Loan Application Prioritization via Predictive Scoring
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Solution Overview
Problem
Existing tools for relationship managers in financial institutions lack the ability to prioritize loan applications effectively, leading to suboptimal chances of winning loan bids due to high volumes and reliance on manual experience rather than data-driven insights.
Innovation Solution
A system utilizing machine learning to analyze historical data and predict the probability of loan applications being funded, ranking items based on scores and providing a user-friendly interface with different views to facilitate engagement with loan arranging entities, enabling proactive or reactive engagement strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If relationship managers manually evaluate all loan applications, then they can review each application in detail, but they cannot handle high volumes of applications efficiently
Solution Approach 1:
The patent introduces an automated prioritization system as an intermediary between the high volume of loan applications and the relationship manager's evaluation capacity. This system uses machine learning models to analyze application data, calculate priority scores, and rank applications automatically, enabling relationship managers to handle high volumes while maintaining evaluation quality by focusing on prioritized subsets of applications.
2Productivity
If relationship managers rely on experience to prioritize applications, then they can make quick decisions, but the prioritization is suboptimal and reduces winning bid probability
Solution Approach 1:
The patent replaces the mechanical system of human experience-based prioritization with an automated machine learning system. The machine learning model processes application data objectively, considering multiple factors simultaneously to generate priority scores that are both fast and accurate, eliminating the limitations of subjective human judgment while maintaining rapid decision-making capability.
3Loss of information
If the system displays all loan applications in a single list, then all information is accessible, but relationship managers cannot easily identify high-priority applications
Solution Approach 1:
The patent applies local quality by differentiating the display of applications based on their priority levels. High-priority applications are visually distinguished through sorting, highlighting, or separate grouping, allowing relationship managers to quickly identify and access the most important applications while maintaining access to complete application information when needed. This creates different display qualities for different portions of the data based on their importance.
Data Source
AI summary
Systems and methods for processing items in a queue, a system including one or more memory devices storing instructions and one or more processors configured to execute the instructions to perform operations including: analyzing training data to build a predictive model; applying the predictive model to items in a queue to determine scores of the items based on respective probabilities of an entity completing an action for each item; listing the items, sorted by the scores, in a first display view; identifying a first and a second item, respectively having highest and next highest scores; grouping, with each of the first and second items, items that satisfy a grouping condition based on characteristic information of the items; and listing the groups of items including the first and second items, sorted based on the scores of the first and second items, in a second display view.