Introduction – Company Background
Based in San Francisco, Bank of America is the third largest banking institution in the U.S. One of world’s largest financial institutions, the bank serves individual customers, large corporations and small and medium enterprises including banking, investment, financial and risk management and asset management services. With sixteen major operating subsidiaries, twenty one holding companies, around six thousand retail banking offices and eighteen thousand ATMs, the bank serves a customer base of around fifty nine million (Avraham et al, 2012). They have award-winning internet banking services and millions of mobile banking customers and held a net income of 4.8 billion dollars for the calendar year 2014 (Bank of America, 2015). The Bank of America Corporation stocks are listed in the New York Stock Exchange (NYSE: BAC). The bank serves the customers through operations in over forty countries offering industry leading to support to small businesses and trade across a broad range of asset classes serving governments, institutions and individuals around the world.
Key Business Tasks Supported
Bank of America involves three key capabilities operationally, IT services, Business Process and Knowledge Services and throughout America encompass technology and banking operations and support processes including credit cards, savings accounts, loans, investment management, wealth management, tax planning, retirement services, trust and concentrated stock strategies.
Key Business Users Supported
Bank of America provides individuals, businesses, and investors the financial help and services required to fulfill their goals. Their key business users include small businesses, fast growing companies and large MNCs and they help them in services including capital raising and advisory. They are among the thirteen banks pledged to the small businesses administration and the White House to increase small business lending.
The General Architecture
The Bank exhibits a divisional corporate hierarchy where large sections of the corporation are segregated into semi-autonomous bodies. The groups are assigned to various field of service headed by an executive officer or Vice-President (Theofficialboard, 2015). It helps the bank perform well in all sections because each group is focused on a single service resulting in an increase in productivity.
Business Decisions Driving the Decision for a Data Warehouse
The decision for a data warehouse was made in order to address new business initiatives requiring enterprise information analysis. The Corporate Investment Group that manages the bank’s available-for-sale portfolio and is also responsible for calculating and modeling the PD (Probability of Default) and assessment of loan losses and other services, in order to reduce the processing time for credit-risk modeling, loss forecasting, scoring, and ad hoc analysis time decided to have a data warehouse.
Key Business Objectives
The key objective was efficient forecasting to provide solution addressing the problems the business faced today while ensuring it was flexible for future use. Another key objective was reducing process time for credit-risk modeling which needed processing of large, multi-terabyte data sets rapidly and efficiently.
Efficient forecasting, reduction in the probability of loan defaults in the bank by reducing the calculating time, reduction in processing time for projects, minimizing losses and efficient handling of new business portfolios.
A computer Engineer or graduate or post-graduate from related field with training in data modeling and obtaining data for large enterprise data warehouse, data warehouses SDLC training, Meta data management knowledge, ORACLE SQL, Ab initio tools and BPMN (Business Process Modeling Notation). A Teradata certified professional, one of the most highly recognized certifications in the industry helps in improving overall productivity of a Big Data warehousing team.
Outside Services used during Implementation
In order to meet the benefits expected, Bank of America moved processing to a new dedicated platform, IBM’s XIV storage system and 112-core IBM BladeCenter Grid. The platform comprises the bank’s SAS for Enterprise Risk Management on SAS scalable performance data server and SAS Grid Computing. Data from eight SORs (System of Records) are pulled by the platform amounting millions of records and thirty terabytes of source dat. This allows the SAS environment to consume from IBM’s XIV storage environment, 3.9 GB of input output per second.
Was the Project a Success?
In my perspective, yes the project is a success. According to two of the Vice Presidents of the bank, this platform has been a game changer for them. It helps them access and filter millions of rows of data easily and has enabled efficient forecasting. It offers a concise solution merging functions together right from predictive modeling, creating interactive dashboard, forecasting and presenting data that has resulted in increased efficiency and smarter use of time available (Sas.com, 2015).
The Pain the Company would feel if the Key System Failed for 3 hours
Malfunctioning of data warehouses can cause data loss which the bank can definitely not afford. It affects the entire concept. System failure would affect the process of storing large amount of data and analyzing. It becomes more difficult to work with the data. Also, the interruption of data analysis may not yield accurate information.
Major Success Factors
The major success factor is the ability of the system to reduce bank’s calculation of PD from ninety six hours to just four hours. Also, time reduction by ninety percent in process of ad hoc analysis and the speed of processing has increased by three times compared to the previous environment. The process of scoring particular business portfolios that earlier too three hours has been reduced to ten minutes now so the bank has more time to analyze more data and concentrate on more business portfolios.
Major Risk Factors
Analyzing and storing of heavy volumes of data requires large amounts of software and hardware infrastructure. The reliability of these systems is questionable and over a period of time hardware malfunctions can lead to loss of important data. Back-up data saved in a separate system helps reduce risk. On the other hand, software malfunctions may cause inaccurate results which could affect the processes that depend on this information. It also requires skilled personnel.
Companies are largely looking towards IT solutions for storing and analyzing large and complex data which poses a great challenge to large corporations. The data warehousing helps them in increasing their efficiency by reducing the time taken for processing.
Enhancements to the System
Forecasting loss quickly and accurately could go a long way in helping a bank. The resources can be enhanced to make possible the scoring loans and risk assessment appropriately which could help risk management in the bank.
More information on the Bank’s SAS management structure and the functioning along with the areas of banking that utilize them the most could have helped obtain a comprehensive picture of the functioning and advantages of the system which could have in turn helped answering more effectively.
1. Avraham, D., Selvaggi, P., & Vickery, J. (2012). A Structural View of U.S. Bank Holding Companies. FRBNY Economic Policy Review. doi:10.2139/ssrn.2118036
2. Bank of America. (2015). Bank of America | Investor Relations | Capital, Liquidity, and Organization. Retrieved 23 March 2015, from https://investor.bankofamerica.com/phoenix.zhtml?c=71595&p=debtother#fbid=CqAqOLoL3zS
3. Sas.com. (2015). A comprehensive environment for more efficient forecasting. Retrieved 23 March 2015, from https://www.sas.com/en_us/customers/bank-of-america-business-analytics.html
4. TheOfficialBoard. (2015). Org Chart Bank of America. Retrieved 23 March 2015, from https://www.theofficialboard.com/org-chart/bank-of-america