Customizable Risk Management Tools with Statistical Modeling

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Solution Overview

Problem

Existing payment systems lack control over user experience, robust fraud prevention mechanisms, and the ability to provide understandable explanations for blocked transactions, leading to poor user experience and limited fraud management capabilities for merchants.

Innovation Solution

Implementing user customizable risk management tools with statistical modeling and a recommendation engine within a computing environment, allowing merchants to define rules for transaction acceptance or rejection, monitor rule performance, and receive recommendations for rule retention or cancellation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If merchants work with a third party payment provider to handle their payment needs, then implementation simplicity is improved, but user experience control and fraud prevention capability deteriorate

Engineering Contradiction:

Improveimplementation simplicityVSAvoiduser experience control

Core Design Contradiction:

Ease of manufactureVSAdaptability or versatility

Solution Approach 1:

The system segments fraud prevention control into two layers: (1) automated machine learning models that analyze transaction patterns and generate fraud scores, and (2) customizable user-defined rules that merchants can configure through a graphical interface. This segmentation allows merchants to maintain control over fraud prevention strategy while benefiting from automated analysis, resolving the contradiction between implementation simplicity and user experience control.

Inventive Principle:

Principle #1Segmentation

2Ease of manufacture

If merchants work with a third party payment provider, then implementation simplicity is improved, but fraud prevention capability deteriorates

Engineering Contradiction:

Improveimplementation simplicityVSAvoidfraud prevention capability

Core Design Contradiction:

Ease of manufactureVSReliability

Solution Approach 1:

The system implements feedback mechanisms where transaction outcomes (approved/rejected) are fed back into the machine learning model to continuously improve fraud detection accuracy. Additionally, merchants receive feedback through explanations of why transactions were rejected, enabling them to adjust their customizable rules. This feedback loop enhances fraud prevention capability while maintaining implementation simplicity through automated learning.

Inventive Principle:

Principle #23Feedback

3Reliability

If automated fraud detection systems block transactions based on risk scores, then fraud prevention is improved, but user understandability of blocking decisions deteriorates

Engineering Contradiction:

Improvefraud preventionVSAvoiduser understandability

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The system introduces an explanation generation component that acts as an intermediary between the automated fraud detection system and the merchant. This component translates complex risk score calculations into human-readable explanations that describe why a transaction was rejected, preserving both the automated fraud prevention capability and merchant understandability of decisions.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Reliability

If merchants implement robust fraud prevention schemes, then fraud detection accuracy is improved, but system complexity increases

Engineering Contradiction:

Improvefraud detection accuracyVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system enables merchants to self-configure fraud prevention rules through an intuitive graphical user interface without requiring technical expertise. The machine learning model automatically analyzes transaction patterns and generates fraud scores, eliminating the need for merchants to manually configure complex detection algorithms. This self-service approach maintains high fraud detection accuracy while minimizing system complexity for the user.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12327251B2Systems, methods, and apparatuses for implementing user customizable risk management tools with statistical modeling and recommendation engine

Publication Date: 2025.06.10 STRIPE LLC

AI summary

Systems, methods, and apparatuses for implementing user customizable risk management tools with statistical modeling and a recommendation engine within a computing environment are provided. A system may include, for example, means for evaluating the performance of a user rule for fraud prevention, in which the system receives a plurality of purchase transactions for the user; analyzes each purchase transaction received to generate a fraud likelihood score; receives the rule that specifies conditions when the system is to accept or reject transactions regardless of the fraud likelihood score generated by the system; transmits a historical analysis to the user based on the received rule; receives an input from the user to activate the rule; monitors performance of the rule; and transmits a recommendation to the user to retain or cancel the activated rule based on the monitored performance. Other related embodiments are disclosed.