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Elets BFSI CTO Summit - Technology Presentation - L Venkata Subramaniam, Senior Manager - Knowledge Engineering and Data Platforms, IBM India


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Elets BFSI CTO Summit - Technology Presentation on "How Cognitive Computing will Impact Banks and Financial Services" by L Venkata Subramaniam, Senior Manager - Knowledge Engineering and Data Platforms, IBM India

Key Themes

◆ Bankers’ role in technology ecosystem
◆ Latest technology analysis
◆ Building a digital platform for banks
◆ Ways to keep advance cyber threats at bay
◆ Defending against the unknown
◆ Blockchain revolution
◆ Payment Innovations
◆ Social Banking
◆ Optimising banking technologies : New Vistas
◆ Emerging technologies & impact on banking - benefits and challenges

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Elets BFSI CTO Summit - Technology Presentation - L Venkata Subramaniam, Senior Manager - Knowledge Engineering and Data Platforms, IBM India

  1. 1. Cognitive Finance L Venkata Subramaniam Senior Manager, Knowledge Engineering and Data Platforms IBM Research - India Cognitive Finance L Venkata Subramaniam Senior Manager, Knowledge Engineering and Data Platforms IBM Research - India
  2. 2. Financial Technology Timeline 1950s Credit Cards 1960s ATMs 1970s Electronic Stock Trading 1980s Mainframe 1990s Internet & eCommerce 2000s Online Banking 2010s FinTech Disruption (Cloud, Social, Mobile, Analytics)
  3. 3. New services are emerging and new markets are forming for banks Increasing regulation Emergence of new competitors (digital players) Inexperienced advisor workforce 59% of industry executives agree that increased regulation will force them to fundamentally change their business model 1/3 of firms globally spend at least a whole working day per week tracking regulatory change 1/3 of traditional bank revenues could erode from competition from non-banks by 2020 55% of bank executives view non-traditional players as a threat 25% churn in the advisor population 33% of the advisor population is new to their job (<2 yrs) in India Global focus on fee-based business 6 of 10 of the world’s top investment banks have announced strategies to focus on WM & AM and deprioritize IB, S&T 14.5% AUM growth over the last 5 years has been for RIAs fees
  4. 4. Optimize Offers and Cross Sell How can I anticipate customer needs and deliver more timely, relevant offers? Financial Risk Management How can I improve financial risk management to meet regulatory demands and achieve better performance? Fraud and Financial Crime Management How can I better predict, detect and investigate fraud and financial crime? Leverage Payment Insight How can I monetize payment information while lowering costs? Optimize Financial Performance Leverage Social Media for Customer Insight How can I gain new customer insight from social media data? Proactive Customer Service How can I anticipate customer issues and resolve them more efficiently? Operational Risk Management How can I better identify, monitor, and analyze operational risk across the enterprise? Improve Incentive Compensation Mgt. How can I optimize employee compensation to improve performance and increase satisfaction? How can I drive profitability and improve business flexibility? Create a Customer Focused Enterprise Drive Agility and Operational Efficiency Optimize Risk and Compliance Key imperatives and specific use cases where banks are focusing efforts
  5. 5. Imagine a World Where Data Is Simple What if… …so you could… all data from everywhere was available to all roles to drive insights and results shop for the right data as easily you shop for next mobile phone. you could quickly adopt and integrate the latest innovations into your systems to gain advantage use new open source technologies that prove valuable as easily as plugging in an appliance. you could use data anyway you want – from freedom of discovery to established reporting have the right balance between business flexibility and governance and security. you could rapidly launch new web and mobile apps and connect them to analytics so they could optimize behavior in real-time build new businesses based on that unique advantage. Imagine if you could do this … and drive your business based on deep insights
  6. 6. Technology is changing rapidly Breadth of Insights to Enable Decisions How can everyone be more right… ….more often? Descriptive Prescriptive Predictive Cognitive What has happened? What could happen? How can we achieve the best outcome? Tell me the best course of action? Business Value Insights
  7. 7. Banking in the Cognitive Era GrowthProfitabilityEfficiency Cognitive Operations Cognitive Analytics Cognitive Engagement Cognitive Operations Drives a simpler, leaner organization that can make faster decisions and is closer to customers. Cognitive Analytics Applies machine learning algorithms to mine big data for trends, real-time behaviours, predicted outcomes and optimal responses. Cognitive Engagement Aligns with a customer’s economic choices and optimizes the customer interaction and experience with the Bank.
  8. 8. Regulatory Compliance • Can XYZ small finance bank open up a new branch in location ‘L’? – Regulatory Constraints on Small Finance Banks – Is Location L an unbanked rural center or a prescribed district – What %age of branches are in unbanked rural centers 8
