Risk and reward assessment mechanism

US7765139B2 · kind B2 · utility

Assignee

• Bank of America Corporation · US

Inventors

• Timothy J. Breault · Charlotte, US
• Ulrich A. Bruns · Rock Hill, US
• John Delmonico · Wakefield, US
• Shelly X. Ennis · Matthews, US
• Ruilong He · Charlotte, US
• Glenn B. Jones · Harrisburg, US
• Weicheng Liu · Weddington, US
• Elaine C. Marino · Sterling, US
• Arun R. Pinto · Charlotte, US
• Meghan Alita Steach · Charlotte, US
• Agus Sudjianto · Charlotte, US
• Naveen Gururaja Yeri · Kanchinakote, IN
• Benhong Zhang · Charlotte, US
• Zhe Zhang · Sunnyvale, US
• Tony Nobili · Charlotte, US
• Shuchun Wang · Shandong, CN
• Hungjen Wang · Charlotte, US
• Aijun Zhang · Geneva, US

Key dates

• Filing date: Aug 30, 2007
• Grant date: Jul 27, 2010
• Expiry date: Jul 25, 2028

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q40/06
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

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.