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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
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
The Financial Services & Insurance Value Chain
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
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.
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
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
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
AI and the Financial Service Segment
Artificial Intelligence is an evolutionary process
Evolution of Data Analysis
• AI/ Semantic Computing focus is around the Prescriptive Analytics approach
Gartner Descriptive Diagnostic Predictive Analytics**
Definition of Artificial Intelligence (1955)
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
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
AI Tools Available Today
Different Forms of AI
Where can AI assist Financial Services?
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
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
Semantic Computing in the Media and Research 2017
How Does AI tools Work – Data Flow review
How does Semantic Computing Work?
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
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.
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
26
A Relationship Query of RDF Data
Semantic Computing in Action
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.
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
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
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
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
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
Thank You…..
Questions ?

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AI and the Financial Service Segment

  • 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. 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. The Financial Services & Insurance Value Chain
  • 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. 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. 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. 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. 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
  • 10. Artificial Intelligence is an evolutionary process
  • 11. Evolution of Data Analysis • AI/ Semantic Computing focus is around the Prescriptive Analytics approach Gartner Descriptive Diagnostic Predictive Analytics**
  • 12. Definition of Artificial Intelligence (1955)
  • 13. 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
  • 14. 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
  • 17. Where can AI assist Financial Services?
  • 18. 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
  • 19. 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
  • 20. Semantic Computing in the Media and Research 2017
  • 21. How Does AI tools Work – Data Flow review
  • 22. How does Semantic Computing Work?
  • 23. 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
  • 24. 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.
  • 25. 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
  • 26. 26 A Relationship Query of RDF Data
  • 28. 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.
  • 29. 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
  • 30. 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
  • 31. 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
  • 32. 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
  • 33. 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