Analytics in bankingTaking a fresh look at your challenges

Issues and trends in the banking sectorThe banking sector is rife with change and uncertainty. How will changes in banking laws and regulations affect profitability? What should be the framework of stress scenarios, including specific regulatory scenarios and guidelines? What is needed to correctly measure each business line’s different risk characteristics (e.g. loans, Collateralized Debt Obligation securities, structured products, derivatives)? Where can we more effectively apply better customer models to reduce losses and focus growth? These questions are fraught with ambiguity and there are no easy answers.

It is difficult to understand the current complex environment, much less to predict the future with any degree of confidence. Banks need more in-depth information to answer these and identify additional questions to effectively manage risk and drive risk-adjusted performance. Leveraging business analytics may help turn data into information that can provide these answers.

Three business drivers increase the importance of analytics within the banking industry:•Regulatory reform — Major legislation such as Dodd-

Frank, the Credit Card Accountability, Responsibility, and Disclosure Act (CARD Act), Foreign Account Tax Act and Basel III have changed the business environment for banks. Given the focus on systemic risk, regulators are pushing banks to demonstrate better understanding of data they possess, turn data into information that supports business decisions, and manage risk more effectively. Each request has major ramifications on data collection, governance, and reporting. Over the next several years, regulators will finalize details in the recently passed legislation. However, banks should start transforming their business models today to comply with a radically different regulatory environment.

•Customer profitability — Personalized offerings are expected to play a big role in attracting and retaining the most profitable customers, but studies show that a small percentage of banks have strong capabilities in this area. The CARD Act and Durbin Amendment make it even more important to understand the behavioral economics of each customer and find ways to gain wallet share in the most profitable segments.

•Operational efficiency — while banks have trimmed a lot of fat over the past few years, there is still plenty of room for improvement, including reducing duplicative systems, manual reconciliation tasks and information technology costs.

Data management and integrationRegulators continue to question the integrity and timeliness of data being reported to them. Recent mandates such as Legal Entity Identifier (LEI) by Office of Financial Research (OFR) also require integrated data across the enterprise. Enterprise data management and integration across silos such as risk and finance can enable banks to address regulatory concerns and demonstrate effective controls. Quality, integrated data is also a prerequisite to perform analytics that can drive business results such as systemic risk modeling that requires integrated data with a single source of truth across the enterprise. To attain enterprise data integration, banks should:•Define a enterprise data architecture and roadmap and

redirect spend towards integrated cross-functional, cross-sector projects aligned with target state data roadmap

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•Establish a data governance structure and an executive champion to enforce data governance and discipline across the enterprise. Measure data quality and visualize exposures due to data quality issues for key risk, finance, and regulatory business processes. Develop data remediation plans to reduce the exposure due to data quality issues.

•Perform auto-reconciliation across risk and finance silos to increase timeliness and consistency of reports and reduce army of people dedicated to manual reconciliation.

•Consolidate and rationalize interfaces feeding into general ledger, risk calculators, and analytics models to simplify data sourcing.

Banks can leverage consistent, integrated enterprise data to gain foresight that drives enhanced risk management. Fully integrated solutions are designed to address modified information reporting, analytics, and data requirements as needed by regulators.

Integrated risk analyticsIn the current environment, market pressures for better risk- adjusted performance requires effective use of analytics in risk management. Regulatory demands such as stress testing, systemic risk, Dodd Frank, living will, and Fed’s Supervisory Capital Program (SCAP) require analytics driven approach. The analytics driven approach can be used to answer questions such as measuring each business line and products risk characteristics, identifying exposures across counter parties and determining concentrations and high covariance, defining common metrics for measuring enterprise-wide risk-adjusted performance and risk profile. A number of risk functions can be enhanced by using analytics such as:

Regulatory reportingRegulatory reporting risk can be addressed through a profiling approach to select risk based samples and exceptions/anomalies for testing purposes. Automating the process of transaction level testing across various regulatory reports improves compliance and reduces regulatory risk.

Internal auditAnalytics allows internal audit to expand focus beyond compliance, through automated testing, to strengthening operational insight with quantitative data. Employing analytics streamlines the audit process by more effectively

allocating audit resources and greatly improves the level of operational insight, focusing more attention on scoping and reporting and less time in the field assessing what is already known.

Bank failure predictionThe last recession has witnessed the largest number of bank failures since the savings and loan crisis of the 1980s. The large number of failures provides an opportunity for banks to avoid similar risks. Applying advanced statistical techniques to call report and transactional data, banks can develop a quantitative framework that identifies business processes that elevate their regulatory enforcement and receivership risk. The high predictive accuracy compared to traditional indicators can allow banks to identify risks early enough to take action. The framework can also produce a parsimonious model designed to help guide policy decisions with a high level overview of specific risks.

