Today, many financial institutions extending credit rely on automated credit scorecard decision engines to drive credit strategies that are used to allocate (application scoring) and manage (behavioural scoring) credit limits. The accuracy and predictive power of these models are meticulously monitored, to ensure that they deliver the required separation between good (non-delinquent) accounts and bad (delinquent) accounts. The strategies associated to the scores (champion strategies) produced using the scorecards, are monitored on a quarterly basis (minimum), ensuring that the limit allocated to a customer, with its associated risk, is still providing the lender with the best returns on their appetite for risk. The strategy monitoring opportunity should be used to identify possible clusters of customers that are not producing the optimal returns for the lender. The identified existing strategy (champion) that does not return the desired output is challenged with an alternative strategy that may or may not result in better results. These clusters should have a relatively low credit risk ranking, be credit hungry, and have the capacity to service the debt. This research project focuses on the management of (behavioural) strategies that manage the ongoing limit increases provided to current account holders. Utilising a combination of the behavioural scores and credit turnover, an optimal recommended or confidential limit is calculated for the customer. Once the new limits are calculated, a sample is randomly selected from the cluster of customers and tested in the operational environment. With the implementation of the challenger, strategy should ensure that the intended change on the customer's limit is well received by the customers. Measures that can be used are risk, response, retention, and revenue. The champion and challenger strategies are monitored over a period until a victor (if there is one) can be identified. It is expected that the challenger strategy should have a minimal impact on the customers affected by the experiment and that the bank should not experience greater credit risk from the increased limits. The profit from the challenger should increase the interest revenue earned from the increased limit. Once it has been established through monitoring whether the champion or the challenger strategy has won, the winning strategy is rolled-out to the rest of the customers from the champion population. / Thesis (Ph.D. (Operational Research))--North-West University, Vaal Triangle Campus, 2009.

Today, many financial institutions extending credit rely on automated credit scorecard decision engines to drive credit strategies that are used to allocate (application scoring) and manage (behavioural scoring) credit limits. The accuracy and predictive power of these models are meticulously monitored, to ensure that they deliver the required separation between good (non-delinquent) accounts and bad (delinquent) accounts. The strategies associated to the scores (champion strategies) produced using the scorecards, are monitored on a quarterly basis (minimum), ensuring that the limit allocated to a customer, with its associated risk, is still providing the lender with the best returns on their appetite for risk. The strategy monitoring opportunity should be used to identify possible clusters of customers that are not producing the optimal returns for the lender. The identified existing strategy (champion) that does not return the desired output is challenged with an alternative strategy that may or may not result in better results. These clusters should have a relatively low credit risk ranking, be credit hungry, and have the capacity to service the debt. This research project focuses on the management of (behavioural) strategies that manage the ongoing limit increases provided to current account holders. Utilising a combination of the behavioural scores and credit turnover, an optimal recommended or confidential limit is calculated for the customer. Once the new limits are calculated, a sample is randomly selected from the cluster of customers and tested in the operational environment. With the implementation of the challenger, strategy should ensure that the intended change on the customer's limit is well received by the customers. Measures that can be used are risk, response, retention, and revenue. The champion and challenger strategies are monitored over a period until a victor (if there is one) can be identified. It is expected that the challenger strategy should have a minimal impact on the customers affected by the experiment and that the bank should not experience greater credit risk from the increased limits. The profit from the challenger should increase the interest revenue earned from the increased limit. Once it has been established through monitoring whether the champion or the challenger strategy has won, the winning strategy is rolled-out to the rest of the customers from the champion population. / Thesis (Ph.D. (Operational Research))--North-West University, Vaal Triangle Campus, 2009.