Propensity Model for Predicting Business Financial Needs
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Solution Overview
Problem
Growing businesses often face disadvantages when seeking financing due to a lack of awareness about their future financial needs, leading to burdensome and protracted processes for obtaining loans, forcing them to choose between higher interest rates or delaying business activities.
Innovation Solution
A method and system that utilize an externally augmented propensity model to score businesses based on their financial and operational data, identifying future financial requirements and triggering targeted messaging for financing opportunities when specific workflow events occur, ensuring timely and appropriate access to funding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If businesses wait until they need financing to apply for loans, then they can assess their actual financial needs, but they face higher interest rates and protracted approval processes
Solution Approach 1:
The system performs preliminary scoring and identification of businesses likely to need financing in the future, before they actually apply. By using propensity models to predict future financial requirements based on current business data, the system prepares loan offers in advance, eliminating the protracted approval process when the business actually needs funding.
2Loss of time
If businesses apply for financing early, then they can access funds sooner, but they may not yet have sufficient financial need or credit profile
Solution Approach 1:
The system continuously monitors business data and updates propensity scores in real-time. When a business reaches certain thresholds or triggers specific workflow events (like adding employees or increasing revenue), the system automatically generates loan offers. This feedback mechanism ensures businesses are offered financing at the optimal moment when they are most likely to qualify and need funding.
3Measurement precision
If lenders manually assess each business application, then they can thoroughly evaluate creditworthiness, but the process becomes burdensome and inefficient
Solution Approach 1:
The system replaces manual credit assessment with automated propensity models that analyze business data using machine learning algorithms. These models evaluate multiple data points simultaneously to predict future financial requirements and creditworthiness, maintaining high accuracy while dramatically increasing processing efficiency and reducing lender workload.
4Productivity
If lenders offer financing proactively to identified businesses, then they can capture more lending opportunities, but they risk offering loans to businesses that don't yet need or qualify for financing
Solution Approach 1:
The system dynamically adjusts propensity scores and offer thresholds based on changing business conditions and model performance. By continuously refining the parameters that determine when to make offers, the system optimizes the balance between capturing lending opportunities and ensuring loan suitability, reducing both false positives and missed opportunities.
Data Source
AI summary
A method for transmitting a message based on the occurrence of a workflow event and the output of an externally augmented propensity model. The method includes scoring a business entity by applying a propensity model to data. The data includes a first portion created based on a platform utilized by users associated with the business entity, and a second portion that includes financial data of an owner of the business entity. The method includes generating, based on the score, a classification of a future financial requirement of the business entity, and determining that the classification meets a financial requirement threshold. Moreover, the method includes determining, using the first portion of data, that an aspect of the business entity meets a business activity threshold. Also, the method includes detecting that a workflow event has occurred on the platform, and, in response, transmitting a message to a user of the business entity.