Stress testingBanks with more than $10B assets are required to conduct regular stress tests applying extreme but plausible adverse conditions to bank’s business model to determine impact on the organization including profitability, solvency, and capitalization. This is an important part of regulatory capital planning decisions. Analytics is integral part of stress testing to develop various simulation scenarios, what-if scenarios, and analytic models that are specific to each product or portfolio.

On-demand riskNear real time analytics across front and back office to provide capabilities such as near real-time value at risk (VaR)., pre-deal limit checks, and exposure drill downs. These emerging capabilities facilitate the ability to incorporate risk in business processes to improve risk- adjusted returns for banks.

Lending credit risk analyticsDuring the recent economic downturn, many loan officers and underwriters realized a credit score alone may not suffice to predict default risk. The complex economic conditions require additional macroeconomic, product, and borrower demographic indicators to correctly forecast default. Banks can develop analytical models to evaluate the default propensity of each loan and to determine an effective collections strategy if a loan falls into arrears. By identifying the important attributes and likely delinquent loan outcome, they can appropriately allocate resources and mitigate risk with employing third party agencies.

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Combining default and collections models can provide concrete recommendations that improve the operational underwriting model.

Finance analyticsA bank finance function can apply advanced analytics to leverage general- and subledger data. By connecting transactions to underlying business processes, finance can support operational decisions in other functions. Customer and product level profitability models can help customer relationship management and marketing operations. Adding external data can also provide consumer demand forecasts to inform payment product development and decisions.

Enhancing forensic risk analysis capabilities can help banks better anticipate fraud. Bank finance and internal audit departments can invoke statistical techniques demonstrated in public company accounting investigations to help mitigate transaction fraud risk. Going beyond rules and controls, statistical methods define an evolving transaction profile that balances detecting new fraud schemes and limiting additional followup resulting from false detections. The accuracy and efficiency enable a timely batch review of millions of transactions.

Customer analyticsAs soon as the economy shows signs of stabilization, banks may quickly focus on growth. While some banks are already looking at growth opportunities, they are also aware that revenues are likely to remain under significant pressure for the foreseeable future. These banks are likely to experiment with a variety of growth strategies, such as attempting to enhance customer profitability by deploying more sophisticated analytics.

Some of the specific goals are to increase cross-selling opportunities, create better targeted offerings, develop more effective relationship pricing, and leverage bundled pricing to create more compelling customer experiences. Indeed, with many formerly innovative banking products now often viewed by customers as commodities, many banks have shifted focus to concentrate on enriched customer service as a primary differentiator.

Customer behavior analyticsTo more effectively serve their customers and drive profitability, banks can use advanced predictive analytic techniques to parse credit card databases, mortgage data, deposit data and even social networks to find subtle cues to help analyze, categorize and anticipate customer behavior patterns. This capability can help improve marketing and risk management practices. It can also help create products aimed at a traditionally overlooked customer segment — e.g., credit cards and loans that helps first-time defaulters rebuild their credit history. Analysts can also make finer distinctions in customer behavior patterns that allow them to more quickly detect deviations from normal behavior — e.g., widespread shifts in credit card spending habits from upscale to value-priced retailers. This analytics capability can help banks more effectively tailor their customer service offerings and develop an enhanced customer-service portfolio that serves as an effective differentiator.

Intelligent customer segmentationIn the past few years, only a few leading institutions have established programs to transition from product-to-customer-centric strategies to bolster their growth, although many have talked about it. The implementation of these strategies requires more sophisticated customer segmentation capabilities — and only few banks have them. Most retail banks can benefit from enhanced insight into current and potential customer profitably, preferences, and needs. This innovative way to segmenting customers uses multiple internal and external data sources including account, billing, demographical, and psychographical information. Advanced analytics solutions can provide banks with tools and templates to more effectively segment their customer base to acquire, service, cross-sell, retain, and expand personal and small business relationships.

“Risk comes from not knowing what you are doing” — Warren Buffett

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Price and profitability analyticsAdvanced analytics solutions can also help with product pricing and profitability analysis. Banks can analyze their product portfolios in relation to their competition and customer base to develop pricing strategies that better reflect their segmentation models, align with customer characteristics, and provide more opportunities to improve profitability. Pricing models that help banks set their rates and fees based on customer price elasticities can be powerful tools to improve margins and appropriately support growth strategies.

