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Case Studies

Forecasting losses aided better management and recovery

Business challenge:

Our client, a mid-sized Private Bank was incurring high credit losses in their loan portfolio. The management was unable to predict the monthly loss run rate and ascertain which segments within the overall portfolio were likely to contribute to disproportionately higher losses.

Our solution:

Comprised of a credit loss forecast model with an ability to dynamically adjust based on monthly performance. It was parameterised to deliver the forecast based on user defined loss recognition triggers. The model also enabled measurement of the lifetime value of the portfolio.
 

Process:

The model was built based on life time portfolio booking and repayment trends. Statistical techniques were used along with business judgement to ensure that the forecasts were stable on an on-going basis.

Result:

The client used these forecasts for provision recognition and target setting for their collection unit. The collection unit also used the model to drill down and isolate the high loss yielding portfolios for focused effort.