Machine Learning Queue for Loan Application Prioritization
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Solution Overview
Problem
Existing tools for loan application management in financial institutions lack the ability to intelligently prioritize actions, leading to suboptimal decision-making due to the high volume of applications and reliance on manual evaluation by relationship managers.
Innovation Solution
A system and method utilizing machine learning to analyze training data, build predictive models, and apply them to loan applications to determine probabilities of funding based on proactive or reactive engagement, ranking applications for prioritization and reporting through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If relationship managers manually evaluate loan applications, then decision-making can be made with human judgment, but the productivity is low due to high volume of applications
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the loan applications and relationship managers. The model processes applications automatically and generates predictions, serving as a mediator that handles the high-volume processing while relationship managers focus on complex decision-making for prioritized applications.
Solution Approach 2:
The patent segments the loan application processing into two streams: automated processing by the machine learning model for initial evaluation and prioritization, and human review by relationship managers for final decisions on prioritized applications. This segmentation allows both high productivity and reliable decision-making.
2Ease of operation
If relationship managers rely on experience to prioritize applications, then manual evaluation can be performed, but the prioritization becomes suboptimal
Solution Approach 1:
The patent replaces the mechanical system of human experience-based prioritization with an automated machine learning system. The ML model objectively analyzes application data and generates prioritization rankings, eliminating the limitations of subjective human judgment while maintaining ease of operation through automated processing.
Solution Approach 2:
The machine learning model performs self-service by automatically evaluating and prioritizing loan applications without requiring manual intervention from relationship managers. The system independently processes applications, generates predictions, and creates prioritized queues, freeing managers to focus on final decision-making.
3Measurement precision
If all loan applications are processed manually, then thorough evaluation can be achieved, but the loss of time increases due to high application volume
Solution Approach 1:
The patent applies preliminary action by having the machine learning model perform initial evaluation and prioritization of all loan applications before relationship managers review them. This preliminary processing filters and ranks applications, so managers only need to conduct thorough evaluation on a smaller set of prioritized applications, reducing overall processing time while maintaining evaluation precision.
Data Source
AI summary
Systems and methods for optimizing processing of loan applications are disclosed. A system may include one or more memory devices storing instructions and one or more processors configured to execute the instructions. The instructions may instruct the system to analyze training data to build a predictive model. The instructions may also instruct the system to apply the predictive model to a loan application to determine a first probability of an institution funding the loan application if the institution proactively engages a loan-arranging entity of the loan application and a second probability of the institution funding the loan application if the institution reactively engages the loan-arranging entity. The instructions may further instruct the system to reporting the first and second probabilities to a user through a user interface.