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Analytic Challenges in the Banking Industry
In recent years, the banking industry has evolved dramatically, driven by changes in the business and economic environment, in legislation, in competitive pressures and in enabling technologies. With a wide variety of products, such as credit cards, mortgages, home equities, lines of credit, savings and checking accounts, insurance and investment products, banks need to anticipate and satisfy the changing needs of their customers. In addition, financial institutions need to be able to estimate and review risk and compliance with regulations such as Basel II and mandatory capital requirements. More than ever, banks need better understanding of key indicators and best practices for decision making in all areas of operations, including:
In today's demanding marketplace, leading financial institutions have no way to differentiate themselves except by taking advantage of the information locked up in their enormous volumes of data from transactions, daily operations and demographics. Timely analysis of this data will help these enterprises manage all facets of customer interaction, investment, risk, regulation, and asset evaluation to enhance customer experience and increase profitability.
The Analytic Challenges
Traditionally, financial institutions have invested money and effort in predictive and descriptive models to understand key influencers and changes in the business by analyzing the data collected in daily business operations. This approach may be used to design reports and executive dashboards as well as to understand risk and fraud, determine marketing ROI and improve business operations at every level.
However to do this, expert technical analytic teams spent weeks searching for information in a sea of data, using reporting, OLAP and traditional data mining software. To be effective today, an enterprise modeling solution must be fully automated, making it easy for an executive to search for information in terabytes of data.
Marketing and CRM
Modern financial institutions have long used predictive modeling to generate lists for direct mail, e-mail and telephone campaigns. But only a fraction of business questions were modeled because traditional tools are slow, expensive and unreliable in the hands of business users. With state-of-the art analytic technology, marketing departments can identify all underserved market segments and create, test and launch highly targeted product and service offerings for small groups of customers and prospects. Shifting from product-centric to customer-centric thought processes, leading banks are now able to design and recommend products that deliver the right solution to the right customer at the right time.
Customer Profitability: Assess the overall cost of maintaining a customer and weigh it against the predicted revenues from this customer and the predicted length of the relationship.
Acquisition and Cross-Sell: Match a specific product offering to each customer's needs and probability to purchase.
Retention: With many alternative offerings in a competitive and increasingly global market it has become easier than ever for customers to compare products and switch banks.
Operations and Finance
The Basel II accord obliges banks to reconstitute historic data with regard to elements of both credit risk and operational risk for long maturity dates (five to seven years). In the future, banks will be obliged to store and be able to reconstruct additional information, for various purposes, including updating internal models, reporting to supervisory bodies and communications intended for the markets and financial investors. These challenges can create new opportunities for financial institutions.
There are three routes to transform a "defensive" regulation into a tool for creating value:
Risk Management: As risk patterns change, the detection mechanisms must evolve with them (static filters have only short term value). New methodologies in terms of built-in deviation detection that alerts users or automatically triggers the creation of a new model as incoming data changes should be in place.
Fraud Detection: The key for early detection of new types of fraud is to monitor all of the available information for anomalies. Anomalies that are verified to be new fraud schemes can then be flagged and modeled for automated early detection of probable fraud before significant financial damage is done.
Pricing: Macro-economic pricing by estimating the cost of risk which may be added to the cost of capital, administrative overhead, and estimated economic financial costs should be one of the key focus areas as well.
Monitoring Financial Flows: Prediction and monitoring of capital flows:
By using the historical information stored on customers, Financial Institutions can design reliable models to better assess risk and better conform to the new Basel II requirements for risk assessment. Other important areas of application in the banking industry are financial planning and asset valuation, analysis of cash flow, service cost, billing and collections should be also part of the bigger framework as well.
Leading technology enablers have come up with state-of-the-art solutions to address most of the analytic challenges previously discussed. Even though, these packaged products are fairly exhaustive in terms of functionality and scalability, these are yet to be seen as "one size fits all" type of solution across the banking industry.
From my experience in implementing DW/BI solutions in the banking industry, these products need to undergo a moderate to exhaustive amount of customization exercise to fit the requirements of individual banking organizations.
An enterprise modeling solution must be able to:
Enterprise modeling starts with identifying the key performance indicators for corporate business goals. Market share, for example, could be one KPI for the overall goal of increasing revenues. Business managers use these business drivers or KPIs to explore the information contained in their data warehouse and automatically identify the key drivers for market share. As the next step they create a set of projects or initiatives to influence drivers that increase market share, such as customer retention. Using these they can predict and rank the profitability of each initiative, and decide where best to invest to achieve the desired outcome. What used to take weeks for experts to work on now takes an executive minutes to analyze, understand and take action?
Analytic challenges, especially in the financial domain are complex in nature and always carry a huge capital risk (gaining or losing momentum). Industry-wide acceptance of common tools and technologies as well as strategic initiatives within the enterprise should guide financial institutions to attain the desired level of maturity.
For more information on related topics visit the following related portals...
Analytic Applications and Financial Industry.
Soumendra Mohanty is a program manager of Accenture, India where he leads the Data Warehousing/Business Intelligence Capability Group providing architectural solutions on various industry domains and DW/BI technology platforms. He has worked with several fortune 500 clients and executed projects in various industry domains as well as technology platform areas. He can be reached at Soumendra.Mohanty@accenture.com.