Bank of America Corporation

William Anthony Nobili

David Neil Joffe

Sandi Setiawan

David Joa

Timothy J Bendel

Mark V Krein

Arun Pinto

Agus Sudjianto

Timothy John Breault

Debashis Ghosh

Kurt Newman

Naveen Gururaja Yeri

Sudeshna Banerjee

Jie Chen

Harsh Singhal

Aijun Zhang

Benhong Zhang

Zhe Zhang

Kaloyan Mihaylov

Marc Douglas Halsted

Daniel Hudson

Jocelyn Sikora

Kiran Vuppu

Hungien Wang

Michael Chorba

Fernando Cela Diaz

Meghan Alita Steach

Jeffrey Roger Walls

Elaine C Marino

Michael J Leary

Michael S Bonn

Ulrich A Bruns

John Delmonico

Shelly X Ennis

Ruilong He

Glenn B Jones

WeiCheng Liu

Shuchun Wang

Hungjen Wang

Brian David King

Keith Carlascio

Scott D'Allaird

Diana L Imrie

Andrea Renee Turner

Bradford Timothy Winkelman

Computer-implemented methods for identifying or assessing any type of risk and/or opportunity that may arise can include either, alone or in combination, band pass filtering, principal component analysis, random matrix theory analysis, synchronization analysis, and early-warning detection. Each technique can also be viewed as a process that takes a set of inputs and converts it to a set of outputs. These outputs can be used as inputs for a subsequent process or the outputs may be directly actionable for formulating certain economic predictions to make certain decisions.

Predicting economic conditions

Computer-implemented methods for identifying or assessing any type of risk and/or opportunity that may arise can include either, alone or in combination, band pass filtering, principal component analysis, random matrix theory analysis, synchronization analysis, and early-warning detection. With one approach, principal component analysis is applied to a times series. Resulting normalized eigenvector content is sorted and distances between the normalized eigenvector content are determined, where the dispersion level of the distances is indicative of an economic instability of an economic system. Each technique can also be viewed as a process that takes a set of inputs and converts it to a set of outputs. These outputs can be used as inputs for a subsequent process or the outputs may be directly actionable for formulating certain economic predictions to make certain decisions.

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

According to one embodiment of the present invention, a method comprises receiving a request to aggregate data associated with a customer. The method determines a first identifier associated with the customer that identifies the customer and a second identifier that identifies a household associated with the customer. Data associated with the second identifier is aggregated and communicated in response to the request.

Customer exposure view and income statements (cevis)

Aspects of the invention provide for the use of transactional data in determining credit and product offerings. Further aspects of the invention provide for generating financial statements and indices using transactional data for use by users and/or financial institutions. The transactional based financial statements and indices may be used for making underwriting and financial planning decisions.

Calculating credit worthiness using transactional data

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.

The tool of the invention is a data gathering and analytical process that uses internal customer data and external CRA data to build a consumer credit market data base and data structure. The internal customer data is classified according to identified Customer Attributes, the CRA data is also classified according to identified Consumer Attributes and the combined data is further classified according to additional Value Added Attributes. The resulting data structure is organized at the individual level such that each individual has associated therewith values for each of the Customer Attributes, Consumer Attributes and Value Added Attributes. The resulting analysis can be used to set institutional strategies and/or to make predictive decisions on individual borrower credit requests.

Method and apparatus for obtaining, organizing, and analyzing multi-source data

Growing community of inventors

Charlotte, NC, United States of America

William Anthony Nobili