Share with your friends


Analytics Magazine

Corporate Profile: 25 years of analytics at Bank of America

March/April 2013

By Russ Labe

In November 1986, the Management Science Group was established at Merrill Lynch. The initial group was comprised of six operations research/management science practitioners who previously worked together at the RCA Operations Research Group, which was founded by Franz Edelman circa 1950. DuWayne Peterson, head of Operations, Systems and Telecommunications at Merrill Lynch in 1986, was the driving force behind the idea to build an analytics team within the brokerage business. He believed that an internal team of management scientists could identify a host of opportunities to improve business performance. Since RCA had recently been acquired by General Electric, the RCA O.R. group was looking for a new challenge as well as a new home. Over the past 25 years, Peterson’s decision paid off with big dividends.

The mission of the group was unique in financial services. There were and still are numerous analysts on Wall Street focused on developing investment strategies, managing risk, supporting trading activity and seeking arbitrage opportunities. Our idea was to focus instead on supporting the general business functions of the organization, to improve efficiency and apply analytics to key business functions such as operations, marketing, pricing and product management.

Much has happened since 1986. Executive leadership changed several times. Financial products and solutions have evolved, grown and declined. Bank of America acquired Merrill Lynch in 2009. The size of the group grew, shrank and grew again. The name of the group changed to Decision Support Modeling. The group migrated to different functional areas of the organization, including technology, marketing, strategic planning and the brokerage line of business. Three different leaders managed the team. H. Newton Garber, the original director, retired in 1990. Garber was followed by Raj Nigam, who led the group until he retired in 2004. Today, the group is managed by Russ Labe, one of the original founding members, and includes 10 professionals with more than 120 years of combined experience at Bank of America and Merrill Lynch. It is part of the marketing organization supporting the client-managed businesses within Bank of America, which includes Merrill Lynch Wealth Management, U.S. Trust and the Bank of America Merrill Lynch institutional businesses.

Throughout all those changes, the group has stayed focused on its mission to improve profitability and assess strategic decisions by providing statistical analysis and mathematical modeling.

A view of the Bank of America Merrill Lynch corporate campus in Hopewell, N.J.
A view of the Bank of America Merrill Lynch corporate campus in Hopewell, N.J.

Application Areas

While the focus of the team’s project work has certainly evolved over time, certain application areas – pricing, client retention, product propensity and revenue forecasting – are consistently important to the business and provide recurring projects.

The group has evaluated numerous pricing situations related to new products and restructuring the pricing of existing products and solutions. Typically these models required gathering huge amounts of transactional data and building historical simulations to evaluate alternate pricing scenarios. The analysis was focused on understanding the detailed impact of price changes on individual clients, financial advisor compensation and firm profitability. In phase one of the analysis, the team assumed client behavior remains the same in order to establish an initial estimate of change impact. In phase two of the analysis the team simulated changes in client behavior based on factors such as client satisfaction, financial advisor loyalty and transactional behavior patterns. Sometimes the resulting analysis led to a decision to not implement any changes. In other cases, the analysis helped launch new products, solutions and services.

Models the team developed to identify clients at risk of leaving Merrill Lynch are used on a regular basis. Alerts are distributed to branch offices each week so they can save some of those relationships. This program has been in place for more than 10 years and is estimated to save $1 billion of client assets annually. The underlying models were developed using a combination of decision trees and logistic regression. A significant refresh and update of the models was recently completed and an enhanced program implementation is in progress. Over the years, the success of this work led to related applications, including development of customized models specifically focused on commercial or small business clients and Merrill Edge clients. Merrill Edge consists of the Merrill Edge Advisory Center (a call center providing investment guidance to clients), as well as self-directed online investing.

