May 2025
Incorporating Machine Learning techniques in banking transaction scores
Read our latest research report, detailing how a Machine Learned Transaction Score would have helped the two case-study lenders in Australia (one offering Personal Loans, the other offering Credit Cards) to:
- Increase their credit approval rates, while still effectively mitigating their credit risk
- Decrease bad debts & increase loan profit (per-capita)
- Lower borrowing costs without underwriting additional risk
- Increase credit opportunities for traditionally underserved consumers
This is a report by Manager of Bureau Analytics, Michael Landgraf
