BI solutions for Banking and Financial Services


Overview

A Gartner survey evaluated responses from banks, insurers and nonfinancial businesses. The survey found that more than 95 percent of banking respondents agreed that BI is a strategic initiative driven by senior management, and more than 90 percent agreed they received the value expected from their BI investment. This compared very favorably with nonfinancial respondents, where both categories were approximately 20 percentage points lower.

BI has been primarily at the departmental or line of business level. Financial services companies, as a whole, struggle with enterprise-wide solutions. These companies are typically focused on individual lines of business or departments. This is common because business performance is based on financial results for each line of business. There is still a significant opportunity to improve the business with BI.

Today's banking, finance, and investment companies operate in an industry more diverse and unpredictable than any other. Through mergers, acquisitions, partnerships, and internal growth, financial institutions are racing to gain a competitive edge by entering new business areas and delivering more products and services. That's why industry leaders have turned to BI solutions to deliver crucial business information to managers, employees, customers, and partners.

Due to the complexity of the recent economic crisis , it’s extremely difficult to determine the triggering effects that caused the integrity of the system to degenerate and ultimately stall. In looking at the number of banks and businesses that failed, it is ironic that many of the participants had also invested millions of dollars in sophisticated business intelligence (BI) systems, processes and consulting firms. Decision makers at all levels were bombarded with conflicting and incomplete information from a wide variety of sources. Clearly the increased insights offered by these sources were insufficient.

Trend of BI in 2009

Financial services companies will have to develop innovative ways to acquire and retain customers. Cost takeout may be the #1 criterion for evaluating BI and data warehouse (DW) projects in 2009, but right after that are improving customer management capability related to protecting the customer base, work associated with mergers and acquisitions (M&A) integration, and risk analysis.

Use of BI in Financial Services

BI solutions in the financial services industry have been delivered most effectively within individual lines of business. BI has allowed individual financial products and lines of business to use integrated information for financial analysis and customer service. BI is used for risk management, regulatory compliance and reporting.

Risk Management
  • Data Mining
  • Predictive Analytics
Risk in financial services comes in two forms. The first is the risk to lending institutions of default on payments such as mortgage payments. Defaults are a problem because they disrupt cash flow, affect securities issued by the financial institution that are backed by loans and can affect (as one of the rating factors) the rating agencies' evaluations of the financial strength of the company. BI data mining and statistical modeling are being applied successfully to the problem of managing risk associated with defaults on loans. These tools help to identify patterns in data that correlate with loan defaulters so that the process of underwriting or approving loan applications can screen out applicants with these data patterns.

The second area of risk is the evaluation of the company by rating agencies. Evaluations are meant to provide an assessment of the stability of the financial institution, its ability to fulfill its obligations to customers and its relative (compared to other companies rated) strength for investors. Relative strength is a risk factor because an agency's rating can affect the cost of capital to the financial institution, and the cost of capital is the "raw material" of financial products and services. It determines the operating margin (the difference between costs and gross revenues) of the company because rates for many financial products and services (such as mortgage rates) are set by the market. A lower cost of capital (due to a better rating) can increase the operating margin and provide an advantage over competitors with lower ratings.

Regulatory Compliance
  • Reporting
  • OLAP (Online Analytical Processing)
  • Analytics
  • Text Mining
Both the USA Patriot and Sarbanes-Oxley Acts will require enterprise-wide financial and operational information integration and analytics combined with management of documents, e-mails and company content for full compliance. Too often, however, the focus is on reporting and records management alone. The information integration and analysis component is a typical BI application, which is why virtually all of the major BI analytics vendors quickly introduced functionality and marketing materials designed to help create the financial transparency required for Sarbanes-Oxley compliance. The major challenges to a BI solution for these regulations are providing the necessary ability to rapidly drill down into details embedded in enterprise resource planning (ERP), contract management and other back- and front-office systems, and integrating information and analytics with records and document management processes as directed by a corporate compliance officer.

