Consumer Value Score for Electronic Transaction Fraud Detection
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Solution Overview
Problem
Current fraud detection systems in electronic transactions often incorrectly flag legitimate customers as potential fraud, leading to declined transactions and potential loss of customer loyalty, as they fail to accurately assess a customer's value to the merchant.
Innovation Solution
A computer-implemented method and system for generating a consumer value score based on transaction data, including a user identifier, transaction time, and purchase amount, which identifies valued customers and tailors transaction authorization and promotional strategies accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fraud detection mechanisms are implemented to prevent fraudulent transactions, then payment security is improved, but legitimate transactions may be erroneously declined causing customer frustration and loss
Solution Approach 1:
The system applies different authorization rules to different customers based on their individual value scores. High-value customers receive more lenient fraud detection thresholds and higher approval limits, while lower-value customers undergo stricter verification. This localized approach to fraud detection maintains security while reducing false positives for valuable customers.
Solution Approach 2:
The system dynamically adjusts fraud detection parameters such as transaction limits, verification thresholds, and monitoring intensity based on the customer's value score. As a customer's value score changes over time, the system modifies the strictness of fraud detection applied to that customer, allowing legitimate high-value transactions to proceed while maintaining security protocols.
2Reliability
If fraud detection mechanisms are implemented to prevent fraudulent transactions, then payment security is improved, but customer relationships may be damaged due to erroneous declines
Solution Approach 1:
The system pre-calculates customer value scores by analyzing historical transaction data, purchase patterns, and customer demographics before transactions occur. This preliminary assessment allows the fraud detection system to have advance knowledge of customer value, enabling it to differentiate between high-value and low-value customers and apply appropriate authorization rules, thereby preserving customer relationships while maintaining security.
Solution Approach 2:
The system continuously updates customer value scores based on new transaction data and behavioral patterns. This feedback loop allows the system to learn from past transactions and improve its understanding of customer value over time, reducing erroneous declines and improving both security and customer satisfaction.
3Reliability
If uniform fraud detection rules are applied to all customers, then payment security is maintained, but operational efficiency decreases due to inability to prioritize valued customers
Solution Approach 1:
The system segments the customer base into different tiers based on calculated value scores. High-value customers are placed in a premium segment that receives expedited processing and more lenient fraud detection thresholds, while other customers receive standard processing. This segmentation allows the system to maintain security across all transactions while improving efficiency for the most valuable customers.
Solution Approach 2:
The system dynamically adjusts transaction limits, authorization thresholds, and processing priorities based on real-time value score assessments. Rather than applying static uniform rules, the system adapts its operational parameters to each customer's demonstrated value, enabling more efficient processing of high-value transactions while maintaining appropriate security controls.
Data Source
AI summary
Systems and methods are disclosed for generating a consumer value score. One method includes: receiving a user identifier associated with a first user; receiving a designation of a period of time for analysis; receiving transaction data associated with the user identifier, the transaction data including a record of one or more transactions, wherein each transaction is associated with the user identifier, a merchant, a transaction time, and a purchase amount; identifying, of the received transaction data, a set of transactions conducted during the received period of time, based on the transaction time of each transaction; identifying, of the set of transactions, purchase data related to a given merchant; determining, of the purchase data related to a given merchant, a value of a purchase amount; and generating a consumer value score based on the determined value of the purchase amount.