Credit Risk Strategy: Case Study 1

Implementing daily Bureau alerts as a tool for decision-making in Account Management

4-Most consultants have completed a range of work in the account management function for a company specialising in ‘Sub Prime’ Credit Cards. Typically, on such a portfolio, credit limits rarely exceed £1000, with APRs upwards of 35%. Subscribers to this product are ‘credit hungry’ and usually consist of those who are trying to repair their credit ratings or those new to the UK. To date, the existing limit management was done through a ‘Triad’ decision engine on a month-end basis combining monthly bureau variables with the internal behavioural characteristics.

By month-end, some of the highest risk customers would have utilised all their available ‘head room’ regardless of their ability to meet minimum payment requirements thus sending them into arrears. On such low limits and with higher risk customers – a new approach was necessary. A feed was available whereby a Credit Bureau would send a daily alert the moment it received notification of one of these customers going 2 cycles down, or bankrupt or receiving a CCJ elsewhere which would allow us to ‘action’ the account promptly rather than waiting for the automated process at month-end, which by then could be too late.

After 6 months, it showed significant value. The £ differential due to lagged time by waiting till month-end for an automated limit reduction treatment was significant, as was the fact, that between notification and month-end many of these higher risk customers had utilised all available ‘head room’ making them unavailable for limit reductions.

Credit Risk Strategy: Case Study 2

Mining current account transactional data to inform Credit Decisions.

Leveraging data effectively is critical to any large organisation’s success. 4-Most consultants have completed analysis of current account data where we were able to identify particular transactions from the Department of Work and Pensions.

Many financial institutions capture employment status via applications and / or the customer updating them with the right information for use in credit decisioning. Unfortunately because this data is captured primarily at application point means that after a period much of the information held could be quite outdated.

Retrospective analysis of customers in receipt of Job Seekers Allowance (JSA) payments highlighted that there was a significant breach of risk appetite within this segment. Customers newly in receipt of JSA payments may be recently unemployed and seeking credit to tide them over before finding employment. Similarly benefit types such as income support may be subtracted from any customer income calculations to ensure the credit facility would be affordable should the benefit cease. Aside from ensuring responsible lending practice, impairment will reduce and long term profitability is maintained as the customer can manage the size of the credit line effectively.

By looking deeper into data that’s readily available the solution provided a more responsible credit decision, reduction in new product for existing customer default rates and a better chance for the customer to have a fuller conversation with the bank about their circumstances where professional staff can give the right advice.