Bank of America Corporation

Timothy John Breault

Agus Sudjianto

William Anthony Nobili

Harsh Singhal

Naveen Gururaja Yeri

Jie Chen

Arun Pinto

Sandi Setiawan

Aijun Zhang

Zhe Zhang

Kaloyan Mihaylov

Benhong Zhang

Hungien Wang

Michael Chorba

Daniel Hudson

Jocelyn Sikora

Kiran Vuppu

Fernando Cela Diaz

Meghan Alita Steach

Elaine C Marino

Andrea Renee Turner

Bradford Timothy Winkelman

Ulrich A Bruns

John Delmonico

Shelly X Ennis

Magali F Van Belle

Ruilong He

Glenn B Jones

WeiCheng Liu

Shuchun Wang

Wei Wei

Hungjen Wang

Methods, computer readable media, and apparatuses for evaluating models using forecast error attribution are presented. According to one or more aspects, one or more input values corresponding to one or more input variables may be forecast. One or more results of a modeling function may be calculated using the one or more forecasted input values. Thereafter, actual performance data corresponding to the modeling function may be received. One or more holdout values for the modeling function may be calculated using the actual performance data. Subsequently, a graph that includes the one or more results of the modeling function, the actual performance data, and the one or more holdout values for the modeling function may be plotted. In some arrangements, the one or more holdout values for the modeling function may be indicative of one or more assumption errors made with respect to the one or more forecasted input values.

Evaluating models using forecast error attribution

A data driven and forward looking risk and reward appetite methodology for consumer and small business is described. The methodology includes account level historical data collection for customers associated with accounts as part of a portfolio. The account level historical data is segmented into groups of customers with similar revenues and loss characteristics. Segmented data is decomposed into seasoning, vintage, and cycle effects. Statistical clusters are formed based upon the data and effects. A simulation is applied to the statistical clusters and prediction data is generated. A simulation strategy to forecast and simulate revenue and loss volatility is developed. Efficient frontier curves of risk (e.g., return volatility) and reward (e.g., expected return) are created for the current portfolio under various economic scenarios.

Risk and reward assessment mechanism

Embodiments of the present invention relate to methods and apparatuses for determining leading indicators and/or for modeling one or more time series. For example, in some embodiments, a method is provided that includes: (a) receiving first data indicating the value of a total income amount for a plurality of consumers over a period of time; (b) receiving second data indicating the value of a total debt amount for a plurality of consumers over a period of time; (c) selecting a consumer leverage time series that compares the total income amount to the total debt amount over a period of time; (d) modeling the consumer leverage time series based at least partially on the first and second data; (e) determining, using a processor, the value of the cycle component for a particular time; and (f) outputting an indication of the value of the cycle component for the particular time.

Determining leading indicators

A data driven and forward looking risk and reward appetite methodology for consumer and small business is described. The methodology includes customer segmentation to create pools of homogeneous assets in terms of revenue and loss characteristics, forward looking simulation to forecast expected values and volatilities of revenue and loss, and risk and reward optimization of the portfolio. One methodology used for modeling revenue and loss is a generalized additive effect decomposition model to fit historical data. Based on the model, a segmentation procedure is performed, which allows for creation of groups of customers with similar revenue and loss characteristics. An estimation procedure for the model is developed and a simulation strategy to forecast and simulate revenue and loss volatility is developed. Efficient frontier curves of risk (e.g., return volatility) and reward (e.g., expected return) are created for the current portfolio under various economic scenarios.

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Huntersville, NC, United States of America

Timothy John Breault