliance and streamline implementation have continued to grow.

Adoption remains slow, with only 40% of banks surveyed widely using AI for risk management and just 17% using GenAI. While both enhance risk modeling, they also create new challenges, such as explainability and model proliferation. A lack of skills, cited by 50% of banks surveyed, remains a major hurdle.

Data consolidation challenges

Banks face significant data challenges due to fragmentation, silos and sheer volume. This hampers AI adoption, risk modeling and understanding interconnected risks. Poor data management remains a major barrier to effectively using AI and machine learning.

Surprisingly only 14% of banks surveyed plan to significantly consolidate their customer data, while less than half say the same for non-customer data. Larger banks, possibly due to resources or regulations, are more inclined to consolidate their data.

Reluctance to consolidate data stems from perceived tool limitations and concerns about storing sensitive information. When asked about the main benefits of consolidating customer data, banks cite improved risk management (64%) and better customer experience (55%), yet many more opportunities remain untapped.

Prepare for the unexpected

Banks continue to make progress in risk management capabilities. Still, growing complexity, outdated IT infrastructure and emerging technologies like AI and GenAI have raised the bar and altered the playing field. History tends to repeat itself, but banks can choose a different path, just as my bank did before the global banking recession.

Read the full report, Transforming Risk Management: New research finds that banks urgently need to bring risk management up to date.

 

_{Images are for reference only.Images and contents gathered automatic from google or 3rd party sources.All rights on the images and contents are with their legal original owners.}