Banks are often faced with the difficult decision of whether or not to chase debts. On the one hand, they want to collect as much money as possible. On the other hand, they don't want to spend more money chasing debts than they're worth.

A new method developed by researchers at the University of California, Berkeley, could help banks make this decision. The method uses a machine-learning algorithm to predict the probability that a borrower will default on a loan and the amount of money the bank is likely to recover if they chase the debt.

The researchers tested the method on a dataset of over 1 million loans. They found that it was able to predict defaults and recoveries with a high degree of accuracy. This suggests that banks could use the method to improve their decision-making processes and increase their profitability.

The method could also be used by other lenders, such as credit card companies and student loan providers. By more accurately predicting defaults and recoveries, these lenders could make better decisions about which debts to pursue and improve their bottom lines.

Here are the key points of the new method:

* Uses a machine-learning algorithm to predict the probability that a borrower will default on a loan.

* Considers a variety of factors, including the borrower's credit score, debt-to-income ratio, and payment history.

* Predicts the amount of money the bank is likely to recover if they chase the debt.

* Has been tested on a dataset of over 1 million loans and shown to be highly accurate.

* Could be used by banks and other lenders to improve their decision-making processes and increase their profitability.

Benefits of the new method:

* Helps banks make better decisions about which debts to chase.

* Increases the accuracy of default and recovery predictions.

* Improves profitability by reducing the cost of chasing bad debts.

* Can be used by other lenders, such as credit card companies and student loan providers.

Conclusion:

The new method developed by researchers at the University of California, Berkeley, could help banks make better decisions about when to chase debts. The method uses a machine-learning algorithm to predict the probability that a borrower will default on a loan and the amount of money the bank is likely to recover if they chase the debt. The method could also be used by other lenders, such as credit card companies and student loan providers.