Risk Modeling

Credit risk management

Regulators want to see you have measures in place to mitigate the impact of credit risk on your balance sheet. From (A)IRB to IFRS9, through solid and transparent risk modeling, we help you build capital reserves to insulate you from the risk of credit deterioration.

With credit risk modelling, we make sure you’re positioned to give regulators, investors, and shareholders confidence that your business has sufficient buffers to pull through a period of economic stress.

Building a war chest of capital reserves

If market conditions plummet, regulators want assurance of your business continuity. Remaining able to provide business credit to the real economy is an important part of this. Managing credit risk enables banks to meet these expectations by building more reliable capital buffers.

Credit risk modelling provides you with the information to mitigate losses, satisfy regulatory standards, and prevent liquidity crises from derailing your strategy.

Providing opportunity as well as insurance

Credit risk models are not only a regulatory obligation for banks but also a way to gain competitive advantage. Financial institutions that accurately factor credit risk into their pricing, also see improvements in decision-making, allowing them to manage their capital more effectively.

The power of a credit risk modeling


Multi-dimensional risk modeling

Credit risk can arise from various sources. PD, LGD, EAD A-IRB, IFRS9 and stress testing – we model and evaluate the risk in your credit portfolio from a whole range of perspectives.

Measuring credit risk in the real world

Within the Zanders team, you’ll find bankers with real world experience of lending and credit acceptance. Our clients appreciate the pragmaticism they add to our approach to modelling credit risk.

Relieving the regulatory burden

Credit risk is one of the most regulated risk management areas. It’s also one of the most challenging to manage. We apply our leading minds in statistics, banking and credit risk regulation to structure credit risk models that are reliable and withstand regulatory scrutiny.


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