cognitive computing in insurance

© 2015 International Business Machines Corporation

Cognitive Computing - in insurance Forsikringsselskabernes PlanlægningsUdvalg - January 26, 2014 Anders Quitzau Innovation Exec & Watson Advocate


Agenda

•  Cognitive Computing and Watson overview

•  Watson for Insurance and Financial Services

•  Q&A - discussion


Tabulating Systems Era

Programmable Systems Era

Cognitive Systems Era

1900 1950 2011

Cognitive Computing is a new era of computing


Cognitive computing promises a new partnership between human and computers

Scale expertise by elevating the

consistency and objectivity of decision making across an

organization.

Enhance the cognitive process of

professionals to strengthen decision making in the

moment

Accelerate development of expertise in

others by capturing the expertise of top performers


Cognitive systems expand the problems we can address

Programmatic Systems Cognitive Systems •  Leverage traditional data sources •  Follow pre-defined rules (programs) •  Provide the same output to all users

•  Are taught, not programmed •  Learn and improve based on experience •  Interpret sensory and non-traditional data •  Relate to each of us as individuals •  Allow us to expand and scale our own

thinking


Cognitive systems learns and evaluates like we do

Study Apprentice

Practice Master

Ingest Learn Test

Experience


IBM’s answer to the new era, Watson, is a cognitive learning system that enhances our abilities to perceive, reason, and relate

Perceiving: Watson understands the world as we do: it interprets sensory input beyond traditional data

Reasoning: Watson thinks through complex problems: it deepens our analysis and inspires creativity

Relating: Watson understands how we communicate, and personalizes its interactions with each of us

Learning: Watson learns from every interaction, scaling our ability build experience


Why is it so hard for computers to understand us?

Person Organization

L. Gerstner IBM

J. Welch GE

W. Gates Microsoft

“If leadership is an art then surely Jack has

proved himself a master painter during his tenure

at this company”

Welch ran this?

§  Noses that run and feet that smell? §  How can a house burn up as it burns down? §  How can I fill in a form that I am asked to fill-out? §  Where is Paris Hilton?

NLP

Machine Learning


Answer Scoring

Models

Responses with Confidence

Inquiry

Evidence Sources

Models

Models

Models

Models

Models Primary Search

Candidate Answer Generation

Hypothesis Generation

Hypothesis and Evidence Scoring

Final Confidence Merging & Ranking Synthesis

Answer Sources

Inquiry/Topic Analysis

Evidence Retrieval

Deep Evidence Scoring

Learned Models help combine and weigh the Evidence

Hypothesis Generation

Hypothesis and Evidence Scoring

Inquiry Decomposition

How Watson Works

1000’s of Pieces of Evidence

Multiple Interpretations of a question

100,000’s Scores from many Deep Analysis Algorithms

100’s sources

100’s Possible Answers

Balance & Combine


R&D Demonstration Validation

Research Project

2006 – 2010

Jeopardy! Grand Challenge

2011

Internal Startup Division

2011 – 2013

IBM Watson Group

2014 – present

Commercialization •  $1B Investment •  $100M for Ecosystem •  NYC Headquarters

Watson has graduated from a startup to a full-fledged business


IBM Watson Advisors

IBM Watson Solutions

IBM Watson Foundations

IBM Watson Cognitive

Fabric Provides the big data and analytics capabilities that fuel Watson

Products based on Watson’s core attributes and capabilities

APIs, tools, methodologies, SDKs, and infrastructure that fuels Watson

Customized solutions designed to meet some of industries most demanding needs leveraging cognitive capabilities

IBM Watson Ecosystems

The Watson Developer Cloud, Watson Content Store and Watson Talent Hub driving innovation from partners

The Watson Family


Agenda





Customer expectations are shaped by experiences outside of Insurance Pressure of competition is putting a lot of focus on customer experience

Advisors and Managers are challenged to keep up with data explosion Customers are informed better than ever with “Google knowledge”

Customers expect transparent, evidence-based recommendation

Quality of advice varies

Pace of innovation is accelerating

Regulations increasing in volume and complexity Additional scrutiny increasing time spent on compliance, and cost

Insurance industry trends – Imagine if… Client Engagement Analysis / Discovery Decision Policy / Compliance

Watson enable

s advisors to

deliver cost-‐

effec2ve

personalized

advice Watson

supports An

alysts and

advisors by p

re-‐reading 1

000s

of pages of r

elevant info

Watson uses s

tructured an

d

unstructured

informa2on t

o

provide proa

c2ve, transp

arent,

evidence-‐bas

ed advice

Watson helps e

valuate

decisions aga

inst relevant

policies & gu

idelines


Financial Services solutions map into “patterns”

