公开(公告)号：GB2473117A

公开(公告)日：2011-03-02

申请号：GB201014075

申请日：2010-08-23

Applicant: BANK OF AMERICA

Inventor： SUDJIANTO AGUS ,  CHORBA MICHAEL ,  HUDSON DANIEL

IPC: G06Q10/00 ,  G06Q40/00

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. The simulation strategy may involve applying an initial simulation to a statistical cluster and then generating multiple simulation holdout runs by changing an input to the simulation. 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.

公开(公告)号：GB2465518A

公开(公告)日：2010-05-26

申请号：GB201004105

申请日：2008-08-29

Applicant: BANK OF AMERICA

Inventor： BREAULT TIMOTHY J ,  BRUNS ULRICH A ,  DELMONICO JOHN ,  ENNIS SHELLY X ,  HE RUILONG ,  JONES GLENN B ,  LIU WEICHENG ,  MARINO ELAINE C ,  PINTO ARUN R ,  STEACH MEGHAN A ,  SUDJIANTO AGUS ,  YERI NAVEEN G ,  ZHANG BENHONG ,  ZHANG ZHE ,  NOBILI TONY ,  WANG SHUCHUN ,  WANG HUNGJEN ,  ZHANG AIJUN

IPC: G06Q30/00

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.

公开(公告)号：CA2697852A1

公开(公告)日：2009-03-05

申请号：CA2697852

申请日：2008-08-29

Applicant: BANK OF AMERICA

Inventor： BREAULT TIMOTHY J ,  BRUNS ULRICH A ,  DELMONICO JOHN ,  ENNIS SHELLY X ,  HE RUILONG ,  JONES GLENN B ,  LIU WEICHENG ,  MARINO ELAINE C ,  PINTO ARUN R ,  STEACH MEGHAN A ,  SUDJIANTO AGUS ,  YERI NAVEEN G ,  ZHANG BENHONG ,  ZHANG ZHE ,  NOBILI TONY ,  WANG SHUCHUN ,  WANG HUNGJEN ,  ZHANG AIJUN

IPC: G06Q40/00

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.

公开(公告)号：CA2713597A1

公开(公告)日：2011-02-25

申请号：CA2713597

申请日：2010-08-20

Applicant: BANK OF AMERICA

Inventor： SUDJIANTO AGUS ,  CHORBA MICHAEL ,  HUDSON DANIEL ,  SETIAWAN SANDI ,  SIKORA JOCELYN ,  SINGHAL HARSH ,  VUPPU KIRAN ,  MIHAYLOV KALOYAN ,  CHEN JIE ,  BREAULT TIMOTHY J ,  PINTO ARUN R ,  YERI NAVEEN G ,  ZHANG BENHONG ,  ZHANG ZHE ,  NOBILI TONY ,  WANG HUNGIEN ,  ZHANG AIJUN

IPC: G06Q10/00 ,  G06Q40/00

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.

公开(公告)号：GB2471317A

公开(公告)日：2010-12-29

申请号：GB0911028

申请日：2009-06-25

Applicant: BANK OF AMERICA

Inventor： PORTS PRESTON W ,  GHOSH DEBASHIS ,  LIU WELCHENG ,  SUDJIANTO AGUS ,  CHEN JIE ,  ALLISON THAYER ,  JOFFE DAVID ,  AMIN MACK ,  PAWAR SAMIR ,  QUINN MATT

IPC: G06Q40/00

Abstract: A method for providing decision support systems using customer clustering comprises obtaining customer transaction data (e.g. internal-external customer transaction data, credit card information, customer funds ( income)), and categorising it. The categorised customer transaction data is analysed to identify patterns among the data. The identified patterns are used to isolate a selected number of behavioural factors. The customers are grouped into population segments based on the behavioural factors. Any non-linear transaction data may be analysed using a Hidden Markov method. The behavioural segments may be visually presented in a graph (Fig. 3). The decision support system may be used to provide more directed and accurate offers to customers thus improving the customer experience.

公开(公告)号：WO2009029745A3

公开(公告)日：2009-12-30

申请号：PCT/US2008074723

申请日：2008-08-29

Applicant: BANK OF AMERICA ,  BREAULT TIMOTHY J ,  BRUNS ULRICH A ,  DELMONICO JOHN ,  ENNIS SHELLY X ,  HE RUILONG ,  JONES GLENN B ,  LIU WEICHENG ,  MARINO ELAINE C ,  PINTO ARUN R ,  STEACH MEGHAN A ,  SUDJIANTO AGUS ,  YERI NAVEEN G ,  ZHANG BENHONG ,  ZHANG ZHE ,  NOBILI TONY ,  WANG SHUCHUN ,  WANG HUNGJEN ,  ZHANG AIJUN

Inventor： BREAULT TIMOTHY J ,  BRUNS ULRICH A ,  DELMONICO JOHN ,  ENNIS SHELLY X ,  HE RUILONG ,  JONES GLENN B ,  LIU WEICHENG ,  MARINO ELAINE C ,  PINTO ARUN R ,  STEACH MEGHAN A ,  SUDJIANTO AGUS ,  YERI NAVEEN G ,  ZHANG BENHONG ,  ZHANG ZHE ,  NOBILI TONY ,  WANG SHUCHUN ,  WANG HUNGJEN ,  ZHANG AIJUN

IPC: G06E1/00

CPC classification number: G06Q30/02 ,  G06Q40/00 ,  G06Q40/025 ,  G06Q40/06

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.

Abstract translation: 描述了消费者和小企业的数据驱动和前瞻性风险和奖励食欲方法。 该方法包括客户细分，以便在收入和损失特征方面创建同质资产池，前瞻性模拟以预测收益和损失的预期值和波动率，以及投资组合的风险和报酬优化。 用于建模收入和损失的一种方法是广义加和效应分解模型，以适应历史数据。 基于该模型，执行分割程序，其允许创建具有类似收入和损失特征的客户群体。 开发了模型的估计程序，并开发了一种预测和模拟收入和损失波动性的模拟策略。 在各种经济情景下为当前投资组合创建有效的边际风险曲线（例如回报波动性）和报酬（例如预期收益）。