|With the credit card still being a most widely excepted credit product, credit card companies have an essential responsibility to protect their customers from risks; both external (e.g. fraud) but also internal (e.g. irresponsible credit usage).|
This credit card company has historical transactions at its disposal, which contain valuable information on credit behaviour. Investigating each transaction individually is not feasible; therefore, business rules were used. However, capturing all risk pattern in business rules is difficult since user behaviour changes over time.
Using machines learning, we developed a model to uncover the most important patterns from historical transactions to make a pre-selection of high-risk clients. This pre-selection of clients can serve as a good basis for further investigation of a credit expert, as such saving time in time consuming process.
• Efficiency in a labor intensive process
• Self-learning model which can be re-trained easily
• Reveal complex transaction patterns