Information Consolidation
  • Reporting
  • OLAP (Online Analytical Processing)
  • Analytics
In financial services, customers make applications, the applications are processed or underwritten, a contract is issued, accounts are set up and serviced, and customer problems are handled. As with most businesses, these various activities are supported by many disparate application systems that make it difficult to obtain consistent, correct, consolidated or synchronized information from them. Using BI to consolidate customer account information can save considerable time for staff.

Consolidating customer account information can also support customer self-service with the use of a centralized data warehouse (DW) or operational data store (ODS). According to Gartner, Inc., "The top five data integration vendors report that the one consistent problem plaguing their clients is the difficulty of determining their true number of unique customers." While a centralized customer information file is an important step to understanding customers, the ability to analyze customer profitability, overall transaction patterns and trends can best be supported by a complete and fully integrated customer data BI solution.

Consolidated information can also support financial analysis, another important use of BI in financial services. Financial analysis focuses on product profitability, portfolio performance and scorecards or dashboards for a line of business or profit center.

Sales and Marketing
  • Reporting
  • OLAP (Online Analytical Processing)
  • Analytics
  • Data Mining
  • Predictive Analytics
Every financial services company has many products and services to offer its customers and several different marketing channels to reach them. Customers do not always know all that is available to them, and every company wants to expand its business with each customer. This makes cross-selling (the practice of selling customers additional products and services) a top sales priority in every financial services company.

Customer segmentation is grouping customers based on certain characteristics. The financial services industry generally segments customers based on a single financial measure: the amount of money in the account or the size of the policy or contract. Marketing effectiveness is limited by this lack of customer information. Equally important is the need to analyze the effectiveness and costs of sales channels used in the business.

Every financial services company must establish a more sophisticated view, substantiated with data and analysis, of their customers and sales channels. This needs to include data on customers from external sources and data on costs of sales and distribution in order to determine customer profitability and sales channel effectiveness. Each of these requires an enterprise-wide rather than a line-of-business perspective in order to be effective.

Business Challenges

Risk and Compliance

Many of the original solutions that were implemented were focused on a single aspect of risk or component of a regulation such as Basel or anti-money laundering(AML). With Basel, for example, the primary development focus was on credit risk management with few allowances for the more vague component of operational risk. This resulted in data stores optimised for the required reporting and capital calculations thus forcing separate solutions for operations risk and the other regulatory requirements.

On top of the required regulatory reporting, came an increased number of requests for ad hoc reports from the regulators to confirm compliance with the regulations. In most cases, the data required for such compliance reports were not available in the highly optimised data stores that were built. This spurred asurge in what is sometimes referred to as ‘shadow data marts.’ Collections of large amounts of data in spreadsheets and access databases that are manipulated with desktop reporting tools to generate the reports required. Documentation, governance standards and internal controls are typically short-circuited, thus replication of the report or reconciliation of the report with internal ledgers and transaction files is impossible.

Finally there is the ‘management letter’ and potential regulatory fines in the millions of dollars (coupled with the free derogatory mention of the bank on the front pages of the Wall Street Journal and Financial Times) for those reporting requests or incomplete implementations of a required activity such as ‘know your customer’ or AML. These generally arise when the installed solutions cannot produce the required reports and results, or do not have the breadth of functionality to deal with the requests. So yet another specialised solution must be designed and implemented. Typically, these run nine to 18 months and cost in the tens of millions of dollars.

