ML Auto-Adjudication for Loan Processing Bottlenecks
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Solution Overview
Problem
The existing loan adjudication process is inefficient, requiring significant manual work and taking days or weeks to complete, necessitating a more efficient method for processing loan applications.
Innovation Solution
An apparatus and method utilizing a machine learning model, specifically a decision tree, to automatically adjudicate loan applications by extracting personal attributes, querying the model via an API, and determining approval based on identified rules, with the ability to transmit approval notices electronically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review process is used for loan adjudication, then human expertise and policy application are maintained, but processing time increases significantly to days or weeks
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated machine learning system. The ML model extracts attributes from loan applications and automatically applies decision rules to determine approval or denial, substituting human manual adjudication with an automated computational system that operates in seconds rather than days or weeks.
2Adaptability or versatility
If manual adjudication process is used, then complex policy guidelines can be applied by human judgment, but significant manual work and human resources are required
Solution Approach 1:
The patent transforms complex policy guidelines into structured decision rules with defined parameters and thresholds. The ML model evaluates loan applications against these parameterized rules, maintaining the adaptability of policy application while enabling automated high-volume processing. The system can process multiple applications simultaneously without additional human resources.
3Productivity
If automated machine learning process is implemented, then processing time is reduced to seconds, but complexity of the adjudication system increases
Solution Approach 1:
The patent introduces an intermediary layer between the loan application and the decision outcome. The ML model serves as this intermediary, containing the complexity of attribute extraction, rule evaluation, and decision logic within a single automated system. This intermediary handles the computational complexity while presenting a simple interface for loan approval or denial decisions.
4Reliability
If human employees perform loan reviews, then first-hand knowledge and policy understanding are applied, but the process requires significant human intervention and cannot scale efficiently
Solution Approach 1:
The patent implements a self-service automated adjudication system where the ML model independently extracts attributes from loan applications, evaluates them against embedded decision rules, and generates approval or denial decisions without human intervention. The system serves itself by automatically processing applications through the complete adjudication workflow, eliminating the need for human employees to perform manual reviews while maintaining consistent policy application.
Data Source
AI summary
An example operation may include one or more of receiving a loan application of a user, extracting a plurality of personal attributes about the user from the loan application, querying a machine learning model via an application programming interface (API) based on the plurality of attributes about the user to identify one or more rules for auto-adjudicating the loan application, determining whether or not to approve the loan application based on the one or more rules identified via the machine learning model, and transmitting notice of the determination to a device associated with the user.