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Consumer credit markets: Fair and transparent?

a TFI research event
Posted on May 13, 2019

In consumer credit markets borrowers are only as good as their credit score. What makes up a credit score and what can influence it often stays unclear. Machine learning and big data play an important role in decisions by financial institutions to grant a mortgage or loan. Deducted from data on what you do rather than who you are, these decisions should increase transparency and remove biases associated with gender, race or residential area. But will big data improve the transparency and fairness in consumer credit markets?

This was the topic during TFI’s event “Fairness in Consumer Credit Markets” held on May 9 in Lund, Sweden. Various experts from both business and academia shared their insights. Data driven decisions aim to rule out unfairness in the consumer credit markets. Although characteristics such as gender and religion are explicitly excluded, it is not guaranteed that they won’t indirectly influence the credit allocation decision. The development and application of transactions-based risk models could result in less bias and broader access to credit.

Want to know more? Read the full article here.