What factors can predict early payoffs in South Korea? How loan amount, age and reluctance to reveal marital status are key considerations when managing a loan’s life cycle.
- Too many early payoffs (EPOs) can disrupt the credit cycle and hinder a company’s ability to forecast long-term cashflow.
- In recent years, customer churn has increased due to a competitive and lowering interest rate market.
- How do we predict EPO and customer churn? Can we identify customers that will leave, before they leave?
- What are key factors correlated with EPO? What insights do they reveal?
- How do we create strategies and product offerings to reduce churn and extend credit cycle?
- A combination of internal data, Credit Bureau data and open-source data allowed us to analyse 97,000 accounts and 32,000 EPO events over an 18-month period.
- Internal data profiled customers by age, income, loan details and marital status.
- External data analysed credit history as well as environmental and employment factors.
- In 4 weeks, a series of models were prototyped and fine-tuned to calculate probabilities of EPO.
- Optional data fields– Customers willing to provide additional, non-compulsory information like ‘marital status’ were 3 times less likely to pay out loans early than those who left these fields blank.
- Youth– Those below 30 are more likely to EPO in the first 4 months than older customers. The young are generally more price sensitive and responsive to competitor marketing.
- Interest– Loans with a high interest rate are 3 times more likely to pre-pay than those with a low interest rate. Unsurprisingly, the higher the cost of debt, the stronger the incentive to settle early.
- Loan size– Smaller loans have higher churn, as the market is more competitive at lower price points.
- Home movers– Those who move home are more likely to switch banks.
- Utilising our data science model, the success could translate to an estimate of $1.2 million improvement in annual net interest margin for the business.
- Open-source data, such as information on traffic flow and air quality, improved the model’s ability to find leavers and avoid non-leavers, raising the potential incremental loan value by $10.5 million.
- Our model continues to be refined and upgraded to deliver greater accuracy and targeting precision when used with compelling offers to retain customers.