Financial Forecasting and Analysis for Low-Wage Workers

TL;DR

This paper introduces a data-driven system to enhance personalized financial advice for low-wage workers by accurately predicting balances and identifying transactions, aiming to improve financial stability support.

Contribution

It presents a hybrid prediction method and heuristic transaction analysis tailored for low-income users, outperforming existing approaches in real financial data.

Findings

Higher prediction accuracy than conventional methods

Effective extraction of recurring transactions

Improved financial planning for low-wage workers

Abstract

Despite the plethora of financial services and products on the market nowadays, there is a lack of such services and products designed especially for the low-wage population. Approximately 30% of the U.S. working population engage in low-wage work, and many of them lead a paycheck-to-paycheck lifestyle. Financial planning advice needs to explicitly address their financial instability. We propose a system of data mining techniques on small-scale transactions data to improve automatic and personalized financial planning advice to low-wage workers. We propose robust methods for accurate prediction of bank account balances and automatic extraction of recurring transactions and unexpected large expenses. We formulate a hybrid method consisting of historical data averaging and a regularized regression framework for prediction. To uncover recurring transactions, we use a heuristic approach that capitalizes on transaction descriptions. Our methods achieve higher performance compared to conventional approaches and state-of-the-art predictive methods in real financial transactions data. In collaboration with Neighborhood Trust Financial Partners, the proposed methods will upgrade the functionalities in WageGoal, Neighborhood Trust Financial Partners' web-based application that provides budgeting and cash flow management services to a user base comprising mostly low-income individuals. The proposed methods will therefore have a direct impact on the individuals who are or will be connected to the product.