AI Loan Routing System for Underwriter Prioritization

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Solution Overview

Problem

The manual processing of loan origination applications is inefficient and prone to delays, leading to lost opportunities due to the need for manual routing and prioritization, which is costly and time-consuming.

Innovation Solution

An automated method using an AI algorithm that processes loan origination applications by receiving and analyzing parameters such as customer information, commercial value, and underwriter efficiency to identify the most suitable underwriter and prioritize applications, incorporating real-time insights and market changes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual processing of loan origination applications is used, then detailed analysis and routing decisions can be made based on specific application details, but processing time increases and opportunities may be lost due to delays

Engineering Contradiction:

Improverouting accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

An automated routing system acts as an intermediary between loan applications and underwriters, using AI algorithms to analyze application parameters and determine optimal routing decisions. This intermediary process enables faster processing while maintaining routing accuracy by systematically evaluating multiple parameters simultaneously.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The manual mechanical process of reviewing applications and making routing decisions is replaced with an automated electronic system that uses machine learning algorithms to analyze application data. This substitution dramatically reduces processing time while maintaining or improving routing accuracy through consistent application of routing criteria.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If manual routing of loan applications is performed, then routing decisions can be customized based on specific application details, but processing costs increase due to the manual effort required

Engineering Contradiction:

Improverouting customizationVSAvoidprocessing cost

Core Design Contradiction:

Adaptability or versatilityVSLoss of energy

Solution Approach 1:

The system evaluates multiple parameters from loan applications (loan amount, credit score, loan type, geographic location, etc.) and dynamically adjusts routing decisions based on combinations of these parameters. This parameter-based approach enables customized routing for each application while automating the analysis process to reduce costs.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

Manual analysis and routing decisions are replaced with automated machine learning models that can process and evaluate multiple application parameters simultaneously. This substitution maintains the ability to customize routing based on specific application details while dramatically reducing the labor costs associated with manual processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If automated routing is implemented, then processing speed and efficiency improve, but the ability to handle specific details of each application may be reduced

Engineering Contradiction:

Improveprocessing efficiencyVSAvoiddetailed analysis accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The automated system uses machine learning algorithms that have been trained on historical loan data to recognize patterns and make routing decisions. This electronic substitution enables rapid processing of multiple applications while maintaining high accuracy in detailed analysis through the algorithm's ability to evaluate numerous parameters simultaneously.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The automated routing system incorporates feedback mechanisms where routing decisions and their outcomes are continuously monitored and used to retrain and improve the machine learning models. This feedback loop ensures that processing efficiency is maintained while detailed analysis accuracy improves over time through learning from actual results.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20240169430A1Method and system for automatic loan origination application routing and prioritization

Publication Date: 2024.05.23 JPMORGAN CHASE BANK NA

AI summary

A method for automating a loan origination process by optimizing parameters used for routing loan origination applications to underwriters and prioritizing the handling of the applications is provided. The method includes: receiving first information that relates to a loan origination application; obtaining parameter values from the first information; retrieving second information that relates to candidate underwriters; generating, based on the parameter values and the second information, a task procedure for processing the loan origination application, including an identification of a target underwriter and requirements for completion of the processing of the loan origination application; transmitting, to the target underwriter, a request message that includes the first information and the task procedure; and receiving a confirmation message indicating that the task procedure is acceptable. The generating of the task procedure uses an artificial intelligence (AI)/machine learning (ML) model that leverages real time insights and adjusts for changing conditions.