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Big Data in Financial Services: How to Improve Performance with Data-Driven Decisions


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Most banking and financial services organizations have only scratched the surface of leveraging customer data to transform their business, realize new revenue opportunities, manage risk and address customer loyalty. Yet a business’s digital footprint continues to evolve as automated payments, location-based purchases, and unstructured customer communications continue to influence the technology landscape for financial services.

Published in: Technology

Big Data in Financial Services: How to Improve Performance with Data-Driven Decisions

  1. 1. Big Data in Financial Services:How to Improve Performance with Data-Driven Decisions November 28, 2012
  2. 2. About PerficientPerficient is a leading information technology consulting firm serving clientsthroughout North America.We help clients implement business-driven technology solutions that integratebusiness processes, improve worker productivity, increase customer loyalty and createa more agile enterprise to better respond to new business opportunities.
  3. 3. Perficient Profile Founded in 1997 Public, NASDAQ: PRFT 2012 Projected Revenue of $320 Million Major market locations throughout North America — Atlanta, Austin, Charlotte, Chicago, Cincinnati, Cleveland, Columbus, Dallas, Denver, Detroit, Fairfax, Houston, Indianapolis, Los Angeles, Minneapolis, New Orleans, Philadelphia, San Francisco, San Jose, Southern California, St. Louis and Toronto Global delivery centers in China, Europe and India 2,000+ colleagues Dedicated solution practices 87% repeat business rate Alliance partnerships with major technology vendors Multiple vendor/industry technology and growth awards
  4. 4. Our Solutions Expertise & ServicesConsulting Services Perficient Solutions• Big Data Strategy & Roadmap - Enterprise Application Integration• Big Data Assessment - Business Intelligence• Architecture Planning & Platform - Business Process Management Selection - Enterprise Architecture• Master Data Management - eCommerce• Data Governance - Customer Relationship Management• Regulatory Compliance Assessment - Enterprise Content Management - Master Data ManagementBI & Analytics Capabilities - Portal / Collaboration• BI/Big Data Implementations - User Experience• Risk and Fraud Detection - Mobile Solutions• Social Analytics• Cloud Analytics• Real-time Analytics• Self-service Analytics Perficient brings deep solutions expertise and offers a complete set of flexible services to help clients implement business-driven IT solutions.
  5. 5. Our SpeakersMike Panzarella, Director, Financial Services PracticeWith 20 years of experience with Big Four consulting andcommercial banking, Mike has expertise in BI platform architecturesfor Fortune 100 financial service firms with a focus on social mediaand mobile convergence. Mike has extensive experience indesigning and implementing Big Data solutions for Fortune 100companies.Jeff Fisher, Director, FS Practice Operations &Advisory ServicesWith over 20 years of experience as a technology leader with globalenterprise organizations, Jeff has a proven track record of successleading technology teams in financial services organizations.
  6. 6. What We Will Cover Va l u e o f B i g Big DataAbout Us Data Tr e n d sChallenges Effective Strategies Leverage ITNext Steps Investments Q & A
  7. 7. Value of Big Data inFinancial Services
  8. 8. What is Big Data?
  9. 9. What is Big Data?Extracting insight from an immense volume, variety and velocityof data, in context, beyond what was previously possible.
  10. 10. Business Impacts of Big Data 44x as much Data & Content 2020 35 zettabytes Business leaders frequently Over Coming Decade 1 in 3 make decisions based on information they don’t trust, or don’t have 1 in 2 Business leaders say they don’t have access to the information they need to do their jobs of CIOs cited “Business 2009800,000 petabytes 80+% 83% intelligence and analytics” as part of their visionary plans to enhance competitiveness Of world’s data is unstructured of CEOs need to do a better job 60% capturing and understanding information rapidly in order to make swift business decisions
  11. 11. Traditional Analytics• Managed schema• Data in many siloes• Customer view not always federated across the enterprise• Slowly changing facts and dimensions
  12. 12. Value to the EnterpriseCustomer-centric Outcomes• Retail mobile offers based on preferences or buying patterns• Model targeting done with online banking offers based onFunctional Outcomes• Collect KPI and metrics for Enterprise Performance Management (EPM)• Compliance checks and auditsSource: CMSwire, IBM: The Business, IT Casefor Big Data Investments (Oct. 31, 2012)
  13. 13. Big Data Challenges Economy Trust Uncertain global conditions are Rebuilding customer trust affecting revenue and reducing and marketplace IT spending. confidence is critical to future growth. Competition Regulation Intensifying with Radically increased mergers, oversight is drivingacquisitions, and non- investment in risk traditional entrants. management technology. Customers Consumers have rapidly evolving expectations for $ Capitalization Mature and emerging market segments are focus on offerings and services. optimizing use of capital.
  14. 14. Value to the Enterprise Payments Big data can detect and prevent a wire Branch management transfer incidents of fraud.Big data interprets which branches or products are performing the best. Executive leaders Big data enables more effective business decisions using accurate data across all time horizons. Relationship management Big data considers the risk and profitability of the entire customer Risk and finance relationship when pricing Big data streamlines new deals. compliance and Marketing understand risk exposure Big data predicts the right across businesses and offer for the right customer at regions. the right time.Source: IBM Corporation
