Intelligent loan application solution using ML and Power BI visualization
About the client
The client is a diversified, community-based financial services company in San Francisco. Their vision is to satisfy customers’ financial needs and help them succeed. They provide banking, investment, mortgage products and services, and consumer and commercial finance.
Client requirement
- Mundane activities involved in loan application eligibility checks hindered decision-making processes and approval workflows.
- Monthly reports required manual data preparation that consumed too much time and raised data quality issues due to a lack of an efficient cleansing framework.
- A prediction setup for determining qualification statuses such as credit score and personal history is absent.
- The client wanted to automate the loan application eligibility review process and reduce the time spent on monthly reporting.
Solution
- PreludeSys designed eligibility prediction models and balanced them using the Synthetic Minority Oversampling Technique (SMOTE).
- Created ML models using technologies such as Gradient Boosting, Logistic Regression, Random Forest, and XGB Classifier.
- Hyperparameter tuning further improved ML model performance which was evaluated through Accuracy and Area Under ROC Curve (AUC) metrics.
- Power BI implementation provided valuable insight into loans status (approved/rejected) by reason.
Benefits
- ML model replaced the manual loan application solution.
- Loan eligibility decisions in minutes instead of days or weeks.
- Proper data cleansing and preparation for monthly reporting using Power BI.
- Power BI visuals present loan status by reason.
- Ability to predict eligibility and loan amounts.
- Automated approval workflow improved decision-making.
Technology
Power BI, SQL database
