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Credit Risk Management –
What it is and why it matters

Do you want to meet regulatory requirements for credit risk? Or do you want to go beyond the requirements and improve your business with your credit risk models? If your credit risk is managed properly, you should be able to do both. Let’s break it down.  

Credit risk refers to the probability of loss due to a borrower’s failure to make payments on any type of debt. Credit risk management is the practice of mitigating losses by assessing borrowers’ credit risk – including payment behavior and affordability. This process has been a longstanding challenge for financial institutions.

Continued global economic crises, ongoing digitalization, recent developments in technology and the increased use of artificial intelligence in banking have kept credit risk management in the spotlight. As a result, regulators continue to demand transparency and other improved capabilities in this space. They want to know that banks have a thorough knowledge of customers and their associated credit risk. And as Basel regulations evolve, banks will face an even bigger regulatory burden.

To comply with ever-changing regulatory requirements and to better manage risk, many banks are overhauling their approaches to credit risk. But banks who view this as strictly a compliance exercise are being short-sighted. Better credit risk management presents an opportunity to improve overall performance and secure a competitive advantage.  

Challenges to successful credit risk management

  • Inefficient data management. An inability to access the right data when it’s needed causes problematic delays.
  • No groupwide risk modelling framework. Without it, banks can’t generate complex, meaningful risk measures and get a big picture of groupwide risk.
  • Constant rework. Analysts can’t change model parameters easily, which results in too much duplication of effort and negatively affects a bank’s efficiency ratio.
  • Insufficient risk tools. Without a robust risk solution, banks can’t identify portfolio concentrations or re-grade portfolios often enough to effectively manage risk.
  • Cumbersome reporting. Manual, spreadsheet-based reporting processes overburden analysts and IT.

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Top retail bank applies AI to improve customer service and credit scoring

“SAS didn’t just provide us with one solution that solved one problem – SAS covered the entire analytics life cycle and most of our needs. Once we started discussing this within S-Bank, we saw clearly that SAS was a one-to-one match for what we had drawn up and what we needed," said Johanna Makkonen, Senior Analyst for S-Bank.

Best practices in credit risk management

The first step in effective credit risk management is to gain a complete understanding of a bank’s overall credit risk by viewing risk at the individual customer and portfolio levels.

While banks strive for an integrated understanding of their risk profiles, much information is often scattered among business units. Without a thorough risk assessment, banks have no way of knowing if capital reserves accurately reflect risks or if loan loss reserves adequately cover potential short-term credit losses. Vulnerable banks are targets for close scrutiny by regulators and investors, as well as debilitating losses.

The key to reducing loan losses – and ensuring that capital reserves appropriately reflect the risk profile – is to implement an integrated, quantitative credit risk solution. This solution should get banks up and running quickly with simple portfolio measures. It should also accommodate a path to more sophisticated credit risk management measures as needs evolve. The solution should include:

  • Better model management that spans the entire modelling life cycle.
  • Real-time scoring and limits monitoring.
  • Robust stress-testing capabilities.
  • Data visualisation capabilities and business intelligence tools that get important information into the hands of those who need it, when they need it.

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