The team developed numerous product propensity models to help target the most appropriate financial solutions and services for the most appropriate clients. This has been a highly active area for many years. Originally the team developed customized models for each product and solution of interest. Over the last few years the team developed an automated process, based on collaborative filtering, that allows us to automatically update more than 70 product propensity models and score more than two million clients across all the models on a monthly basis. The models are customized by client segment, resulting in more than 1,000 separate models that are revised each month. These models are used as input to various marketing campaigns and client contact optimization strategies and models.

A related application is referral models, a type of propensity model that addresses a strategic priority for the bank – identify opportunities to support clients across multiple lines of business. Examples include identifying consumer banking clients with a need to manage their investment accounts and finding small business owners with a need to manage their personal investments. The team developed a series of these referral models, which are used to help customer service representatives provide better service to clients and to inform marketing campaigns by selecting the most appropriate messages for each client. Results from these models are embedded in the bank’s Web sites to determine messaging strategies.

Another application area is revenue forecasting models. Recently, this has become an area of heightened interest to meet new regulatory requirements imposed by the Federal Reserve. The models estimate the impact of economic stress scenarios, defined quarterly by regulators, on the bank’s financial performance. The team developed a series of econometric regression models that predict monthly revenue over a two-year horizon based on a combination of macro-economic factors, internal business drivers and seasonality. The models are used each quarter to support the required stress test analysis. In addition, Finance uses the models as input to their ongoing planning process. These models were classified as trade secrets by the bank.

Other examples of the team’s work include financial advisor segmentation, client profitability models, advertising impact modeling and measurement, financial advisor compensation analysis, business strategy impact evaluations, reserve requirement models for debit card reward points and deferred compensation programs, and portfolio optimization. Benefits from these projects through the years have impacted strategic business decisions and contributed hundreds of millions of dollars in bottom line benefits through increased revenue, cost reduction and efficiency improvements.

Members of the Decision Support Modeling team (l-r): Lihua Yang, Mark Goldstein, Fang Liu, Je Oh, Zhaoping Wang, Yanni Papadakis, Vera Helman, Russ Labe and Brian Jiang.
Members of the Decision Support Modeling team (l-r): Lihua Yang, Mark Goldstein, Fang Liu, Je Oh, Zhaoping Wang, Yanni Papadakis, Vera Helman, Russ Labe and Brian Jiang.

Professional Recognition

In addition to internal contributions, the team has received external recognition from INFORMS for the quality and business value of its work. In 1997, the team helped Merrill Lynch win the INFORMS Prize for the effective and widespread use of analytics throughout the organization sustained over 10 years.

In 2001, the team helped Merrill Lynch win the Franz Edelman Prize for the pricing analysis it conducted to help launch Merrill Lynch Unlimited Advantage (MLUA), as well as the legacy ML Direct business, now called Merrill Edge. MLUA was the first financial solution in the brokerage marketplace with true client relationship pricing and attracted $22 billion of incremental assets to the firm during its first two years. ML Direct was the firm’s first effort in Web-based online trading accounts.

In 2004, the team won the Wagner Prize for modeling the liquidity risk of revolving credit lines provided to other companies through Merrill Lynch’s legacy Bank and Trust business. This analysis allowed ML Bank and Trust to free up $4 billion of capital. In 2005, the team helped the ML Treasury group win the Alexander Hamilton Prize associated with the same work around liquidity risk. These awards from objective, professional peers helped raise the profile of the group internally and increased management confidence in the quality and value of the team’s work.


Looking back over the last 25 years, the Decision Support Modeling team at Bank of America has much to celebrate. The team survived many market cycles and reorganizations. The team has a strong history of business impact on the organization, using advanced analytics to support good management practices and business transformation. The group provided contributions on a wide range of business issues, including pricing, client attrition, product propensity, financial advisor segmentation, revenue forecasting, financial advisor compensation, business strategy impact evaluations and portfolio optimization. The team employed a wide range of modeling techniques across different types of projects, including data mining, design of experiments, multi-variate statistics, simulation, and optimization. Looking to the future, the team sees many opportunities to continue applying analytics and modeling at Bank of America and to continue providing added value to the business.

