Random Forest Model for Interchange Code Prediction

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Solution Overview

Problem

Current credit card transaction processing systems lack the ability to accurately predict interchange fees in real-time, leading to undesirable monthly billing for merchants and increased latency in processing transactions.

Innovation Solution

A computer-implemented method using a random forest model to predict interchange codes by transforming bank identification numbers and transaction features into probabilistic BIN features, training the model with historical data, and generating predicted interchange codes for current transactions, allowing for timely and accurate calculation of interchange charges.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If interchange fees are calculated using traditional monthly billing methods, then accuracy of fee assessment is improved, but processing latency and cash flow efficiency deteriorate

Engineering Contradiction:

Improvefee assessment accuracyVSAvoidprocessing latency

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary classification of transactions into interchange code categories at the time of transaction processing, before the actual interchange fee calculation. This preliminary action enables real-time fee estimation without waiting for monthly batch processing, thus reducing processing latency while maintaining acceptable accuracy through continuous model refinement using historical data

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements a feedback mechanism where actual interchange fees from monthly billing are compared with predicted fees, and the differences are used to retrain and improve the classification model. This continuous feedback loop enhances measurement precision over time while maintaining real-time processing capabilities

Inventive Principle:

Principle #23Feedback

2Productivity

If real-time prediction of interchange codes is implemented, then processing speed and cash flow management are improved, but prediction accuracy and model complexity increase

Engineering Contradiction:

Improvetransaction processing speedVSAvoidinterchange code prediction accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system uses a dynamic classification model that adapts to changing interchange fee structures and transaction patterns. The model is continuously retrained with historical data to maintain high prediction accuracy while enabling real-time processing. The dynamic nature allows the system to handle various card types, merchant categories, and transaction scenarios with improved precision

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system segments the complex interchange fee structure into distinct code categories based on transaction characteristics such as card type, merchant category, and transaction location. This segmentation simplifies the prediction task while maintaining accuracy by focusing on key discriminative features rather than attempting to predict all possible fee variations

Inventive Principle:

Principle #1Segmentation

3Measurement precision

If complex classification models are used to predict interchange codes, then prediction accuracy is improved, but computational resources and system complexity increase

Engineering Contradiction:

Improveinterchange code prediction accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system introduces a trained classification model as an intermediary between raw transaction data and interchange fee calculation. This intermediary model handles the complexity of mapping diverse transaction characteristics to appropriate interchange codes, thereby improving prediction accuracy while shielding the rest of the system from computational complexity. The model translates complex patterns into simplified code categories that can be processed efficiently

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11587092B2System for dynamic prediction of interchange rates for credit card transaction processing

Publication Date: 2023.02.21 TOAST INC

AI summary

A computer-implemented method for predicting interchange charges includes: retrieving a historical transactions set, where each completed transaction in the set includes transaction features, a bank identification number (BIN), and a corresponding true interchange code; transforming all BINs in the set into a corresponding plurality of BIN features that comprise probabilities; creating a first training set including all transaction features, all pluralities of BIN features, and all true interchange codes associated with the historical transactions set; training a random forest model using the first training set and generating a second training set including rounded BIN features, rounded transaction features, discrete ones of the transaction features, and the true interchange codes; training the random forest model using the second training set to generate a trained random forest model for prediction of the interchange codes; and executing the trained random forest model for new transactions to generate corresponding predicted interchange codes.