ML Settlement Pathing for Overdraft Fee Avoidance
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Solution Overview
Problem
Existing financial systems lack the ability to accurately determine account status in real time, leading to potential overdraft fees and frustration for customers due to the slow confirmation of electronic fund transfers, which can result in costly penalties for both customers and card issuers.
Innovation Solution
Employing a machine learning platform to analyze account transaction data, identify fee causes, and define settlement paths to minimize the likelihood of triggering fees, using a fee identification engine and fee avoidance engine to update models continuously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ACH transfers are used for electronic fund transfers, then money transfer capability is provided, but transfer confirmation is slow and account status is uncertain
Solution Approach 1:
The system performs preliminary actions by continuously monitoring account transactions and predicting potential overdraft conditions before they occur. The machine learning model analyzes historical data and transaction patterns to forecast account status, allowing the system to take preventive measures such as adjusting credit limits or notifying customers in advance, thereby eliminating the need to wait for actual transfer confirmation while maintaining account status accuracy.
Solution Approach 2:
The system implements feedback mechanisms by continuously receiving account transaction data, analyzing it through machine learning models, and using the results to update predictions and adjustments. This closed-loop feedback system ensures that account status information is continuously refined and improved, providing accurate real-time status without requiring slow ACH confirmation processes.
2Productivity
If card issuers float money to customers without real-time account status confirmation, then transaction speed is improved, but overdraft fees and financial penalties increase
Solution Approach 1:
The system takes preliminary action by using machine learning models to predict potential overdraft conditions before transactions occur. By analyzing historical transaction data, account patterns, and real-time transaction flows, the system can forecast when a customer's account balance may become insufficient and take preventive measures such as adjusting credit limits, flagging transactions for review, or notifying customers in advance, thereby preventing overdraft fees while maintaining fast transaction processing.
Solution Approach 2:
The system enables self-service by automatically monitoring account transactions and adjusting credit limits or transaction parameters without requiring manual intervention. The machine learning model continuously analyzes account data and autonomously makes decisions about which transactions to allow, modify, or flag, eliminating the need for manual review while preventing overdraft conditions and associated fees.
3Measurement precision
If machine learning models continuously monitor and update settlement paths, then fee avoidance accuracy is improved, but system complexity increases
Solution Approach 1:
The system achieves universality by implementing a multi-functional machine learning platform that performs multiple tasks: monitoring account transactions, predicting overdraft conditions, analyzing transaction patterns, adjusting credit limits, and optimizing settlement paths. By consolidating these functions into a single integrated system, the patent reduces overall complexity compared to having separate systems for each function, while maintaining high fee prediction accuracy through the unified model's comprehensive analysis capabilities.
Data Source
AI summary
A method for facilitating minimization of fees charged to customers with respect to electronic fund transfers associated with transactions may include receiving, by a facilitation agent, account transaction data associated with transactions initiated with respect to a plurality of customer accounts, employing a machine learning platform to identify fee charges in the account transaction data, employing the machine learning platform to determine a fee profile for the identified fee charges, the fee profile including a potential cause for each of the identified fee charges, employing the machine learning platform to define a settlement path to minimize a likelihood of triggering a fee for a given customer account associated with a transaction based on avoidance of the potential cause for each of the identified fee charges, and updating a settlement model based on the settlement path.