The table of content
Overview
In the banking sector, accurate and timely risk assessment of corporate clients plays a critical role. The quality of this assessment directly influences a bank’s credit strategy, financial stability, and its ability to respond to market changes. Being able to quickly and reliably evaluate the financial standing of business clients helps reduce the share of non-performing loans, increase portfolio profitability, and improve overall decision-making.
Traditionally, developing credit scoring models for corporate clients has been a time-consuming and resource-intensive process. It often required teams of experienced analysts working over several months. As a result, banks faced a constant trade-off between model quality and development speed, limiting their flexibility in a dynamic business environment.
Challenge
With growing competition and rapid economic shifts, traditional manual model development became a bottleneck. Each new model could take up to four months to complete and required the involvement of a team of highly qualified experts, including PhD-level professionals.
This approach had several downsides:
Solution
To address these challenges, an automated approach to credit scoring model development was introduced. The goal was to drastically reduce development time while maintaining the precision of the results.
The entire modeling process — from variable selection to model structure, testing, and validation — was moved to an analytical environment that doesn’t require large teams. The models are easily adapted to specific industries and client profiles, ensuring high accuracy.
With automation in place, a complete scoring model can now be developed by a single analyst in just 30 minutes — instead of four months of team effort. This shift enabled faster, more flexible, and cost-effective risk evaluation.
Business Impact
The impact of the new approach was significant:
Conclusion
This case shows how automating credit scoring can transform traditional banking processes. What once required months of team effort can now be done by one person in half an hour — with better speed, scalability, and precision.
The results demonstrate that efficiency and quality don't have to be mutually exclusive. With the right approach, banks can modernize their risk assessment practices and stay competitive in a rapidly changing financial landscape.
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