When it comes to managing financial risk, there are many different aspects to look at. Two of the most significant risks in the banking and fintech industry are fraud risk and credit risk.
As Costin Mincovici, tbi bank’s Chief Credit Officer explains:
tbi bank managed its risks with the help of SEON. tbi simulated and modeled the performance and behavior of its customers using the data collected through SEON and enhanced the latter’s machine learning score models to better predict the performance of their customers.
So, what did this result in? The improved models allowed tbi to better predict the performance and behaviour of its customers and more effectively manage its approaches to credit risk scoring and the customer journey.
Costin had this to say about the improvement:
By enhancing our machine learning models we were able to improve approval rates for our customers by 5 percentage points, a significant improvement in onboarding new customers rapidly.
Allow rapid decision making for a frictionless onboarding experience. Leaving your team more time to help your Neobank grow.
This meant that the bank was able to expand its customer base and increase its revenue streams, all while effectively managing credit risk. This all resulted in a great ROI for tbi bank.
By focusing on other types of data, tbi bank was still able to gain insights into customer behavior and preferences without relying on potentially sensitive demographic information, leading to a significant improvement in the customer experience.
Costin offered these concluding words:
By leveraging the power of advanced analytics and data-driven insights, we were able to achieve our goals of expanding our customer base while effectively managing credit risk.
For more information about tbi bank, visit www.tbibank.bg