Patents by Inventor Tony Nobili
Tony Nobili has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 8577776Abstract: 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.Type: GrantFiled: September 14, 2012Date of Patent: November 5, 2013Assignee: Bank of America CorporationInventors: Agus Sudjianto, Michael Chorba, Daniel Hudson, Sandi Setiawan, Jocelyn Sikora, Harsh Singhal, Kiran Vuppu, Kaloyan Mihaylov, Jie Chen, Timothy J. Breault, Arun R. Pinto, Naveen G. Yeri, Benhong Zhang, Zhe Zhang, Tony Nobili, Hungien Wang, Aijun Zhang
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Publication number: 20130073481Abstract: 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.Type: ApplicationFiled: September 14, 2012Publication date: March 21, 2013Applicant: BANK OF AMERICA CORPORATIONInventors: Agus Sudjianto, Michael Chorba, Daniel Hudson, Sandi Setiawa, Jocelyn Sikora, Harsh Singhal, Kiran Vuppo, Kaloyan Mihaylov, Jie Chen, Timothy J. Breault, Arun R. Pinto, Naveen G. Yeri, Benhong Zhang, Zhe Zhang, Tony Nobili, Hungien Wang, Aijun Zhang
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Patent number: 8326723Abstract: 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.Type: GrantFiled: August 25, 2009Date of Patent: December 4, 2012Assignee: Bank of America CorporationInventors: Agus Sudjianto, Michael Chorba, Daniel Hudson, Sandi Setiawan, Jocelyn Sikora, Harsh Singhal, Kiran Vuppu, Kaloyan Mihaylov, Jie Chen, Timothy J. Breault, Arun R. Pinto, Naveen G. Yeri, Benhong Zhang, Zhe Zhang, Tony Nobili, Hungien Wang, Aijun Zhang
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Publication number: 20100293107Abstract: 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.Type: ApplicationFiled: August 25, 2009Publication date: November 18, 2010Applicant: Bank of America CorporationInventors: Agus Sudjianto, Michael Chorba, Daniel Hudson, Sandi Setiawan, Jocelyn Sikora, Harsh Singhal, Kiran Vuppu, Kaloyan Mihaylov, Jie Chen, Timothy J. Breault, Arun R. Pinto, Naveen G. Yeri, Benhong Zhang, Zhe Zhang, Tony Nobili, Hungien Wang, Aijun Zhang
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Patent number: 7765139Abstract: 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.Type: GrantFiled: August 30, 2007Date of Patent: July 27, 2010Assignee: Bank of America CorporationInventors: Timothy J. Breault, Ulrich A. Bruns, John Delmonico, Shelly X. Ennis, Ruilong He, Glenn B. Jones, WeiCheng Liu, Elaine C. Marino, Arun R. Pinto, Meghan A. Steach, Agus Sudjianto, Naveen G. Yeri, Benhong Zhang, Zhe Zhang, Tony Nobili, Shuchun Wang, Hungjen Wang, Aijun Zhang
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Publication number: 20090063361Abstract: 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.Type: ApplicationFiled: August 30, 2007Publication date: March 5, 2009Applicant: BANK OF AMERICA CORPORATIONInventors: Timothy J. Breault, Ulrich A. Bruns, John Delmonico, Shelly X. Ennis, Ruilong He, Glenn B. Jones, WeiCheng Liu, Elaine C. Marino, Arun R. Pinto, Meghan A. Steach, Agus Sudjianto, Naveen G. Yeri, Benhong Zhang, Zhe Zhang, Tony Nobili, Shuchun Wang, Hungjen Wang, Aijun Zhang