Hexaview Technologies

Developed an Application for EMI Repayment Prediction Leading To A Huge Decrease In Frauds Rates

Business Scenario

The client was a well-established lending firm having a client base across the globe. They were facing challenges in judging their customer’s loan settlement capabilities, further leading to losses and affecting their decision-making abilities.

Client’s Requirements

  • The client was not able to judge their customer’s loan repayment capabilities.
  • They were encountering challenges in analytics and decision making.

Hexaview’s Solution

We developed an application to predict loan repayment capabilities & validate the eligibility of potential consumers.

Tools & Technologies Used:

  • Languages: Python & React.JS
  • EDA: Density plot, Scatter plot, Box plot, Outlier detection on this dataset using Jupyter notebook
  • Feature Engineering: PCA, Standardization, Normalization, Dummy feature addition
  • Prediction Algorithm: Random forest, Logistic regression, Decision Tree Classifier
  • Libraries: Flask, Sklearn, Joblib, Pathlib, Pickel

Impact of the implementation:

  • It facilitated the identification process of potential fraud customers, making the process simpler.
  • Decrease in the number of potential fraud customers by 70%.

Key Success Factors

Empowering our solutions with AI & ML helped us in creating more sustainable products for our clients.

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