Banking on Big Data: Harnessing Big Data to drive valuable BigDecisions


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Volatility in the banking and financial services markets has become a constant. Controlling costs, while being flexible enough to react to changing global demands is critical. With new regulations, mergers and other forms of uncertainty sweeping markets, monitoring and reducing risk is a business imperative. Demographic trends such as the rise of social media and the growth of emerging markets are creating opportunities. Yet, the cost and risk of acquiring good customers remains challenging. Surviving and prospering in these challenging times demands excellence at every point of operation. From achieving governance, risk and compliance to cross channel optimization, harnessing Big Data can help companies run better and run different. Whether its driving innovation or delivering new efficiencies, Cognizant’s BigDecisions2.0TM, a new paradigm for seamless, end-to-end information management & analytics value is redefining the way companies can benefit from Big Data. Learn how to unlock and manage market opportunities presented by Big Data better with Cognizant’s BigDecisions2.0 Business solution platform.

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  • 1: (of the 720 organisations surveyed by Gartner)
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  • Banking on Big Data: Harnessing Big Data to drive valuable BigDecisions

    1. 1. Banking on Big Data: Harnessing Big Data to drive valuable BigDecisions Ian West Head of Enterprise Information Management
    3. 3. | ©2014, Cognizant Cognizant From internal consulting unit to a market leading Global Services Provider 1223+ Active Customers $8.84 Billion 2013 178,600+ Employees Globally 20, 000+ Projects in 40 countries 25+ Regional Sales Offices 75+ Global Development Centres Financial Services: 42.3% Healthcare: 25.4% Retail, Manufacturing & Logistics: 21.1% 20.4% 3
    4. 4. | ©2014, Cognizant4 Emergence of Big Data and Analytics Increasing belief in its potential to create competitive advantage of banking organisations globally have invested in big data1 >34% Between 2012- 2017, the uptake of big data analytics amongst larger enterprises in the UK will more than double to ~30% of organisations 3 30% of large global companies will have adopted big data analytics for at least one Security or Fraud Detection Use Case by 2016 (up from 8% today) 2 25%
    5. 5. | ©2014, Cognizant5 Big Data and Analytics drive business value “Leveraging the power of big data & analytics to drive valuable big decisions” Business value Big Data & Analytics Traditional Analytics & BI  Improved understanding of customer  Information based business decisions  Deeper insight into risk  Increase revenue  Reduce cost  Mitigate risk and ensure compliance  Additional data sources to enrich customer profiles  Variety of unstructured information to better understand context  Real time analysis  Structured & transactional customer data  Ad hoc & retrospective pattern analysis
    6. 6. | ©2014, Cognizant6 Corporate Listening - Voice of our customers Identify the right customer for the right product at the right price at the lowest risk to improve revenue and profitability Deal with aggressive and innovative non-bank competitors by leveraging data as an asset Develop new and reliable sources of revenue & increase business value of customer relationship through analytics Incorporate mobile banking as a regular delivery channel & develop a strategy around social media to personalise engagement with customers Achieve & monitor regulatory compliance across Line of Businesses and Business Functions
    7. 7. | ©2014, Cognizant7 Big Data & Analytics can be leveraged across multiple areas in the Financial services industry  Improving branch & channel efficiency and effectiveness  Helping to drive high value, high touch traffic back to branchesCustomer Centricity  Improved targeting of customer segments  Moving from a product to customer focus  Better management of sales leads across channels  Inclusion of customer incentives to influence behaviour Reduce Costs & Increase Revenues  Branch, ATM  Online, Mobile  Omni-channel  Channel management & integrationEveryone’s Mobile  Sentiment analysis  Social media analysis  Credit analysis  Customer profitability & lifetime value  Predictive analytics Customer Insight  Ability to process increased volume & variety of data  Cost effective technologyTechnology Advancement  Risk & capital management, Risk adjusted pricing  Portfolio risk management, Fraud/AMLRisk & Compliance Management
    8. 8. | ©2014, Cognizant8 The Data You Need is Everywhere Around You! Big Data and Analytics Opportunity in Banking To deliver the best customer experience, banks need to • Augment internal data with external data assets to enrich customer profile • Provide customized products and services • Personalise communication in a timely, relevant and impactful manner Customer profile integration Customer experience analysis Customer preferences & behaviour analysis Customer sentiment analysis
    9. 9. | ©2014, Cognizant Big Data and Analytics Opportunity in Retail Banking Breaking Siloes and Analysing Raw Data from Multiple Sources 9 Example Outcomes Profile Contact History Transaction Models Big Data Analytics Customer View Integrated Web Intelligence The Web Visit • What searches? • How did they get you? • Page navigation Research • What do they look at? • What do they search? • Do they dig deeper? Purchase Path • Which product? • How far into the process? • What’s looked at during purchase? Social Media Verbatim • Blogs • Tweets • Postings • Reviews Internal Text Data Research • Verbatim • Ad-hoc • Longitudinal studies • NPS/Satisfaction Other Direct Contact • Branch interview Records • Branch Enquiries • Manger Notes • E-Mails Call Center • Queries • Complaints • Service issues Micro-segmentation Higher Quality Leads Better Fraud Detection More accurate Propensity Models Multi-channel Customer Sentiment
    10. 10. | ©2014, Cognizant10 Changing Regulatory Environment Financial organisations’ leverage Big Data Analytics Cost-reduction programmes, de-risking & price adjustments Manage ROI in the new environment Reduce capital and liquidity inefficiency Balance-sheet restructuring Business-model adjustments Achieving compliance with evolving regulatory norms Strategic planning for the BASEL III world Capital & risk strategy Implementation management Challenges Response Compliance
