Automated Loan Offer Generation Using Transaction Data Analysis
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Solution Overview
Problem
Existing methods for constructing portfolios of assets, such as loan products, are labor-intensive and inefficient, particularly for businesses with limited capital assets or uneven revenue streams, making it difficult for them to access funds for growth.
Innovation Solution
The use of a payment transaction processing platform to analyze historical and real-time transaction data of merchants, predicting future revenue growth, and generating customized loan offers that match target characteristics for a portfolio of loans, ensuring alignment with investment goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual review processes are used to analyze merchant financial risk and determine loan offers, then loan decisions can be made with detailed financial analysis, but the process becomes labor-intensive and limits access to funds for businesses
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated computer-based system that uses machine learning models and algorithms to analyze merchant transaction data, predict revenue growth, and generate loan offers. This substitution eliminates labor-intensive manual analysis while maintaining or improving decision quality through data-driven predictions.
Solution Approach 2:
The system enables businesses to automatically receive loan offers based on their transaction data without requiring manual application or review processes. The automated system self-evaluates merchant risk and generates appropriate loan offers, making the process accessible to businesses with limited capital assets or uneven revenue streams.
2Productivity
If automated systems are used to generate loan offers based on transaction data, then access to funds improves and efficiency increases, but the complexity of the system increases
Solution Approach 1:
The patent introduces a payment transaction processing platform as an intermediary that already collects and stores merchant transaction data. This intermediary system provides the necessary data infrastructure without requiring a completely new complex system, as the loan offer generation builds upon existing transaction processing capabilities.
Solution Approach 2:
The system combines multiple functions into a unified platform that performs transaction processing, risk assessment, revenue growth prediction, and loan offer generation. This multi-functionality reduces the need for separate systems while managing complexity through integration of related functions in a single coherent platform.
3Reliability
If traditional portfolio construction methods are used, then investment goals can be met, but the process is labor-intensive and inefficient
Solution Approach 1:
The system performs preliminary actions by pre-calculating merchant revenue growth predictions and risk assessments before loan offers are needed. Machine learning models are trained in advance on historical transaction data, and the system pre-identifies suitable merchants for loan offers, enabling rapid portfolio construction when investment opportunities arise.
Solution Approach 2:
The patent transforms the portfolio construction process from manual parameter adjustment to automated optimization by changing key parameters such as using machine learning predictions for revenue growth, automated risk scoring, and algorithmic optimization to select merchants that meet target yield, loss rate, and repayment time constraints.
Data Source
AI summary
A method according to one embodiment includes: receiving historical transaction data collected by a transaction processing platform for a plurality of merchants; computing, using a machine learning model, parameters of an implied growth ratio distribution for each of the merchants based on features of the corresponding historical transaction data; identifying an offer size and an offer premium for financial offers to be made to the merchants by looking up parameters in a lookup table based on the implied growth ratio distributions, the lookup table being computed based on: a first optimization based on a target yield rate constraint and backtesting on a target loss rate constraint and a target repayment time constraint; and a second optimization based on the target loss rate constraint and backtesting on the target yield rate constraint and the target repayment time constraint; and transmitting the plurality of financial offers to the plurality of merchants.