Besides helping banks make more-effective pricing and product and service offering decisions, granular profitability analysis also comes into play in navigating the new regulatory environment. Regulatory changes are forcing many banks to drop certain traditional and profitable business practices, such as proprietary trading. And, more strict capital requirements may suppress banks’ risk appetite for focusing so heavily on traditional business lines, such as small business lending, subprime credit, etc. To sustain profitability, banks can utilize analytics to develop demand-based pricing models that help them analyze and reassess their strategic priorities — and better understand where profits lie — across geographies, business lines, and legal entities.

Improved customer service and relevancySocial media has emerged as an influencer of brand awareness and loyalty, as well as a powerful catalyst for community building, albeit with new compliance implications. Banks can leverage social media as an analytics engine. Using social media data to power analytics applications, banks can better understand customer preferences and align communications, products, sales strategies, distribution channels, and customer service strategies to facilitate better individual customer experiences. Utilizing social networks and link-analysis techniques can also assist in the discovery of relationships between accounts, customers, households, groups, rings, and institutions, and lead to more in-depth customer knowledge.

By leveraging advanced analytics, banks can also develop more sophisticated models to understand the stages of customers’ lifecycles, providing differentiated customer experiences that are relevant to them.

Customer relationship analyticsAdvanced analytics solutions can help banks in their efforts to develop more effective cross-selling and up-selling methods. For example, with scenario modeling algorithms, banks can identify “the next best” banking product or service most likely to be purchased by a particular customer or segment. Banks can also leverage analytics techniques like affinity grouping to identify those products that would be the most profitable additions to a customer’s existing portfolio.

Many banks — especially larger, national banks, have invested heavily in marketing and the development of nonbranch distribution channels; i.e., ATMs, Internet banking sites, call centers, and wireless technologies. However, these multichannel marketing actions and infrastructures typically require a significant investment, as well as the development of a broad implementation strategy. Thus, implementation of multichannel marketing has led to increased customer service costs.

To mitigate these costs, many banks are considering rationalizing their distribution channels and reduce marketing spending to better align with individual customer segments and payment preferences. Analytics can help in this rationalization process by providing more in-depth insight, by providing increased visibility into the drivers of financial purchases and/or transactions by channel. This visibility can help banks design more

Advanced analytics solutions can provide banks with tools and templates to more effectively segment their customer base to acquire, service, cross-sell, retain, and expand personal and small business relationships

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effective distribution channel models, based on the nature of customer segments served and costs incurred. It can also help banks quantify the return on investment from marketing investments, such as media buying and promotional activity, and better facilitate the allocation of the marketing budget.

Operations analyticsThe pressures on banks’ information technology budgets will likely continue as institutions look to execute broad cost-reduction programs to preserve capital and improve profitability. Chief information officers have been asked not only to cut discretionary spending, but to drive additional savings out of their baseline technology operational run rates as well. One way of attaining operational savings is to make the supply-chain function more efficient.

Supplier and procurement analyticsAnalytics solutions have been demonstrated to provide banks with the ability to better track back-end processing to more effectively support front-end delivery to their customers. Analytics has also been shown to provide banks with more visibility into their external-vendors’ performance in order to facilitate more timely and cost-effective management and discover opportunities to achieve greater cost and process efficiency.

Expense analyticsIncreased regulatory and tax requirements have led many banks to find ways to reduce their compliance-related expenses. One way to reduce these expenses is to leverage analytics to more-effectively analyze tax and litigation spending to help reduce known costs, and to identify hidden costs.

Further, banks can reduce costs by leveraging commonalities across different areas through enabling shared services.

Workforce analyticsBanks continue to closely monitor headcount, and they understand that the current skills gap will continue to grow in critical workforce segments. Anticipating workforce demand and maintaining high retention rates while effectively recruiting new employees and properly allocating them will likely continue to be a challenge for many banks.

Workforce performance managementAnalytics can help banks in their efforts to analyze, model, and more-efficiently manage their workforce. Banks can leverage advanced analytics solutions to improve people-management decisions and control workforce costs and productivity. Analytics can also be used in a wide variety of other workforce management activities, such as workforce planning to improve staffing utilization, recruitment analysis to predict the probability of success in a particular job family, emergent leader analysis to identify and train future leaders for next-level management roles, and organizational alignment to “right size” with more-effective management layers and control structures.

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ContactsBank sector analytics leadersRobert Contri U.S. Banking & Securities Sector Leader Deloitte Consulting LLP +1 212 436 2043 bcontri@deloitte.com

Omer SohailDirector, Banking & Securities Sector Deloitte Consulting LLP +1 214 840 7220 osohail@deloitte.com

Vivek KatyalPrincipalDeloitte & Touche LLP+1 612 397 4774vkatyal@deloitte.com

Prakash SanthanaDirectorDeloitte & Touche LLP+1 212 436 7964psanthana@deloitte.com