Generic scorecards that use large, general populations are good at predicting payment behavior on an average population, but custom models that focus only on the customers in your portfolio are even better. By using the origination and payment data from your customers or members, you can identify the distinct characteristics that count most when decisioning your applications.
- Competitors: More lenders are using custom scores than ever before. In such a competitive marketplace, it’s always good to know what you’re up against. Let’s face it, a “keeping up with the Joneses” mentality can be healthy when it helps you evaluate if such a decision will either help you keep up or propel you ahead of the pack.
- Data history: Depending on volumes, you should have at least two years of application data (including rejects) and account performance monthly snapshots of any accounts resulting from approvals.
- Data volume: Unless you have a high delinquency rate, you should have approximately 2,000 monthly applications over the time frame listed above.
- Management environment: To make the best use of a solution like this, changes to the existing credit policy will likely be required. It is helpful to understand the reception your management team will have to policy changes and account for this in your buy-in plan.
- Multi-dimensional to manage every category of risk financial institutions face today
- Scalable to provide the analytics needed today and to lay the foundation for the analytics of tomorrow
- Expandable across the credit cycle and lending functions
- Agile for rapid return on decision making investments