Traditional Risk and Compliance Solutions
  • Traditional risk management solutions have focused on the tool or solution. Data and the architecture of the data were optimised for the tool not for the strategic solution.
  • Little to nothing was done around the critical feedback loop. This is where many of the ad hoc compliance reports are aimed, and the inability to directly report on and demonstrate confirmation of management’s actions and policies is driving many management letters and potential fines.
  • The data required for risk management reporting is the same data that is required for the corresponding and related regulatory applications. Yet due to redundancy, millions of dollars are often wasted to source the same data multiple times for different legal and regulatory compliance efforts.
  • Seldom did the same programmer write the different regulatory applications. The result has been different and inconsistent data being used when the same data should have been used. This is an enormous data quality issue that impacts compliance and ultimately, confidence in the business.
  • Sarbanes Oxley requires absolute transparency from data source to regulatory and compliance reports. This transparency is not available with traditional tools and reporting solutions. The two primary reasons are:(a.) the data is sourced from intermediate files versus source files due to costs and (b.) many tool solutions embed data aggregations and transformations within their optimised data store or reporting templates, further destroying the transparency.
Good Risk and Compliance Solutions
  • ‘Source once; use many’ is the mantra that drives the ability to deliver accurate, consistent, high-quality,transparent, expansive, adaptive, flexible and affordable data.
  • Traditional methods of storing data (warehouses, marts and cubes) are suited for tools and reporting but, because of their structure, they have become the expensive problem. Storing data at its elemental level – atomic layer storage – is the key to tremendous cost reduction, flexibility and adaptability.
  • Traditional forms of data sourcing must be re-ordered and augmented so that data accuracy, consistency and quality can be measured and tracked thus enabling data to be ‘certified’ for use within regulatory and compliance tools and applications.
  • Complete flexibility, as it relates to data, is required so data can be delivered within days and weeks of new regulations taking effect or of new compliance requests. This means that any conceivable hierarchy or master data must be able to be quickly implemented.
  • Analysis results and management decisions must be able to be quickly fed back into the data provisioning framework so that they can be acted upon and measured.
  • All of the aforementioned points need to be applied across the entire enterprise spanning and incorporating global regulations, local country regulations and laws, local country currencies, and accounting standards.

Data Provisioning

Business Requirements

Data provisioning involves re-thinking the traditional process by which data is captured from source application or external data files for the purpose of addressing risk management. It is also the key component in handling data for all major strategic and corporate issues such as:
  • Enterprise reporting - Finance, operations, analysis, modeling
  • Front office/back office - Trade execution, risk/exposure, capital
  • Operational excellence - Six Sigma, TQM, activity costing
  • Risk management - Credit, market, interest rate, operational
  • Credit markets - Sub-prime, risk exposure, delinquencies
  • 360 degree view of the customer - Segmentation, targeting, campaign management
  • ‘Organic growth’ - Client experience, profitability, wallet share
  • Compliance - Basel, KYC, AML, MiFED, Solvency2, SEC
  • Merger integration - Strategic utility
By implementing data provisioning for risk management, you have implemented a solution and architecture that can be leveraged beyond risk. From an investment and affordability perspective, you get a powerful asset.

Business Demanding - Invest Once, Build Once, Provide for Many

The traditional data acquisition process is point focused and aimed at moving data into storage structures that are optimised for specific reporting, analyses or modeling. In this case, Basel II on one hand and a different approach for AML or ‘know your customer’. If the business requirements change, as they do frequently in the risk management world, the new data requirements cannot be met because of the rigid data warehouse and mart structures. Another poor result is that changing the ‘optimised old structure’ is either too expensive or time consuming.

The business of banking today (total banking not just risk management) demands:
  • Faster and cheaper access to data of all types (structured, unstructured, web-based) and all latencies (from real time to monthly)
  • Data delivered in days and weeks versus months and years
  • Data in any format, with any hierarchy
  • Access to all master data reference hubs
  • Data that is enterprise-sourced and available to all applications, analysts, reporting tools and channels
  • Data that is quality scored and capable of being certified as fit for specific uses (regulatory reporting, marketing analyses/modelling, investor reporting)
  • Data that is available to service-oriented architecture (SOA) solutions in real time
  • Data that can be acquired, updated and pushed to real-time systems
  • The ‘operationalisation’ of the intelligence gained from analysis and modeling
To deliver all of this requires a radical new process centred on data, data provisioning.

