bank of america

INDIVIDUAL ANALYSIS PAPER INSTRUCTIONS:
Choose one of the papers displayed. Follow the instructions below to analyze what has been done with their warehouse.

Report Format for Individual Papers
Your assignment should be at least 3 pages, but no more than 5 pages about the chosen paper. The important thing in developing your paper is to be “thorough” and “accurate”. You should read the selected paper several times before you start writing, and take some time off in between the reading and the reporting so you let some thought and incubation take place. The questions listed below do not necessarily have EXACT answers; however that doesn’t mean that any sloppy thinking will do. I expect to see original thought and in some cases, your personal opinion.
Begin with the background of the company being written about. Use the internet to research any information not available in the paper. Include information on their products, their organization structure, their financial/stock performance and their competitors.
Include all of the following if possible – not all items can be answered for all papers. Include a paragraph header for every item below. If your paper cannot answer a topic, Please put the header, followed by NOT APPLICABLE in parentheses. I do not expect to see NOT APPLICABLE more than once as all questions can be answered.
• Key business tasks supported
• Key business users supported
• Describe the general architecture
• Products utilized
• Business decisions driving the decision for a data warehouse
• Key business objectives
• Expected benefits
• Training/education was required
• Outside services used during the implementation
• From YOUR perspective, was the project a success?
• If the system went down for three hours, what kind of ‘pain’ would the company feel?
• Major success factors
• Major risk factors
• What were the lessons learned?
• What enhancements do YOU see for this system?
• What other materials would you have liked to have in order to answer more effectively?

 
CUSTOMER STORY
Bank of America is one of the world’s largest financial institutions, serving individual consumers, small- and middle-market businesses and large corporations with a full range of banking, investing, asset management and other financial and risk management
products and services.
With approximately 59 million consumer and small business relationships, 6,000 retail banking offices and more than 18,000 ATMs, Bank of America is among the world’s leading wealth management companies and is a global leader in corporate and investment banking and trading across a broad range of asset classes.
The Corporate Investments Group (CIG) manages Bank of America’s available-for-sale portfolio and is responsible for modeling and calculating the probability of default (PD) on the 9.5 million mortgages
it services. In addition, the group calculates the market value, prepayment speeds and sensitivity to changes in interest rates and hedges these risks for the $19 billion mortgage-service-rights asset. Recently, CIG began assisting with the task of forecasting loan losses for the bank’s credit card portfolio.
The need for speed
CIG had been using analytics from SAS for credit-risk modeling for many years, but with the addition of the credit-card loss forecasting responsibility, it was forced to reassess its use of an internal shared-services environment to run its modeling and calculation processes. Doing so would help reduce processing time, increase access and availability of resources for ad hoc analysis, while ensuring business continuity for this mission-critical function of the bank’s business.
“We needed a solution that addressed today’s business problems, as well as a solution with the flexibility for any future business requirements,” says Russell Condrich, Senior Vice President, Corporate Investment Group. “Processing
large, multi-terabyte data sets in a quick, efficient manner was a key requirement for us and SAS performed flawlessly. Without SAS, processing times would be longer, hedging decisions would be delayed and, ultimately, the bank would be behind
the market.”
SAS® and IBM show results
To meet its performance requirements, the group moved its processing to a dedicated platform comprising SAS® for Enterprise Risk Management on SAS Grid Computing, SAS Scalable Performance Data Server on a 112-core IBM BladeCenter® grid and IBM’s XIV® Storage System. The initiative has already produced considerable results, such as reducing the bank’s probability of default calculation time from 96 hours to just four. Processing time for ad hoc jobs has been reduced by 90 percent and, according to the CIG, they are processing at three times the speed of the previous environment.
The platform pulls data from eight systems of record (SOR), amounting to hundreds of millions of records, or 30
Modeling portfolio credit risk is a fundamental function in banking today. Loan products, such as lines of credit, mortgages and credit cards, entail a high degree of risk for banks, and on a large scale, especially in turbulent economic periods – defaults produce difficult situations and huge implications for both the lender and the borrower. Banks regularly employ credit-risk management processes to monitor and
assess credit portfolios, to make certain estimates, and to understand their risk position and value of assets at any given time. In today’s complex and ever-changing financial system, powerful, rigorous and accurate credit-risk management processes and technology play a critical role in mitigating a lending institution’s exposure.
Bank of America avoids gridlock in
credit-risk scoring, forecasts using SAS®
¦ Industry
Financial Services
¦ Business Issue
The Corporate Investments Group needed to reduce processing time for credit-risk modeling, scoring and loss forecasting and increase ad hoc analysis time, while ensuring business continuity and guaranteed uptime for these mission-critical functions.
¦ Solution
SAS® for Enterprise Risk Management
on SAS® Grid Computing
and SAS® Scalable Performance Data Server® on a 112-core IBM BladeCenter® grid and IBM’s XIV® Storage System.
¦ Benefits
The solution has reduced the banking
group’s probability of loan default calculation time from 96 hours to just four and reduced its scoring routine of 400,000 loans from three hours to 10 minutes. Processing time for any given project has been reduced by 90 percent, is three times faster, yields timely decisions around
defaults, helps minimizes losses and can handle new growth opportunities for the bank’s loan portfolio.
¦ Partners
IBM
SAS Institute Inc. World Headquarters +1 919 677 8000
To contact your local SAS office, please visit: www.sas.com/offices
SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA
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The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software.
“Without SAS, processing times would be longer, hedging decisions would be delayed and, ultimately, the bank would be behind the market.”
Russell Condrich
Senior Vice President, Corporate Investment Group
terabytes of source data, and allows the SAS environment to consume 3.9GB of I/O throughput per second from IBM’s XIV storage environment. Approximately 30 users now have unfettered access to the environment, as opposed to the shared services environment of the past, in which user time was competitive
and response times varied dramatically
due to the high number of jobs being executed.
‘Unparalleled’ performance
“We now have an environment that provides users with a robust platform on which to schedule and prioritize jobs, based on duration or computational requirements, so that ad hoc usage is not competing with scheduled work,” says Stephen Lange, Managing Director, Corporate Investments Group. “This advanced grid platform is giving us unparalleled performance. SAS is indispensable for its unique way of handling large data sets.”
As an example, Lange adds, “we have to score a particular portfolio of 400,000 loans with our suite of models, using multiple scenarios, and we need to run it over the 360 months of the mortgages’ life. That process used to take three hours; now it takes 10 minutes because of the parallelization capabilities of the grid. The ability to go from three hours to 10 minutes on a job demonstrates a
tremendous increase in our ability to deliver information and make decisions.
“The bank has a strong desire to enable loss forecasting as accurately and quickly as possible, right up to the senior executive layers of the organization,” says Lange. “The only way we can do that is to have sufficient IT resources to score loans and appropriately assess risks. The partnership between SAS, IBM and our internal technology group has provided a platform for us to demonstrate risk management leadership.”
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bank of america

• Prepare company analysis as to their investment strategy and how they view risk in their investments internationally.
• In your analysis use the concepts from the chapters to discuss the strategy
• Your bank financial analysis using all the ratios, industry averages, perhaps main competitor.

bank of america

• Prepare company analysis as to their investment strategy and how they view risk in their investments internationally.
• In your analysis use the concepts from the chapters to discuss the strategy
• Your bank financial analysis using all the ratios, industry averages, perhaps main competitor.