Bank of America Corporation

Wells Fargo Bank, N.a.

Aijun Zhang

Agus Sudjianto

Naveen Gururaja Yeri

William Anthony Nobili

Arun Pinto

Timothy John Breault

Benhong Zhang

Zhe Zhang

Jie Chen

Harsh Singhal

Sandi Setiawan

Kaloyan Mihaylov

Daniel Hudson

Jocelyn Sikora

Kiran Vuppu

Michael Chorba

Hungien Wang

Meghan Alita Steach

Elaine C Marino

Ulrich A Bruns

John Delmonico

Shelly X Ennis

Ruilong He

Glenn B Jones

WeiCheng Liu

Shuchun Wang

Hungjen Wang

Zebin Yang

Systems, apparatuses, methods, and computer program products are disclosed for generating a single-index model (SIM) tree. An example method includes receiving a data set and a maximum tree depth. The example method further includes screening a set of variables from the data set to form split variables. The method may include, while maximum tree depth has not been reached, (i) generating a fast SIM estimation for nodes of a tree level, (ii) for each node, selecting a split point and split variable based on the fast SIM estimation, (iii) based on the selected split points and split variables, generating nodes for a next tree level, each including a subset of data, and (iv) repeating steps (i), (ii), and (iii). The method may include fitting a SIM for each leaf node at maximum tree depth based on a subset of the data set represented by the leaf node.

Systems and methods for generating a single-index model tree

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

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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Ann Arbor, MI, United States of America

Aijun Zhang