  15. 15. Meaningful Data Drives Quality Decisions Marketing & Solicitation Increase flexibility and What channels are more streamline operations effective to solicit customers? How do I deliver real-time Is our customer portal an insight at the point of impact? effective tool for offering new products? How do I provide better executive visibility into Are our products enterprise performance? competitively priced? How do I manage the evolving risk landscape? Optimize enterprise Create a customer- risk management focused enterprise Who are my ideal customersAm I able to effectively identify and how do I attract them? fraud before it occurs? Could I improve credit How do I retain my most underwriting? profitable customers?
  16. 16. Big Data Capabilities Required Capabilities of Big Data: • Processing • Data Management Big Data • Services Hadoop Distributed file systemDATAGOVERNANCE ISCHALLENGE RDBMS vs. Hadoop 16
  17. 17. Bank’s Application Data Tendency to Apply Intuition Tendency to Apply Analytics Financial management and budgeting Operations and productionPreferred Big Data Approaches Strategy and business developmentOptimized software-only solutions like Hadoop 37% Sales and marketingScale up existing relational technologies 33% Customer Service Product research & developmentCloud infrastructure or service providers 32% General managementIn-memory databases 29% Risk management Business intelligence appliances 28% Customer experience management Brand or market managementColumnar RDBMS 23% Workforce planning and allocation 1 2 3 4 5 6 7 8
  18. 18. B i g D a t a Tr e n d s
  19. 19. Growth of Social Data 19
  20. 20. Best Use PatternsEnhanced Customer View• Internal Customer• “Digital Persona”
  21. 21. Big Data TrendsAppliance Big Data PlatformAppliances provide pre-certifiedplatforms : • Reduces time to implement • Allows the business to focus on Analysis not set-up and configuration • Less impact to internal Network Infrastructure
  22. 22. Tailored Cloud ServicesCloud-based Infrastructures• Low-cost, low-risk solution• Scalable without impact to internal networks and infrastructure• Great first step to “test the water”
  23. 23. Big Data Challenges
  24. 24. Big Data Challenges• Hard to quantify value to the enterprise• Data Scientists roles are difficult to fill• Difficult to design effective visualization and reporting of new data sets 24
  25. 25. Data Governance Data Governance Applies to Big Data Transactional & Collaborative Business Analytic Applications Applications Integrate Analyze Master Big Data Data www Data Warehouses Structured Manage Data Streams ExternalInformation Big Data Data Sources Appliances Content Streaming Information Security & Govern Lifecycle Privacy Quality Management 25
  26. 26. Effective Big Data Strategies
  28. 28. Effective Big Data StrategiesDispelling the Skepticism• Integration with existing infrastructure can be loosely or deeply integrated based on value and need• Leverage service providers and don’t be afraid to use existing talent to fill “Data Scientist” roles• Very real value for clickstream analysis, log file analysis and voice of customer (VOC) are quick wins (internal & external)
  29. 29. Effective Big Data Strategies Align Business Needs and Prioritize Quick WinsCore values for big data success: Leadership: Form big data Staffing: Skills in the operating- Find new value from existing data steering committee with executive platforms and systems to manage- Look for data from new sources sponsorship to drive consensus big data are essential- Learn to capitalize on social and align business goalscollaboration tools- Be customer centric - look at the Analytics: Leverage big data Implementation: Utilize adata from their view patterns; incorporate big data proof concept against a small- Business and technology technology and make current data business unit with deep domaincollaboration analytics and storage more flexible knowledge of analytics- Exchange value with proprietarydata sources- Center of excellence for analytics Network: Network layer and Performance Drivers: Set- Promote the capability enterprise dedicated segments need to be obtainable goals with incrementalwide optimized to work with velocity deliverables to avoid being requirements for “streaming overwhelmed by big data analytics”Be on the Lookout for… Distribution Maturity: Commercial distributions of Hadoop include Cloudera, MapR, Hortonworks, InfoSphere, BigInsights, EMC Greenplum HD, and others. Expect the Hadoop framework to be expanded and leveraged by many more technology vendors. Evolving NoSQL solutions such as Cassandra and Neo4j offer additional big data options.
  30. 30. Effective Big Data StrategiesBuilt around an optimized and integrated back office—one that leverages advancements in technology, global integrationopportunities and a continuous flow of data to cut costs, drive speed and further innovation. OUTSOURCING GENERIC FUNCTIONS PRODUCT INNOVATION ARCHITECTURE RENEWAL AND IT RENOVATION PAYMENTCONSOLIDATION BUSINESSS AND FINANCIAL REPORTING RISK SYSTEMS INTEGRATION
  31. 31. Consistent ChannelDrawing on marketplace insights and engaging customers as co-developers: • Tailor products and services on demand • Delivers through an ever-evolving and increasingly interconnected set of channels • Ensuring consistency across any channel is crucial Bank location MOBILE BANKING MICROFINANCE Phone ATM SOCIAL POINT OF SALE AS ATM NETWORKING Point Web of sale Mail
  32. 32. Effective Big Data StrategiesEnriched Data Improves Management Decision Making 32
  33. 33. Effective Big Data StrategiesFocus on Generating Customer Insights
  34. 34. Effective Big Data Strategies Sentiment Analysis / Voice of CustomerImprove the customer experience across Predictivechannels using:• Hadoop and other tools Analytics• Unstructured feedback and social data • Efficient fraud detection• Online customer surveys & online chat • Cross-selling of products and• Click-stream data services• Emails • Targeted advertising and marketing campaigns Social & Text • Customer loyalty and rewards programs Analytics • Effective business strategies and • Increase engagement to improve informed decisions acquisition • Reduce customer service response times • Adapt marketing and sales strategies • Track customer behavior and preferences • Engage with social influencers 34
  35. 35. Effective Big Data StrategiesBusiness-driven Support forYour Big Data Strategy • Business Assessment • Data Governance Assessment • Big Data Strategy & Roadmap • Technology Selection • Architecture Design • Cloud Services • Implementation Services • Big Data Analytics Support • Big Data Talent
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