Financial Forecasting and Analysis for Low-Wage Workers
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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 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 transactions data to provide 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 the prediction task. To uncover recurring transactions, we use a heuristic approach that capitalizes on transaction descriptions. We also propose a category-wise spending analyzer by grouping users through hierarchical clustering. 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 currently connected to the product as well as future potential users.
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