Engagement

Discovery

Policy

Decision

Explore / analyze

Pattern Description Client success stories

Leading customer service firm, USAA, introduced Watson to answer questions from members

Digital engagement with clients to answer questions, execute actions, and provide recommendations

Leading drug discovery firm, Baylor, publishes breakthrough in protein targeting Explore questions not yet answered

Memorial Sloan Kettering Cancer Center partnered with IBM to build an Oncology solution Help evaluate possible options to come up

with the right decision

IBM partnered with Wellpoint to accelerate pre-approval decisions

Help understand if a particular action is compliant with relevant policies

700+ clients using the product across industries Search, consolidate and analyze enterprise data


Insurance and Financial Services solutions map into “patterns”

Engagement

Discovery

Policy

Decision

Explore / analyze

Pattern Example solutions

Watson Insurance Advisor

Watson Discovery Advisor for Pharma

Watson Oncology Advisor

Watson Policy Advisor for insurance pre-approvals

Watson Explorer/Watson Analytics / Watson Content analytics (x-industry)

Watson for Wealth Management

Watson Discovery Advisor for Gov’t

Watson Discovery Advisor for financial services (emerging)

Watson for Regulatory Compliance (emerging)

Watson Engagement Advisor (x-industry)

Watson Policy Advisor for Clinical Trial Matching

Watson for Retail Banking

F & I services solutions

1 3

4

5

2


Self-service User Experience Journey

Here is what you are looking for

Here it is. Shall I complete the task

for you?

Have you thought about this?

How did it go? Can I assist with these

next steps? Knowledge Expert

Empowered Assistant Personal Advisor

Ask a question Get answers Ask a question Get answers Get action

Ask a question Get answers Get action Get suggestions Get follow-up


Demo


USAA case study

IBM’s Watson Engagement Advisor will answer military separation-related questions on topics such as job searching, home purchasing, military benefits and more.

Members "Ask Watson" as they prepare to transition to civilian life


Policy Pattern


Example use case: Medical procedure approval by Healthcare Payer

Decision Input Case ‘Policy’ Policy Advice Expected

Can I approve the procedure being requested?

Clinical data (structured and unstructured) from a provider’s office requesting a procedure

Payer medical policies and guidelines

•  Approve, medically necessary

•  Reject, not compliant with policies

•  Need MD review, ambiguity in request / policy (specify what information is needed)


Multi page unstructured text case input

Structured data as part of case input


1.  Identify the applicable policies 2.  Evaluate the case against appropriate criteria

from the policies

Identify relevant evidence


Generate summary of case evaluation


Combination of multiple patterns


Watson Wealth Management for DBS Banking clients at DBS will soon get investing advice from Watson, the same IBM technology that triumphed over human “Jeopardy!” contestants. The Singapore based bank plans to begin using the tool, which can answer questions in conversational language and learn from responses, in the second half of 2014 to aid financial planners in guiding its wealth management’s units affluent customers. Watson will analyze large volumes of financial data to help DBS, Southeast Asia’s largest lender, offer more customized service.

Watson will throw up its suggestions after “ingesting” thousand of research reports, product information and customer profiles, more than what a wealth management expert can handle handle, DBS said.

DBS will also leverage a comprehensive portfolio of IBM solutions which include business consulting, advanced analytics, hardware and IBM Research assets to further augment the power of Watson . By bringing together all of these capabilities, DBS will be uniquely positioned as the first bank in Asia to execute a project of this kind.

33


Watson for Wealth Management helps wealth advisor better serve their clients (advisor dashboard)

Key upcoming activities

Client list

Relevant alerts affecting clients

Relevant news

Ask Watson questions in

natural language


Watson for Wealth Management helps wealth advisor better serve their clients (client-specific dashboard)

Customer-specific view Relevant alerts

Client-specific product

recommendations

Ask Watson questions in

natural language


Create personalized recommendation using broad combination of structured and unstructured data (recommendation rationale screen)

Relevant info about the asset Match to client

preferences

Positive outlook from multiple

analysts

Overall high confidence in

recommendation


This will be Watson

Sees

Hears

Experiences

Understands natural language

Generates and evaluates hypotheses

Adapts and learns

Reasons

Explores

Visualizes


If we really want to augment and scale human recognition, we need to re-invent how we compute: IBM TrueNorth ‘brain-chip’ may be the answer….


Agenda





http://ibmwatson.com eller kontakt mig på [email protected] 2880 4705

cognitive computing in insurance

Technology

watson overview watson

watson works

cognitive learning system

new era of computing

learned models

traditional data sources

computers scale expertise

traditional data reasoning