ML Risk Assessment for Merchant Payment Limits
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Solution Overview
Problem
Current third-party electronic payments systems face challenges in quickly and accurately determining meaningful limits for merchants' use, leading to delayed onboarding and potential financial risks for both merchants and providers due to reliance on manual credit checks and simplistic credit scores.
Innovation Solution
Implementing a machine learning-based system that processes input data from disparate sources to generate a machine-readable vector, which is used to predict a risk score and automatically impose limits on merchants, thereby speeding up the onboarding process and providing quantifiably justifiable restrictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual credit checks and simplistic credit scores are used to determine merchant limits, then the assessment process is simple to implement, but the onboarding is delayed and financial risk assessment precision is insufficient
Solution Approach 1:
The patent replaces manual credit checks and simplistic credit scoring mechanisms with a machine learning-based automated risk assessment system. The machine learning model processes multiple data sources (transaction history, merchant information, payment patterns) to generate comprehensive risk scores, eliminating the need for manual assessment while improving both precision and speed of merchant onboarding
Solution Approach 2:
The system transforms the risk assessment approach by changing from single-parameter credit scores to multi-dimensional risk evaluation. The machine learning model analyzes numerous parameters including transaction frequency, amount variations, merchant category, and historical payment behavior, converting these into a comprehensive risk score that enables rapid and accurate merchant limit determination
2Productivity
If machine learning-based risk assessment is implemented, then onboarding speed and risk assessment precision are improved, but the device complexity increases
Solution Approach 1:
The patent segments the risk assessment system into distinct functional modules: data collection module, featurization module, machine learning inference module, and limit determination module. This segmentation allows each component to be independently optimized and maintained, reducing overall system complexity while enabling rapid onboarding through parallel processing of merchant data
Solution Approach 2:
The patent introduces a featurization layer as an intermediary between raw data input and machine learning model processing. This featurization module transforms diverse data sources into standardized numerical features, serving as a mediator that simplifies the interface between data collection and risk assessment, thereby reducing system complexity while maintaining high onboarding speed
3Reliability
If automated limit imposition is implemented, then consistent and quantifiably justifiable limits are established, but the system complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the determined merchant limits are fed back into the system to validate and refine the machine learning model. The system continuously monitors merchant performance against imposed limits, using this feedback to adjust risk parameters and improve limit determination accuracy, ensuring consistent and reliable automated decision-making while maintaining manageable system complexity
Data Source
AI summary
In general, in one aspect, one or more embodiments relate to a method including receiving, in a business rules engine, input data from disparate data sources. The input data describes a merchant and an application by the merchant to use an electronic payments system for processing transactions between the merchant and customers. Featurization is performed on the input data to form a machine readable vector. By applying the machine readable vector as input to a machine learning model in a machine learning layer, a risk score is predicted. The machine learning model is trained using training data describing use of the electronic payments system by other merchants. The risk score is an estimated probability of the merchant being unable to satisfy an obligation of using the electronic payments system. A business rules engine, based on the risk score, limits use of the electronic payments system by the merchant.