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CASE STUDY

Building a Machine Learning Solution for Automated Loan Screening

AT A GLANCE

An automated loan pre-screening solution was developed using machine learning and a rule-based model, enabling real-time decisions for up to 5,000 applications daily, improving efficiency, loan accessibility, and risk management.

Client information

Company Name
A fintech lender
location
Mumbai, India
SIZE
Medium-Sized Business
INDUSTRY
Financial Services
Services Provided
Loan Pre-Screening Automation
20%
Reduction in loan denials, enabling accessibility to a broader customer base without significantly increasing risk.
60%
Reduction in manual application reviews, significantly improving operational efficiency by allowing underwriters to focus on complex cases.
5000
Loan applications pre-screened daily, scaling the client’s operations to handle high volumes while improving the customer experience with faster loan processing.

CHALLENGE

The client, a sub-prime loan company, faced inefficiencies in its loan application pre-screening process, which consumed significant underwriter time and limited operational scalability. They needed an automated solution to streamline application reviews and a flexible, rule-based model to adapt quickly to evolving loan approval policies.

SOLUTION

A machine learning model using a RandomForestClassifier was implemented to automate the pre-screening process, leveraging advanced feature engineering on customer transaction data to enhance prediction accuracy. The solution was integrated into the client’s infrastructure via RESTful APIs, enabling real-time application scoring and seamless deployment. Additionally, a rule-based decision engine was developed to provide flexibility for adjusting loan approval policies dynamically, ensuring adaptability to market trends and regulatory requirements.

IMPACT

  • Increased Efficiency and Scalability: The automated pre-screening process reduced manual application reviews by 60%, allowing underwriters to focus on complex cases. The model pre-screens up to 5,000 applications daily, ensuring real-time decisions, reduced wait times for applicants, and the ability to scale operations as business demands grow.
  • Improved Loan Access and Risk Management: At a 90% probability threshold, loan denials were reduced by 20%, enabling more users to qualify for loans without significantly increasing risk. High-risk applicants are flagged for manual review, minimizing defaults and safeguarding the client’s financial portfolio.
  • Enhanced Decision-Making Flexibility: The rule-based system enables real-time adjustments to loan approval policies, allowing dynamic responses to market changes and regulatory requirements. This adaptability ensures the client remains competitive while maintaining operational compliance.

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