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Credit Risk Scoring Engine

  • Case Study
  • 2020-10-29
  • Muoro Team

Credit Risk Scoring Engine

Client Introduction:

The client is a leading credit management group specializing in debt collection, business information & credit reports and Para-legal services.

Business Challenge:

The client wanted to enhance the current credit risk methodology and wanted to enable learning algorithms to increase the efficiency for current business reports. Further client wanted a better data analytics suite as they wanted to shift from basic excel modeling to advanced statistics with good visualizations.

MUORO’s deployment:

So the current system MUORO had some pre defined advanced statistical models, however we developed the learning algorithms after getting right inputs from the client and making a smart analytical system which had customized algorithms relevant to their needs and industry. Provided the system to ease the analytics job for their analysts with complete dashboard offering collaborative drag &drop features with Tableau

Technologies Used:

The system was designed using the technologies as followed:
• Database – NoSql(MongoDB)
• Frontend – Integration with Tableau for Visualizations
• Backend – Node.js
• Analytics and statistical models – Used Python and associated libraries (numpy, pandas and pytorch)
• API – Developed REST APIs for internal connections with .NET Framework


The new predictive credit risk models helped the company to evolve their obsolete methodology, the decision tree analysis included new set of parameters that were segregated according to learning algorithm, hence automating the task and increasing the efficiency.

• 9% Increase in revenue through new business reports
• 12% decrease in the total time cycle to generate the business reports
• 89% Accuracy rate of the scoring engine

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