fintech

Decreasing default rates thanks to the identification of gamblers

Decreasing default rates thanks to the identification of gamblers

Working as Data Scientists, we have to solve various problems on a daily basis. Some of them are tasks we've completed many times before and some of them are completely new to us and we have to understand the business logic behind them before starting building models. The latter was the case when our client, a bank, approached us a couple of months ago with a problem they were tackling – whether to provide or deny loans to gamblers.

Data Science in a Bank: The Next Best Offer

Data Science in a Bank: The Next Best Offer

In this month's blog post, we are going to share a case study based on a project we did for one of our clients – a Slovak bank.

By now, most financial institutions have been familiar with data analysis for some time. One use case for examples is credit scoring. Money lenders, such as banks and credit card companies, have been using it for a couple of decades now to evaluate the risk of lending money to consumers and to mitigate losses. However, the recent arrival of new technologies and the rise of machine learning and Data Science brought along many new opportunities.