Russ Labe ( is director of the Decision Support Modeling Group, Bank of America Corporation. He is a member of INFORMS Roundtable and a senior member of INFORMS.

Global Wealth & Investment Management is a division of Bank of America Corporation (“BAC”). Merrill Lynch Wealth Management, Merrill Edge™, U.S. Trust and Bank of America Merrill Lynch are affiliated sub-divisions within Global Wealth & Investment Management.

Merrill Lynch Wealth Management makes available products and services offered by Merrill Lynch, Pierce, Fenner & Smith Incorporated (“MLPF&S”) and other subsidiaries of BAC. Merrill Edge™ is the marketing name for two businesses: Merrill Edge Advisory Center, which offers team-based advice and guidance brokerage services; and a self-directed online investing platform.

U.S. Trust, Bank of America Private Wealth Management operates through Bank of America, N.A., and other subsidiaries of BAC.

Bank of America Merrill Lynch is a marketing name for the Retirement & Philanthropic Services businesses of BAC.

Banking products are provided by Bank of America, N.A., and affiliated banks, Members FDIC and wholly owned subsidiaries of BAC.

Investment products are not FDIC insured, are not bank guaranteed and may lose value.

A view of the Bank of America Merrill Lynch corporate campus in Hopewell, N.J.

Members of the Decision Support Modeling team (l-r): Lihua Yang, Mark Goldstein, Fang Liu, Je Oh, Zhaoping Wang, Yanni Papadakis, Vera Helman, Russ Labe and Brian Jiang.

business analytics news and articles


Former INFORMS President Cook named to U.S. Census committee

Tom Cook, a former president of INFORMS, a founding partner of Decision Analytics International and a member of the National Academy of Engineering, was recently named one of five new members of the U.S. Census Bureau’s Census Scientific Advisory Committee (CSAC). The committee meets twice a year to address policy, research and technical issues relating to a full range of Census Bureau programs and activities, including census tests, policies and operations. The CSAC will meet for its fall 2018 meeting at Census Bureau headquarters in Suitland, Md., Sept. 13-14. Read more →

Gartner identifies six barriers to becoming a digital business

As organizations continue to embrace digital transformation, they are finding that digital business is not as simple as buying the latest technology – it requires significant changes to culture and systems. A recent Gartner, Inc. survey found that only a small number of organizations have been able to successfully scale their digital initiatives beyond the experimentation and piloting stages. “The reality is that digital business demands different skills, working practices, organizational models and even cultures,” says Marcus Blosch, research vice president at Gartner. Read more →

Innovation and speculation drive stock market bubble activity

A group of data scientists conducted an in-depth analysis of major innovations and stock market bubbles from 1825 through 2000 and came away with novel takeaways of their own as they found some very distinctive patterns in the occurrence of bubbles over 175 years. The study authors detected bubbles in approximately 73 percent of the innovations they studied, revealing the close relationship between innovation and stock market bubbles. Read more →



INFORMS Annual Meeting
Nov. 4-7, 2018, Phoenix

Winter Simulation Conference
Dec. 9-12, 2018, Gothenburg, Sweden


Applied AI & Machine Learning | Comprehensive
Sept. 10-13, 17-20 and 24-25

Advancing the Analytics-Driven Organization
Sept. 17-20, 12-5 p.m. LIVE Online

The Analytics Clinic: Ensemble Models: Worth the Gains?
Sept. 20, 11 a.m.-12:30 p.m.

Predictive Analytics: Failure to Launch Webinar
Oct. 3, 11 a.m.

Advancing the Analytics-Driven Organization
Oct. 1-4, 12 p.m.-5 p.m.

Applied AI & Machine Learning | Comprehensive
Oct. 15-19, Washington, D.C.

Making Data Science Pay
Oct. 29 -30, 12 p.m.-5 p.m.


CAP® Exam computer-based testing sites are available in 700 locations worldwide. Take the exam close to home and on your schedule:

For more information, go to