Predictive Payment Model for Delinquent Account Balance Forecasting
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Solution Overview
Problem
Banks face challenges in determining optimal payment amounts for delinquent accounts, as continuous withdrawals can lead to overdrafts and penalties, and existing methods lack accuracy in predicting future account balances.
Innovation Solution
A predictive model using historical bank balance data and Autoregressive Integrated Moving Average (ARIMA) models to forecast future average daily balances, allowing for variable and accurate determination of payment amounts, including credit line extensions and delinquent account management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous withdrawals are made from delinquent accounts, then the account is paid up, but the account holder may be overdrafted and left with no money
Solution Approach 1:
The system performs preliminary forecasting of future account balances before executing withdrawals. By predicting the account balance at the time of fund transfer using historical data and forecasting models, the system ensures that withdrawals will not cause overdrafts, thus eliminating the harmful effect while maintaining payment reliability
Solution Approach 2:
The system uses historical balance data as feedback to continuously improve forecasting accuracy. The forecasting model incorporates past balance information to predict future balances, creating a feedback loop that adjusts withdrawal amounts to prevent overdrafts while ensuring account repayment
2Ease of operation
If fixed payment amounts are used, then the payment process is simple, but the accuracy of determining optimal payment amounts is insufficient
Solution Approach 1:
The system transitions from static fixed payment amounts to dynamic variable payment amounts. The payment amount is determined based on forecasted future balances, making it adaptable to each account's specific financial situation and cash flow patterns, thereby improving accuracy while maintaining operational simplicity through automation
3Measurement precision
If predictive models are implemented, then future balance prediction accuracy is improved, but the system complexity increases
Solution Approach 1:
The system performs self-service by automatically collecting historical balance data, executing forecasting calculations, and determining optimal payment amounts without manual intervention. This automation handles the complexity internally while presenting a simple interface to users, effectively managing system complexity through self-service automation
Data Source
AI summary
An example operation may include one or more of determining, via a first server, that an action is to be performed on first data associated with a user, wherein the first data is errant data, accessing a stored value related to second data associated with the user at a second server via the first server, retrieving additional data from the second data, dynamically generating a plurality of different models for predicting a future position of the user data based on the additional data, executing the plurality of different models to generate a plurality of solutions to a predefined operation, selecting a model from among the plurality of different models based on the plurality of solutions generated by the executed plurality of different models, predicting a portion of the future position of the user data accessed from the second server to transfer at a future date via execution of the selected model using the retrieved additional data and transferring the predicted portion of the future position of the user data from the stored value to the second user data on the future date.