Key Design Concepts
  • Data provisioning can be ‘contained’ within an architecture that enables strong governance and rigorous business, financial, process and procurement management; it can be built and run as an enterprise data utility.
  • The core process that drives data provisioning is the set of enhanced and expanded integration services.
  • Any and all source files – regardless of latency – flow through data provisioning prior to being acted upon or used by tools or applications. This enables the institution to closely govern, manage and control its strategic data assets.
  • If properly designed and implemented, any analysis, reporting, modelling or management tool can be ‘plugged’ into the data provisioning platform that will deliver accurate, consistent, high-quality, transparent, expansive, adaptive, flexible and affordable data in the format that is required.
  • The ‘management feedback loop’ is enabled and measured because analysis of results and operational data are connected and are able to work together – in real time – across channels and applications.
  • The entitlement services can provide enhanced security features with access at the field, record or file level thus enabling compliance with the myriad global rules on visibility.
  • Existing and future SOAs can be plugged into the provisioning platform thus verifying that the right data; quality scored data is provided to the requesting application.
  • Enterprise metadata (business and technical) is captured and published for use by the enterprise.

Opportunities of BI in Financial Services

The major opportunity for BI in financial services is clearly for enterprise-wide integration of customer, product, channel and operational data. Too much information is locked in the wide array of application systems including those for departmental and line of business products, customer relationship management, enterprise resource planning, financial management and e-business. There are challenges to creating a truly integrated, enterprise-wide information environment in financial services companies.

The challenges are not technical. Rather, organizational issues present the greatest challenge for financial services companies. In particular, the management culture of financial services companies is focused on financial performance and profit. This culture comes from the entrepreneurial moneymaking that is at the heart of the financial services industry. The drive for enterprise-wide information integration comes from a high-level executive. Currently, it is the rare executive who is willing to invest in an enterprise-wide solution and all that it entails; but there are more such executives appearing every day.

What happened in BI solutions for Financial Services?

  • The majority of BI deployments was biased toward the internal operations of the individual institutions and did not adequately address the interactions between members of the value chain. Individuals, institutions and nations were focused on optimizing their own individual success at the expense of the system as a whole.
  • The assumptions used in building and implementing traditional BI systems were based on the world as we knew it, not on a holistic view of a global financial system of interconnected parts. Most BI systems were designed based on best practices and lessons learned from historical experience and repetitive practices. The interconnectedness of our world community view now requires a more open model that must factor in external data sources and analyze a variety of constantly changing future possibilities.
  • The majority of information yielded from BI deployments was based on hindsight and failed to deliver the foresight for management to make decisions based on a wide range of unfamiliar scenarios and risk threats. Most of the major financial institutions have easily invested over $10M in traditional reporting tools, data warehouses and applications that organize and report on historical results and current activity. While forward-looking analysis tools are used in specific departmental applications, they are often subject to latency and data quality limitations in up-stream systems.
  • There was a widespread presumption that as long as all participants engaged in shifting risk to each other, they would be smart enough to quickly react and cover their bets. The possibility that exposure of one firm would have ripple effect on other firms was not adequately factored in until the wheels of the crises were in motion.

What is the role of BI in transforming the global financial system?