    11. 11. | ©2014, Cognizant Risk Management Office (RMO) 11 Predict risk & provide guidelines A scientific, intuitive statistical model Risk Cataloging Risk Analytics engine Unique risk management model Identify the right project non-invasively ‘Entry Strategy’ Solutions ‘Account Expansion’ Solutions Rules-based project evaluation engine Enhanced data quality pertaining to schedule, testing and effort Help create a model for future engagements Integrated scientific, statistical & human intelligence to predict risks Single authority for proactive Risk Identification for existing & new age delivery methods taking a holistic view across an engagement lifecycle. Vision RMO - One stop shop for risk expertise through proactive risk identification, tracking & mitigation of program risk including financial, customer, execution, governance, solution and stakeholder management
    12. 12. | ©2014, Cognizant Risk Profiling Big Data Use Cases in Financial Services 12 Business ImpactBig Data Capabilities Customer Churn  Timely prediction and reduction of churn  Include customer contact (e.g. call centre transcripts) & social media data  Analyse customer sentiment  Model and score churn propensity Cross and Up-selling  Efficient and precisely targeted marketing  Increased Cross- and Up-sell  Analyse & model response behaviour  Select campaign addresses based on micro-segmentation Data Offloading  Performance & scalability Increased performance and storage  Enabled power of analytics on wealth of data Segmentation  Client lifestyle analysis and spend prediction  Increased customer satisfaction  Advanced analytics to enhance client lifestyle analysis and profiling  Predictive analysis of spend Domain  Comprehensive risk profiling  Improved risk evaluation  Refine risk profiling models frequently to adapt to dynamic business environment
    13. 13. | ©2014, Cognizant Introducing BigDecisions2.0TM 13
    14. 14. | ©2014, Cognizant BigDecisions2.0 Business Solution Platform Components providing agile delivery through focused business apps 14 Robust Core Platform Acquire, Manage and Use Any-Data Rapid Value Delivery Flexible, Agile & Economical Relevant Business Apps Intuitive, Focused and Bite- size BI & Analytics
    15. 15. | ©2014, Cognizant BigDecisions2.0 Business Solution Platform A new paradigm for seamless, end-to-end information management & analytics value 15 Sophisticated BI & Analytics Leverage Universal Data Select proven Technologies Agility for Business Change Easy to Build and Manage Spend time on BI & Analytics, where it matters most (not on building infrastructure) Manage all- structures of data with Universal Data Management Deliver subject areas in weeks, not in months or years Faster business value realisation with proven set of technology options Install, configure & customize, don’t develop
    16. 16. | ©2014, Cognizant Business App | Risk, Fraud & Compliance 16 • Executive Dashboards around BASEL II/III and Adaptive Revenue Assurance • Machine-learning modules for fraud detection and to strengthen entry to the real-time analytics market • Predictive analytics and new features to cover areas in risk and governance prediction • Smarter fraud detection capabilities reduce losses and improve recoveries • Direct fulfillment of all CRO needs, providing them with business discovery tools and services • Proactive risk management across LoBs and product lifecycles with stress testing and scenario analysis RFC Data and Analytics Platform • Flexible systems and processes to accommodate changing regulatory requirement Holistic risk assessment, fraud detection and compliance application that ensures adherence to constantly changing regulatory requirement What? App Features Benefits
    17. 17. | ©2014, Cognizant Representative Experiences 17 Fraud Detection & Prediction @ Global Payment Processing Company • The accuracy of the fraud detection process was improved (~15%) and the speed of >400 million transactions • Detect frauds within seconds and predict frauds within 8/16/32/48/72 hrs Enhance fraud detection and prediction algorithm  Historical data set was stored in a Hadoop cluster (100+ nodes)  Ran several algorithms to prepare clustered data  Neural network algorithm was developed Real-time Reporting @ Leading Financial Services Company • Response time was significantly improved • Substantial performance gains were realised in data service aggregation scenarios by reducing the number of data service calls from RTM  PoC for conversion from Cognos to BOBJ  Infrastructure – Installed Hadoop, HBase, MySQL Performance tuning  NoSQL database for real-time service  Big data archive management for cost effective archival and retrieval • 20% improvement in lead conversion • Operational cost savings of 10 – 20% • Greater NPS and customer satisfaction  Captured last 55 years of customer data involving 23 Million Customers, 13 Million Policies, 60 Million Claims, 8500 Active Products  Segmentation of customers leveraging machine-learning techniques  Churn analysis at each individual cluster level with combinations of net-worth, transaction volume and churn rate Customer Segmentation and Churn Prediction @ Leading insurance major
    18. 18. | ©2014, Cognizant In Summary 18 BigDecisions Business Solution Platform: A platform-based approach to Universal Data Management with a suite of business ready analytical apps  Big Data and Analytics is a key driver in the financial services sector to help businesses run better & run different Start small, think big. ROI on Big Data and Analytics is often too big to ignore
    19. 19. | ©2014, Cognizant19 BigDecisionsTM Business Solution Platform Thank You
    20. 20. | ©2014, Cognizant Big Data Solution Frameworks & Platforms 20 Pave the way to success Scorel Stock price analysis with prices, market feeds, analyst quotes iLASER Longitudinal Analysis for Customer behaviour GReco Graph DB based high performance recommendation engine MIPS/DW Offloading to Big data platforms for performance and cost benefits Delivery through BigDecisions 2.0 Platform – A Holistic Approach to EDM 2.0! Industry Use Case Libraries Solution Components Reference Architectures Proof of Value Technology Frameworks Proof of Concept Big Data Analytics Value Assessment Framework (BAVA) SmartNode Unified access to big data platforms for BI/Analysis tools iSMART Integrated Social Media Analytics and Reporting PayNet Graph Analyse Payment networks for Risk, Fraud and targeted marketing Churn Analysis using big data and machine learning