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Credit Risk Management


Companies need to manage their risks to ensure profitable growth. There is a delicate balance to be struck in order to minimize losses without declining all opportunities. A system is needed to determine what are good versus bad opportunities.

Companies collect a ton of information, including information on how customers pay their bills or repay their loans. For these companies, certain customers prove to be more profitable, by respecting their payment terms, while others generate losses. Predictive analytics techniques use these data to forecast customer behaviour and help the company efficiently manage credit risk.

Credit risk predictive models prove beneficial at a number of levels:

  • At the credit application stage, predictive models allow institutions to make better decisions: accept the application; accept with certain conditions or refuse the application;
  • For existing customers, predictive models allow institutions to determine for whom to authorize a credit increase, as well as limit the risks generated by delinquent customers;
  • For customers accumulating late payments, predictive models help the collection service prioritize corrective actions.

SolutionStat Inc. is pleased to have on its team statisticians who have held positions in the management of customer credit risk for a leading Canadian financial institution.