  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.
