So, what is the role of a Business Intelligence in this world of credit assessment?
Let’s imagine that we had the following items of data about our customers over a history of thirteen months:
- Ø CDRs for incoming and outgoing calls
- Ø Invoice details
- Ø Payment details
- Ø Personal attributes – gender, date of birth, address etc.
- Ø External demographics, credit scores etc.
By running complex queries (neural networks and various statistical operations) over this history we can find answers to the following questions based on people that we know have already used, or are using, our own services:
- Ø What behaviour consistently leads up to someone defaulting on payment?
- Ø What types (segments) of customer tend to default on payments?
- Ø Are there significant patterns in defaulting for individual customers?
- Ø Is defaulting in payment correlated to:
- unusual increase in line usage?
- an unusual and sudden amount of international calls to a specific location?
- time of the year?
- other economic happenings?
Based on this type of analysis and some simple profitability modelling we can assess the money actually lost through non-payment and compare it with the costs of various treatments. We might also start to allow different segments of our customer base to default under certain circumstances, safe in the knowledge that they will likely return to profitability in the near future. Similarly we can start to predict the likely credit worthiness of specific potential customers and modify our marketing strategy by the perceived risk, setting up more sophisticated contractual agreements where risk is counterbalanced with assets.