Machine Learning Model for Cross Border Payment Authentication
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Solution Overview
Problem
Existing systems lack an efficient and proactive method for authenticating wire payments, which can lead to costly errors and fraudulent activities, thereby increasing the cycle time for cross-border payments.
Innovation Solution
A computer system that utilizes a machine learning model to compute genuineness scores for wire payments by integrating data from intra-bank payment networks, such as SWIFT, and internal compliance databases, providing a proactive layer of security and authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication methods are used for wire payments, then the process is simpler, but fraudulent activities are not effectively detected and cycle time increases
Solution Approach 1:
The system performs preliminary authentication actions by obtaining pre-validation data from intra-bank payment networks and internal compliance databases before the payment is processed. The machine learning model evaluates this data in advance to determine genuineness, enabling proactive fraud detection rather than reactive detection after fraudulent activities occur.
Solution Approach 2:
The patent replaces traditional mechanical authentication systems with a machine learning-based authentication system. Instead of relying on rule-based or manual verification processes, the system uses trained machine learning models that automatically analyze pre-validation data from multiple sources to determine payment genuineness, achieving both higher reliability and efficiency.
2Reliability
If machine learning model authentication is implemented, then fraud detection improves, but system complexity increases
Solution Approach 1:
The system introduces an intermediary machine learning model that acts as a mediator between raw pre-validation data and authentication decisions. The ML model consolidates complex data from intra-bank payment networks and internal compliance databases, transforming it into a simplified genuineness assessment that reduces overall system complexity while maintaining high authentication accuracy.
Solution Approach 2:
The machine learning model serves multiple functions simultaneously: it analyzes pre-validation data from different sources, detects fraudulent activities, determines payment genuineness, and provides authentication decisions. This multi-functionality consolidates what would otherwise require multiple separate systems into a single universal authentication component.
3Reliability
If multiple data sources are integrated for authentication, then security increases, but processing time increases
Solution Approach 1:
The system obtains pre-validation data from intra-bank payment networks and internal compliance databases in advance, before the authentication decision is required. This preliminary data collection and validation allows the machine learning model to process information more efficiently during the actual authentication process, maintaining high security without sacrificing processing speed.
Data Source
AI summary
Computer systems and computer-implemented methods train a machine learning model to authenticate a payment transfer between a payer and a payee. Training the machine learning model comprises training data such as pre-validation data for payment transactions from an intra-bank payment network, compliance data, and user transaction history. Authenticating the payment comprises receiving, by a deployment computer system, the proposed wire payment from the payer, obtaining a first set of pre-validation data for the proposed wire payment from the intra-bank payment network, obtaining a second set of pre-validation data via internal compliance databases. Authenticating the payment further comprises determining a genuineness score for the proposed wire payment based on the first set of data and the second set of data using the machine learning model and providing a recommendation for the proposed wire payment to the payer based on the genuineness score.