Synthetic Merchant Authentication for Harder-to-Guess Account Access
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Solution Overview
Problem
Authentication questions based on real or synthetic transactions are vulnerable to exploitation by malicious users due to the predictability and believability issues of synthetic transactions, particularly when they are not grounded in realistic merchant data.
Innovation Solution
Generate synthetic merchants by analyzing real merchant names using natural language processing techniques and machine learning models to create believable synthetic transactions, which are then used to formulate authentication questions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If synthetic transactions are used for authentication questions, then security against unauthorized access is improved, but the transactions become easily identifiable as fake reducing effectiveness
Solution Approach 1:
The patent creates synthetic merchants by copying and recombining name elements from real merchants. Instead of inventing completely fictional merchants, the system extracts meaningful components (prefixes, suffixes, location indicators) from authentic merchant names and assembles them into new, believable combinations that pass as real transactions
Solution Approach 2:
The system transforms real merchant name data by changing parameters such as combining different name elements, modifying location information, and adjusting transaction details to create synthetic versions that maintain statistical properties of real transactions while being distinguishable through analysis
2Ease of operation
If real merchant information is used for authentication questions, then the questions are believable to users, but malicious users can exploit predictability to guess answers
Solution Approach 1:
The patent segments merchant names into distinct components (prefixes, suffixes, location elements) and recombines them to create synthetic merchants. This segmentation allows the system to maintain recognizable patterns from real merchants while creating unique combinations that don't correspond to actual transactions, preventing guessing attacks
Solution Approach 2:
Instead of using real merchant transactions directly or creating obviously fake merchants, the system inverts the approach by generating synthetic merchants that mimic real ones through systematic recombination of name elements, making them indistinguishable without analysis
Data Source
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AI summary
Methods, systems, and apparatuses are described herein for improving computer authentication processes through the generation of synthetic merchants. A plurality of different real merchant names may be received. The plurality of different real merchant names may be processed to determine one or more name elements. A request for access to an account associated with a user may be received. Based on the one or more name elements, one or more synthetic merchant names may be generated. Based on the one or more synthetic merchant names, synthetic transaction data may then be generated. A synthetic authentication question may be generated and presented to a user. A candidate response to the synthetic authentication question may be received. Based on the candidate response, access to the account may be provided.