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.