What are the potential benefits from incorporating Machine Learning (ML) techniques in Transaction Scoring?
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
Written by Manager of Bureau Analytics, Michael Landgraf, read how the ML Score could have helped consumers enter the mainstream credit sector, thereby reducing their borrowing costs and building up their credit record.
The paper also demonstrates the impressive value of ML techniques when applied to banking transaction data, especially in contrast to data with far less nuances such as credit bureau data.
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