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AI and the Financial Service Segment

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An Executive overview on how AI and Semantic computing can improve the Financial and Insurance Industry

Publié dans : Logiciels
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AI and the Financial Service Segment

  1. 1. Artificial Intelligence and Financial Services Industry It’s not the big that eat the small, it’s the fast that eat the slow Presented to
  2. 2. Agenda • Challenges facing the Financial Services Industry • What is Artificial Intelligence, and where did it come from • What is Semantic Computing in AI • How can AI assist the Financial Service Industry • What steps should the Financial Services Industry adopt to get ready
  3. 3. The Financial Services & Insurance Value Chain
  4. 4. Three Challenges for Financial Services Industry • Interest Rates • Margins vs revenue • Regulatory Changes • Compliance cost vs safety net • Sharing Economy • Collaborative consumption • New Payment Options • Mobile wallets/apps, web sites • Evolved Customer Expectations • Anytime anywhere any device
  5. 5. Three Challenges for Insurance Industry • An Industry Ripe For Disruption • Structural Disruption • (self-driving cars, big data, and sharing economy) • New technologies • (Sharing economy (Uber/Airbnb) & coverage. • New Players • (with technology /innovative insurance products fill coverage gaps) • Control for the Consumer • Consumers financial focus • (high medical bills vs a budget) • Millennial Generation • (do-it-yourself approach, mobile technology) • New Platforms • ( increase transparency/efficiency through complex algorithms and big data.
  6. 6. What is FinTech? • Fintech is • the R&D function of financial services in the digital age • less to do with technology more to do with business model reinvention and customer centric design. • Fintech can be categorised as: • Traditional fintech as ‘facilitators’ with larger incumbent technology firms supporting the financial services sector • Emergent fintech as ‘disruptors’ with small innovative firms dis-intermediating incumbent financial services with new technology http://www.vertafore.com/Resources/Blog/what-is-insurtech-and-harness-disruptive-powers#sthash.CnTyw0WP.dpuf
  7. 7. What is InsureTech? • InsurTech is new technologies that are disrupting the insurance industry • Eg. smartphone apps, consumer activity wearables, claim acceleration tools, individual consumer risk development systems, online policy handling, automated compliance processing http://www.vertafore.com/Resources/Blog/what-is-insurtech-and-harness-disruptive-powers#sthash.CnTyw0WP.dpuf
  8. 8. Agenda • Challenges facing the Financial Services Industry • What is Artificial Intelligence, and where did it come from • What is Semantic Computing in AI • How can AI assist the Financial Service Industry • What steps should the Financial Services Industry adopt to get ready
  9. 9. Artificial Intelligence is an evolutionary process
  10. 10. Evolution of Data Analysis • AI/ Semantic Computing focus is around the Prescriptive Analytics approach Gartner Descriptive Diagnostic Predictive Analytics**
  11. 11. Definition of Artificial Intelligence (1955)
  12. 12. Key Word Search Vs AI/NLP Key Word • Keyword searches do not distinguishing between words that are spelled the same way but mean something different • Search tools still applies the same keyword pairing principles. So you get more refined bad results, not more accurate results. AI/Natural Language • Natural Language search systems focus on meaning and context in the natural way humans ask and offer answers • Natural Language is concept-based, it returns search hits on documents that are "about" the subject/theme you're exploring, even if the words in the document don't match all the words you query. https://www.inbenta.com/en/blog/entry/keyword-based-versus-natural-language-search
  13. 13. Why AI Now? • Market uncertainties drive simulation techniques to identify new growth opportunities • The 4 Vs(volume, velocity, variety and validity) of data provides new ways of thinking • High volumes and types of data (i.e. text, pictures, audio, video, blogs) is now accessed in real time, providing context for insightful decision making • Analytics technologies have matured & users’ expectations increased (user /domain-centric) capabilities https://www.linkedin.com/pulse/artificial-intelligence-insurance-virtual-reality-sabine-vanderlinden
  14. 14. AI Tools Available Today
  15. 15. Different Forms of AI
  16. 16. Where can AI assist Financial Services?
  17. 17. Where can AI assist Financial Services? • Machine Learning • builds algorithms to make data-driven predictions on behaviour/ patterns eg forensic analysis, predictive policing • Semantic Computing • understands the context and meanings (semantics) of computational content and expresses these in a machine-processable format • Natural Language Processing • focus on interactions between computers and natural human languages includes Semantic and sentiment analysis (social media) and cognitive customer experience space • Neural Networks • finds relationships among data points by allowing a system to “learn” new categories from collection of data perform predictions • Deep Learning • builds and trains neural networks that learns as it goes. Outputs are usually a predictions. • potential applications include enhanced micro-segmentation, intelligent pricing, prescriptive forecasting and augmented customer experiences https://www.linkedin.com/pulse/artificial-intelligence-insurance-virtual-reality-sabine-vanderlinden
