Insufficient Funds Predictor Using Boosted Decision Trees
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Solution Overview
Problem
Existing methods for predicting insufficient funds fees in banking accounts are inaccurate due to reliance on account balances alone, failing to account for users with low balances who rarely incur fees and those with sufficient funds across multiple accounts.
Innovation Solution
A system using a boosted decision tree model trained on historical financial data, including user-related and transaction data, to predict the probability of an insufficient funds fee, incorporating derived variables and natural language processing to standardize fee indications across different financial institutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If account balance is used as the primary predictor of insufficient funds fee likelihood, then the prediction system is simple to implement, but the prediction accuracy deteriorates because it fails to account for users with low balances who rarely incur fees and users with sufficient funds across multiple accounts
Solution Approach 1:
The patent segments the prediction problem by dividing users into different groups based on their financial behavior patterns (e.g., users with consistently low balances who never incur fees vs. users with sufficient funds who occasionally do). This segmentation allows the system to apply different prediction models and features to different user segments, improving overall accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent transitions from a single-dimensional prediction approach (account balance only) to a multi-dimensional approach by incorporating multiple features including transaction history, user behavior patterns, account relationships, and temporal patterns. This dimensional expansion enables the system to capture complex financial behaviors that single-balance metrics cannot detect, thereby improving prediction accuracy without proportionally increasing system complexity.
2Measurement precision
If a comprehensive multi-feature prediction model is used to improve prediction accuracy, then prediction accuracy improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary data processing and feature engineering during the training phase, where historical data is pre-processed, cleaned, transformed, and organized into meaningful feature sets. This preliminary action prepares the data in advance, reducing the computational burden during actual prediction operations. The system pre-calculates and stores feature transformations, allowing rapid inference without reprocessing the entire dataset, thus managing system complexity while maintaining high accuracy.
Solution Approach 2:
The patent extracts and isolates the most predictive features from the comprehensive dataset through feature selection and importance ranking. By identifying and separating the critical features (such as transaction patterns, balance history, and user behavior indicators) from less relevant data, the system reduces the complexity of the prediction model while retaining the accuracy benefits of multi-feature analysis. This extraction process allows the system to focus computational resources on the most impactful variables.
3Productivity
If the system predicts insufficient funds fees for users with low balances, then more users receive alerts, but the number of false positive alerts increases reducing user trust
Solution Approach 1:
The patent incorporates feedback mechanisms that continuously monitor user responses to alerts and actual insufficient funds events. The system learns from user feedback and adjusts its prediction models accordingly, refining the thresholds and criteria for generating alerts. This feedback loop enables the system to distinguish between users who will actually incur fees and those who won't, reducing false positives while maintaining high alert productivity. The feedback-driven optimization ensures alert accuracy improves over time.
Solution Approach 2:
The patent dynamically adjusts prediction parameters and alert thresholds based on user-specific characteristics and changing financial behaviors. Instead of using fixed thresholds, the system adapts parameters such as balance thresholds, transaction frequency limits, and time-based criteria to match individual user patterns. This parameter flexibility allows the system to generate appropriate alerts for users with low balances who are actually at risk while avoiding false alerts for users with low balances who consistently manage their finances successfully, thereby improving reliability without sacrificing productivity.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for improving a prediction of whether a non-sufficient funds fee will be incurred by a user utilizing machine learning techniques. For example, a predictive model may be trained using machine learning techniques based on historical data and derived data for a plurality of users. The predictive model may then be used to predict a probability of a particular user incurring an insufficient funds fee. The probability of the particular user may be used to generate an alert and suggestion to be presented to the particular user to avoid incurring the insufficient funds fee.