Enhancing Effectiveness of Credit Approval
Fractal helps a large Asian credit cards issuer reduce default rates without dropping approval rates through a scorecard based approval process
The Business challenge
Our client is the second largest card issuer in a leading Asian market. The credit cards market in Asia has grown rapidly over the last 10 years. However, it has also gone through a large default crisis, precipitated by an overall economic crisis in the region. Our client believed that growth opportunities were immense but accurate risk assessment was crucial. The lack of credit bureaus made the job of risk assessment that much tougher. The bank had been using rule based credit policies and was keen to move to a more scientific method of risk assessment. The bank also wanted to reduce approval time, and automate the entire credit decision process.
The challenge lay in building an application scorecard that would help the bank control default risk without significantly affecting growth.
Building the model
A key challenge in building the scorecard was that historically the bank had very low approval rates. Therefore, there was no performance information on a significantly large portion of the population. The new scorecard would replace the old rule based method and therefore be used for scoring all customers that applied to the Bank. It was therefore necessary to build the scorecard after imputing information about the credit behavior of the rejected customers. Sophisticated reject inference techniques were used to get around this problem.
Initial results from the model were validated against testing samples and hold out samples. The results showed that the model could reduce default rates by 30% without dropping the approval rates. The bank has successfully rolled out the model nationally. This has given the bank significant competitive advantage as it can now identify the applicants that suit its risk profile and approve only those applicants that it believes match its risk profile.