  18. 18. Agenda • Challenges facing the Financial Services Industry • What is Artificial Intelligence, and where did it come from • What is Semantic Computing in AI • How can AI assist the Financial Service Industry • What steps should the Financial Services Industry adopt to get ready
  19. 19. Semantic Computing in the Media and Research 2017
  20. 20. How Does AI tools Work – Data Flow review
  21. 21. How does Semantic Computing Work?
  22. 22. What is Resource Description Framework (RDF) • RDF is a general framework for describing website metadata, or "information about the information“ • RDF defines a resource as any object that is uniquely identifiable by an Uniform Resource Identifier (URI) • RDF provides the framework for describing classes and properties in the form "subject", "predicate" and "object" • Enables computers to process data without needing to understand the structure of the data
  23. 23. Why does RDF work? • Integrates data from different sources without customer programming • It provides interoperability between applications and or machines • Develops relationships can be interpreted computationally, which enables the encoding, exchange and reuse of structured metadata • Data is stored in a Triplestore which is a purpose- built database for the storage and retrieval of triples through semantic queries.
  24. 24. Example of Industry Ontologies • An Ontology is a formal machine-interpretable definition of concepts in an area of interest (domain) • It describes the properties, features and attributes of those concepts, and highlights any restrictions • It describes the relationships between those concepts
  25. 25. 26 A Relationship Query of RDF Data
  26. 26. Semantic Computing in Action
  27. 27. Benefits of Semantic Computing ▪ Find more relevant and useful information ▪ Search information from disparate sources (federated search) and automatically refine our searches (faceted search) ▪ Better understand what is happening ▪ Utilise the relationships between concepts to predict and interpret change. ▪ Build more transparent systems and communications ▪ Based on common meanings and mutual understanding of the key concepts and relationships • Increase our effectiveness, efficiency and strategic advantage • Enables us to make changes to our information systems more quickly and easily. • Become more perceptive, intelligent and collaborative • Enables us to ask and answer questions we couldn't ask before.
  28. 28. Agenda • Challenges facing the Financial Services Industry • What is Artificial Intelligence, and where did it come from • What is Semantic Computing in AI • How can AI assist the Financial Service Industry • What steps should the Financial Services Industry adopt to get ready
  29. 29. Engagement Issues with AI • AI Technology will Augment and enhance Human Work. • AI Systems Still Demand considered Design, Knowledge Engineering, and Model Building • AI Technologies Demand New Skills, Not a New Team https://www.forrester.com/report/TechRadar+Artificial+Intelligence+Technologies+And+Solutions+Q1+2017/-/E-RES136196
  30. 30. How would AI assist Financial Services Industry • Marketing: • NLP using sentiment analysis, machine learning or pattern recognition better understand their customers’ needs, • Design unique engagement journeys as well as promotional campaigns. • Intelligent Pricing: • Combining a variety of relevant data sources with clever pricing and optimisation engines. • Pattern recognition, deep learning techniques to identify fraudulent behaviour • Claims Management: • Machine Learning/ Deep Learning using algorithms accelerate claims assessment and identify claims leakages , reducing costs & improving the customer engagement. • detection of new sources of claims fraud • design really remedial and preventative actions. http://aitegroup.com/how-financial-services-can-benefit-artificial-intelligence
  31. 31. Steps to Start • Develop a Data Strategy • Legislations, Clients, Processes • Capture clean, regularised data • Structured and unstructured • Source relevant human capital skills • Industry segment trained, IT literate • Specialist in Data & Analytic tools • Develop environment for Linked Data and Analytics to grow
  32. 32. Key Questions for AI project? • What’s the best use of AI for your Organisation • What are your present and future business needs? • How does AI support your bank’s strategic objectives? • What tasks could be automated to optimize processes/ staffing? • Do you have a specific AI project ? • Do you have a sponsor? • What is the status of the data required? • Do you know what data is required for analysis? • Do you have access to this data? • Can this data be structured for your AI’s algorithms? • Would additional data source improve the analysis? • Do you have plans to source missing data? • AI Project Funding & Resources? • How will you measure the project’s ROI? • What is the acceptable cost of a proof of concept? • Do you have enough funding for an AI project? • Does your technology team have the bandwidth to for an AI project? • Does your team have skills/IT platform capable of developing AI project? • Do you have an executive / technical team to manage AI project? • What is your AI implementation plan? • Can you develop target deadlines for the pilot and launch? • What are the next steps for your AI strategy after this pilot Developing a Data Management Platform
  33. 33. Thank You….. Questions ?