Choosing a right business partner is one of the key factors for running a successful business, as well as choosing right customers to offer your services or products. It is true for all types of business, but particularly it’s important for the financial secto
A significant part of banks income is generated through its primary activity – lending. One of the crucial phases in the process of lending money is the evaluation of a potential customer who apply for a loan. Throughout recent decades credit scoring method has been developing to finally become major technique supporting final approval decision of a bank.
Nowadays, there are many innovative approaches to evaluate credit default risk, for example by analyzing additionally alternative data such as activity on social media platforms or shopping habits in online shops. Moreover, we can find also personalized credit scoring, for special groups of customers like for instance farmers, whose ability to repay financial obligations to a lender depend largely on results of harvest in a particular year.
AI-backed credit scoring can evaluate the final score by taking into consideration different data sources and find nonobvious correlations in order to predict whether a customer is trustworthy when it comes to repaying incurred debt. Employing intelligent technologies, such as machine learning to credit scoring can results in obtaining more accurate results due to its ability to discover more relevant correlations among wide variety of data and using it to produce more precise results.
The solution that K1 Digital created and implemented in Portugal has achieved the accuracy of 90% and it’s estimated to reduce the share on non-performing loans in the bank’s portfolio by more than one third*.
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*estimates basing on fact that within the last 3 years (last quarters of 2015-2017) default rate of consumer credits in Portugal was 16.2%.