A regional online bank applied Python based machine learning to account holders in its Reward Program, helping to identify accounts “gaming” the system, predict activity and high-probability loan defaults, and keep customers informed of their Rewards eligibility.
Business Need
Tools and Technology
The bank partnered with OwlFinancial to use Anaconda Distribution and Python Machine Learning Libraries for the following four use cases: predicting loan behavior, mining consumer insights, personalizing the user experience, and centralizing the outcome data. The developed code, which was written in Python and SQL, is reusable and configurable, enabling flexibility and further applications in other areas of the business. Development not only involved gathering data insights through machine learning models, but also the delivery of a complete solution via web services consumed by the MuleSoft integration platform, front-end UI for customers and CSRs, dashboards generated using Tableau, and deployment pipeline with DataRobot.
Success Factors
The bank attributed the project’s success to smooth collaboration with the OwlFinancial team, as well as OwlFinancial’s investment in ensuring the best quality outcomes. The company also cited OwlFinancial’s domain knowledge, deployable assets, and accelerators as key factors in keeping the project on time and under budget.
Impact
SNAPSHOT
Industry & Region
Financial Services, India
Benefits
- Improved customer experiences by enabling transparency, on-demand access to the Rewards Program, and real-time notification of rewards
- Reduced rate of defaulting using proactive communication approach with customers identified as highly likely to default
- Increased the bank’s competitiveness in the market of FinTechs
Technology Stack
- Developer Tools: Anaconda Distribution and Python Machine Learning Libraries
- Webservices: Flask framework
- Solution Documentation: Jupyter notebook
- Deployment: DataRobot
- Dashboard: Tableau
