NAICS Code Classification for Fraud Detection in Loan Applications
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Solution Overview
Problem
Financial institutions face challenges in processing loan applications and mitigating fraud risks, particularly in accurately classifying businesses for credit products like Payroll Protection Program loans, where manual methods are resource-intensive and prone to errors.
Innovation Solution
A system utilizing a machine learning classification tool that generates classification options based on a business description, allowing applicants to select from suggested codes, and flags applications as suspicious if the selected code does not match the generated options, leveraging a multilayer perceptron model and confidence scoring to improve precision and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual classification methods are used to classify businesses for loan applications, then classification accuracy can be maintained through human judgment, but processing time and resource consumption increase significantly
Solution Approach 1:
The system performs preliminary classification of business descriptions into NAICS codes automatically before human review. The machine learning model pre-processes the application data, generating suggested classifications that reviewers can then verify or correct, thereby reducing the time and resources needed for manual classification while maintaining accuracy.
2Productivity
If automated classification systems are implemented to reduce manual processing, then processing efficiency improves, but fraud detection accuracy may deteriorate due to lack of human judgment
Solution Approach 1:
The system implements feedback mechanisms where classification results are continuously evaluated and refined. The machine learning model learns from reviewed cases and corrections made by human reviewers, improving its fraud detection accuracy over time while maintaining high processing throughput. This creates a closed-loop system that combines automated efficiency with human expertise.
Solution Approach 2:
The machine learning classification system serves as an intermediary between raw application data and final fraud determination. It provides suggested classifications that are then reviewed by human analysts, acting as a bridge that filters and prioritizes cases for human review based on confidence scores and anomaly detection, thereby maintaining both efficiency and reliability.
3Reliability
If comprehensive manual review of all applications is conducted to ensure accuracy, then fraud detection reliability improves, but computational resources and processing time are excessively consumed
Solution Approach 1:
The system applies partial review action by using the machine learning model to confidently classify straightforward cases without human intervention, while flagging only ambiguous or suspicious cases for manual review. This partial automation approach maintains high fraud detection accuracy for critical cases while significantly reducing overall computational resource consumption compared to reviewing all applications manually.
4Device complexity
If traditional classification methods are used without machine learning, then system complexity remains low, but adaptability to new fraud patterns and business types deteriorates
Solution Approach 1:
The classification system transitions from static rule-based methods to dynamic machine learning models that continuously adapt to new fraud patterns and business types. The model can be retrained with new data to recognize emerging fraud schemes, providing adaptability while maintaining manageable system complexity through the use of established ML frameworks and tools.
Data Source
AI summary
The concept involves efficiently using machine learning to quickly identify possible fraudulent applications in small business loan and credit applications by automatically flagging applications that meet certain criteria. In one preferred implementation, the tool compares a business description to a selected NAICS code in a loan application to assess the potential for fraud. Specifically, an algorithm can match the leftmost two digits of the selected code with the description of the category from an applicant. An engine calculates a probability of a fraud score based on the matching attached to the application. Because the tool detects fraud proactively rather than reactively, it substantially reduces computational costs and resources and reduces the biases associated with highly intensive manual work.