PreludeSys leverages AI & ML models to replace manual loan application processes for a leading financial services company

About the client

The client is a leading community-based financial services company in San Francisco. They offer a wide range of financial products and services to both individuals and businesses, including banking, investments, mortgages, and consumer and commercial loans.


Client challenges
  • The client wanted to automate the loan application eligibility review process that slowed down decision-making and approval workflows.
  • Eliminate manual data preparation for monthly reports while eradicating data quality issues due to a lack of an efficient cleansing framework.
  • Develop a predictive model with high accuracy for determining qualification statuses such as credit score and personal history is absent.
Solution
  • We designed eligibility prediction models and balanced them using the Synthetic Minority Oversampling Technique (SMOTE).
  • Developed ML models using Gradient Boosting, Logistic Regression, Random Forest, and XGB Classifier technologies.
  • Improved the performance of the ML model using hyperparameter tuning, which was measured and evaluated through Accuracy and Area Under ROC Curve (AUC) metrics.
  • Power BI implementation provided valuable insight into loans status (approved/rejected) by reason.
Results at a glance
Increased efficiency as ML models reduces manual efforts Faster decision making enabled by automating the workflows AI & ML models offers accuracy and predictability
Benefits
  • ML model replaced the manual loan application solution.
  • Automated approval workflow improved decision-making.
  • Reduced the decision-making time w.r.t loan eligibility from days to minutes.
  • Automated data cleansing and preparation for monthly reporting using Power BI.
  • Ability to visually present loan status by reason using Power BI.
Technology

Power BI, SQL database