Fraud prevention & detection
How we reduced a large financial institution’s vulnerability to fraud in their origination process
We worked with a large financial institution experiencing a high level of fraud in its origination process, particularly its loan application process. The institution was concerned that the high level of fraud was negatively impacting its profitability, and damaging its reputation as a reliable and trustworthy financial institution.
As a result, the institution was able to improve its fraud prevention and detection capabilities and reduce the level of fraud in its origination process. Additionally, it was able to better protect its customers from fraudulent activity and improve its reputation as a reliable and trustworthy financial institution.
Fraud levels reduced
Customers better protected
Our team began by reviewing the bank’s existing credit loss models, including its methods for estimating credit losses under both ‘normal’ and ‘stressed’ economic conditions. Based on this review, we identified several areas where the bank’s models could be improved.
The bank’s existing models relied on a limited number of economic scenarios, which may not have adequately captured the potential range of outcomes that could result from severe economic downturns. We recommended they use a broader range of scenarios, including scenarios that were more severe than those used in the past.
We found that the historical data used to estimate credit losses may not have been adequate for predicting losses under severe economic conditions. By reviewing and improving its data quality and considering using alternative data sources, such as macroeconomic indicators, the bank could supplement its historical data to better capture potential losses.
As the models had not been rigorously validated to ensure they were accurate and reliable, we recommended that the bank conduct a model validation exercise to identify any weaknesses in its models and improve their accuracy.
Based on our recommendations, we worked with the bank to develop new credit loss models and helped the bank to implement a more comprehensive scenario analysis process, including more severe scenarios and comprehensive data inputs. We also worked with them to develop a more rigorous model validation process, including back-testing and sensitivity analysis.
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Our work demonstrated the value of engaging a third-party consultancy to conduct stress tests on credit loss models. Our expertise and guidance helped the bank to identify weaknesses in its existing models and develop new models that were more robust and accurate.
Whether you’re migrating, implementing or enhancing your models, we can support you by developing tools and strategies to provide insights and controls.