As an industry, BI professionals and executives have a unique opportunity and responsibility to take a more systemic view of their business and how it relates to other participants in the global financial system. In the short run, we must apply the best technology and methods we have to identify and exploit opportunities to revive the financial system while doing a better job of identifying and managing risk through efforts such as:
  • Increased emphasis on the use of high ROI applications: Applications such as risk management, fraud detection, asset valuation, customer segmentation and others offer enormous payback potential that can directly influence top- and bottom-line performance. Companies must take a balanced approach to managing both performance and risk to do a better job of addressing the potential factors that might quickly erode hard-earned gains.
  • Focused parallel deployments to reduce risk and achieve higher ROI: Traditional serial approaches to large BI projects can take years to achieve. By that that time, requirements, sponsors, technology platforms and market pressures have changed, resulting in scope creep, higher costs and unmet expectations. Quick-strike paralleled deployments offer faster payback, lower risk and much higher ROI than serial approaches.
  • Increased emphasis on the connection between the BI investment required and the business case in terms that relate to the ultimate stakeholder: Business intelligence professionals must develop new skills to justify, deploy and exploit new technologies based on a clear, significant and immediate business case.
  • Core support functions such as finance, IT and HR must use BI/analytics technology as a vehicle to transform their functions to become a more effective partner with line of business departments: Rather than serving as back-office administrative services, these departments must leverage their knowledge of resource management, corporate performance, resource utilization, analytics and other domains to address an array of evolving business models, alliances and product offerings.
In the long run, the private sector and government agencies must develop more effective ways to manage performance and risk at both the local level and between the various participants of the global financial community. To do this, the private sector and governments will need to consider steps such as:
  • Invest in technologies that are capable of harvesting information more efficiently for specific high value purposes: Information is growing at a faster rate than anything else on the planet, although the federal debt is making a good effort to catch up. As we shift from performing analysis at the local level to looking at more system-wide effectiveness, the potential for dynamically managing globally focused databases will require higher levels of performance and connectivity than exist today.
  • Develop new methodologies for managing system-wide performance and risk: The industry as a whole can no longer afford to operate with an impaired understanding of their risk profile and rely on gamesmanship to transfer risk to the next participant in the value chain. Better visibility is required at the customer, product, corporate and system-wide level. To do this, new processes, algorithms, management philosophies and business models will be required to operate in an interconnected global financial community.
  • Increase executive sponsorship to improve their knowledge-based skills and understand the relationship between their actions and the company’s financial performance within the context of the global financial community: As real-time analytics within a global financial system become more commonplace and embedded in the various information systems, it will be necessary for decision makers at all levels to develop new skills and adopt a more holistic awareness of their actions.
  • Increase funding for educational programs to enhance the knowledge-based skills in all disciplines, not just IT: Business intelligence as we know it is rapidly becoming a thing of the past. Just as IT and other technologies have permeated almost all roles within the company, knowledge-based skills and knowledge re-use are become more critical skills for future labor forces.
For business intelligence to move beyond data movement and reporting, it is important that the philosophy of knowledge utilization and optimization become embedded in our core processes. Decision makers within functions such as risk management, loan origination, application processing, management recruitment, sales and marketing, finance and strategy must all improve their ability to effectively utilize information to help avoid the clotting and sub-optimization of the global financial community.

We are forced to come to grips with the fact that we must deploy systems and practices that look beyond the boundaries of individual organizations to manage the systemic risk factors that may trigger future clots in the global financial community. This will require a new generation of systems that go beyond the capabilities of traditional BI solutions; vendors must deliver the insight to operate effectively within a much larger, more dynamic system of interacting parts. Finally, we need a fundamental shift in the use of business intelligence that will allow us to react faster and anticipate changes that will occur rather than locking us into assumptions based on the way the world used to operate. We cannot manage the future by simply basing our decisions on the paradigms of the way the world is today or has been in the past – we must place a higher importance on using business intelligence and analytics to address the way the world will be in the future.

Key business requirements

  • Increase efficiency of core business processes such as call center management, loan processing, and electronic trading.
  • Better manage the risk associated with investments, credit and lending, and consumer bankruptcies.
  • Detect and deter fraudulent activity such as money laundering and identity theft.
  • Comply with industry regulations such as T+1 and the Bank Secrecy Act.
  • Retain and expand your client base, improve cross-selling opportunities, and increase customer profitability through a better understanding of behavior, needs, and preferences.
  • Utilize an industry standard like XBRL for a real-time, accurate, and single version of the truth.
  • Address globalization issues.

How our BI solutions can help

Our BI solutions can deliver significant ROI, along with competitive and strategic value to banks and financial services companies by securing, managing and repurposing all forms of unstructured content. These solutions can help streamline and automate business processes to deliver trusted information to employees, customers and partners at the right time and in the right business context. They can:
  • Improve customer service levels, expand customer service offerings and provide a balance between online versus in-person delivery channels.
  • Streamline how information is shared and accessed across every business line enterprise-wide.
  • Integrate processes and the flow of information across front-, middle- and back-office operations.
  • Speed the pace of operations through intelligent BPM—increasing productivity while reducing costs and boosting bottom-line results.
  • Achieve a complete, 360-degree view of the customer—through integrated data, content and processes—delivering a single version of the truth.
  • Lower the cost of meeting regulatory compliance mandates while improving risk management.
  • Scale enterprise solutions to meet the changing demands of customers, partners and employees.