  9. 9. iAssist: Automated Customer Service Hello Watson, How are you? Good! How May I help you? I bought the home content insurance and I want to know if it covers for the damages caused by flood Yes, it does. The value of the goods protected depends upon the premium And, damages caused by lightening? No, it only covers against storm, rainwater, flood and wind Oh! How dearly is it going to cost me? Speech Acts Variations / Anaphora Ellipsis/ Context
  10. 10. Cognitive Assistant Issues successfully handled by Cognitive Automation Customer logs a ticket Customer calls IBM support Tickets Fall back to agent (Chat) Unsuccessful Agentsassistedby CognitiveAssistant At the Back In the Middle In the Front Ticket Management System • Monitors and Learns resolutions from Agent Chat • Identify actions/steps which can be automated • Disambiguates User context • Answers queries with resolution • Executes automated resolution • Automation Discovery and Execution • Automation Scheduling • Proactive Anomaly Detection • Answers queries with resolution Prevent Tickets / Problems / Alerts by Monitoring on Alerts / Warnings / Events 10 Application & Infrastructure Watson for IT Landscape for Cognitive Automation Strategy
  11. 11. Watson provides the opportunity to radically change the application support experience 11 News & Alerts News and alerts pertaining to the user that popped up in the system since the last time the user logged on. New KB Articles Knowledge Base Articles relevant to the area that this user works in, and related to the things that this user asked about. Personal Profile Work location, preferences, and other relevant information that provides context to the questions this user is asking. System Updates Upcoming system updates that have a possibility to affect this user’s workflow. Productivity Tips Brief tidbits of information that can assist the user in avoiding certain common problems observed by others around this user. Recommended online training based on patterns of questions asked Relevant Tickets Tickets relevant to the questions this user had in the past and that are related to events happening in the system. People People near this user, including super-users, who can assist with problems that this user is encountering. Virtual Agent Chat interface to provide answers to user queries and problems
  12. 12. The New World of Risk – Linking Credit Risk and Customers’ Reputations Structured Information Internal UnStructured Information External UnStructured Information Assessment Extraction Events Actions Intentions • Model Building • Heat Maps • Portfolio Mgmt Integration • Linkages • Trends • Sources • Locations Sentiments Customers’ Reputations Credit Risk KRIs Financial Losses Credit management Backtesting Segments, Financials, Transactions, •Text Analytics Mapping&Routing Analytics While eventually the early trends in unstructured data will surface in structured metrics. The trick is to capture early.
  13. 13. 13 We will use iterative approaches whilst dealing with data Identify Topics and Issues to monitor User defines analytical models (Rule Editor) Sources Issues “Only selected segments “snippets” of an article which discussed the intersection of the topic are selectedCo-occurrence Analytics Trend by monthTopic vs Entity Topic Classification SentimentClustering User interacts with data to discover insight new topics Dashboard Analysis Reporting Extract Blogs, Boards, News sources, Forums, Complaints, NGO’s, CRM, and Internal structured data Strong, Weak, Emerging Signal Alerts Trending and Sentiment Analysis Companies
  14. 14. Sectorial View Iron and Steel Market Government Global Oil & Gas Market Government Global E- Commerce Market Government Global Sector Wise Company Performances Iron and Steel ABC steel XYZ Steel MNO Oil & Gas OPQ RST UVW Oil Infrastructu re ABC FGH GHI
  15. 15. Natural Language Querying How have institutional investments changed in Alibaba since 2010? 100 million+ facts ~140,000 Company objects 350,000 Person Objects >30 relationship types Financial Ontology Financial Knowledge Graph Natural Language Query Semantically parse the NLQ into an Ontology Query (OQL) JSON response
  16. 16. Inferring Deeper Relationships between Customers • Find relationships between customers because it is not possible to accurately capture all relationships between customers (as many customers may not even choose to declare the same). • Find customers who share a common address • Find customers who have common nominee(s) • Customers who have standing instructions to pay one account from another • Customers who are directors / promoters of the same companies • Customers who share the same relationship with another customer (e.g., if Customer A is Customer B’s guarantor and Customer C’s guarantor then not only are Customer A and B related, and A and C related, the system must establish the relationship between B and C, • Aggregate network-level analytics • Locate customer’s with more than 10 common customers in their transaction network • Discover customer networks which have high volume of within-network transactions in a month Customer A Customer A XYZ Co. promotes Director
  17. 17. Community + Code Data Clients DataWorks family Web/Mobile Data Social Public Enterprise Data IoT IBM Offers a User-Centric Data Services Platform Business Analyst Tele/Web Agent Data Scientist Store, Provision, Govern User-CentricityAdvanced Analytics Wealth Advisor Compliance Officer
  18. 18. Thank You 