AI for Insurance Leaders

About Institute for Experiential AI

The Institute for Experiential AI at Northeastern University researches and develops humancentric AI solutions that leverage machine technology to extend human intelligence.

WHAT IS EXPERIENTIAL AI?

Successful AI is not “set it and forget it.” To create real impact, AI must prioritize the human experience and utilize a human in the loop to consistently evaluate and improve its outcomes.

ADVANCING AI THROUGH APPLIED WORK

We believe the biggest breakthroughs in the art and science of AI have come and will come through applied projects with real-world data that generate tangible results.

POSITIONED FOR OUR PARTNERS’ SUCCESS

The Institute for Experiential AI exists at the intersection of academia and business. We deliver ROI while integrating industry expertise and talent pipelines to deliver unique value to your most pressing AI challenges.

AI and Data in Insurance: Overview

Usama Fayyad, Executive Director, Institute for Experiential AI at Northeastern University and Professor of the Practice, Khoury College of Computer Sciences

Fireside Chat and Case Study Discussion

David Messinger Director and Associate Actuary, Sun Life U.S. and Usama Fayyad

Today’sAgenda

Responsible AI Governance for Insurance Companies

Ricardo Baeza-Yates, Director of Research and CoChair, AI Ethics Advisory Board, Institute for Experiential AI at Northeastern University

Climate & Weather Modeling in Insurance

Auroop Ganguly, Distinguished Professor of Civil and Environmental Engineering, Northeastern University

Director, AI for Climate & Sustainability (AI4CaS), Institute for Experiential AI

Moderator: Kevin Sanborn

Business Lead, Insurance and Financial Services

Institute for Experiential AI

AI and Data in Insurance Overview

Usama Fayyad, Executive Director, Institute for Experiential AI (EAI) at Northeastern University

Professor of the Practice, Khoury College for Computer Sciences, Northeastern University

Education

Large Orgs

Goal: Make AI and Data usable, useful, manageable - democratize the responsible use of AI across fields

Education

Startups

● Ph.D. Computer Science & Engineering (CSE) in AI/Machine Learning

● MSE (CSE), M.Sc. (Mathematics)

● BSE (EE), BSE (Computer Engin)

Academic achievements

● Fellow: Association for the Advancement of Artificial Intelligence (AAAI) and Association for Computing Machinery (ACM)

● Over 100 technical articles on data mining, data science, AI/ML, and databases.

● Over 20 patents, 2 technical books.

● First in industry: Chief Data Officer at Yahoo!

● First Global Chief Data Officer & Group Managing Director at Barclays Bank, London

● Chaired/started major conferences in Data Science, Data Mining, AI

● Founding Editor-in-Chief on two key journals in Data Science

AI and InsurTech: Why

Muchofdigitaltransformationhasfocusedonworkflows,butlittleadvanceoncustomerexperienceand interactions

Front Office

Need to scale the human intelligence/ understanding in customer interactions

Back Office

Still expensive and complex and needs intelligent automation

New Frontiers

Going beyond human servicing capabilities

● Understanding customer context

● Understanding intent, challenges, and issues

● Reduce costs of customer service operations (chatbots, problem understanding, problem resolution)

● X-sell, up-sell, reactivation, nextbest-action, etc… at scale

● Compliance

● Claims processing: speed and cost

● “Operations” – e.g. forms into actions

● Step counters linked to insurance risks

● Tracking safe driving with IoT

● Leveraging networks – social and otherwise, to embed financial transactions

InsurTech: Why is AI necessary

Scaleis a must – humanprocessingisnotscalableorfeasible

● AI algorithms are only feasible approach to deal with “understanding” – of context and customer

o Leverage human judgement in delivery to build the right training data sets and KB

o Need to make sure AI algorithmss are subject to Responsible AI criteria (often overlooked)

● Complexity of products requires “reasoning”: still a big challenge in AI

● Multiple ways of recognizing identity: vision, fingerprint, voice recognition, keystroke analysis, etc.

InsurTech: Why is AI necessary

Newcapabilitiestech/networkrequireautomation

● Novel risk and credit scoring opportunities

● Integration into microservices

● Leverage networks (e.g. social networks) and other viral services

● Modeling risk and future scenarios bettersuited for algorithms – aslongasalgosare fair/responsibleAI

o New ways of scoring risk, credit, and needs

o Leveraging all the information available publicly – e.g. LinkedIn and many other networks

o Better understanding of behavioral data

InsurTech: Some of the Big Challenges for AI

Fairnessandbiasinalgorithmscanhavebigconsequences

● Financial decisions are much more consequential than e.g. targeting ads

● Algorithms have little commonsense reasoning (or any reasoning) –typically all they know is data (with almost no context)

● Modeling complex decisions and consequences is still a hard problem

● Advances in tech and data enable potentially deep intrusion on privacy and civil rights

SuccessfulAIisheavilydependentonML/DataScience, henceneedgoodtrainingdata:Dataremainsahuge challengeformostorganizations

● Good training data is extremely expensive to get

● Just collecting and managing raw data is a challenge for most; growing exponentially with digitization, cloud, and IOT

● Data manipulation is very difficult, few understand unstructured data

Is There a Human in the Loop (HITL) in ChatGPT?

Much speculation about pure AI (AGI) or much human intervention?

• Human editorial review is applied

• Some questions are answered by humans

• This is actually a best practice – we call it Experiential AI – many do it:

• Google MLR

• Amazon recommendations

• Many intervention-based relevance feedback

• Curating “just the right training data” for the GPT3 LLM is a costly human-driven activity - essential to LLM performance.

https://mindmatters.ai/2023/01/found-chatgpts-humans-in-the-loop/

Digital Transformation - Financial Services

● Financial Services has been undergoing major digital transformations for the last 7 decades

● FinTech/InsurTech is a term that represents the super-accelerated tech transformation of the last 2 decades

● AI has gotten a lot of attention in recent FinTech/InsurTech efforts

Simple Example of dramatic transformation in traditional finance: Accounting

○ Think about Accounting of 60 years ago vs today

○ Excel completely replaced many old jobs and “technologies” (like Index cards) but created many more higher value jobs

○ Production chain, supply chain, and inventory management has been completely changed

Digital Transformation - Data is Core for Success !

Failure to Survive Excelling with Data

Incentive-based solution lead to:

● Kodak had a business built on intimate customer relationships

○ Customer goes to store to purchase film

○ Returns to drop off film

○ Returns again for prints

Theshifttodigitaltechnology eliminatedthisecosystem

Kodak invented the digital camera - but was too late to release their own

2012 Bankruptcy

@ 124 years old

Losses double as digital transformation remains at the "thoughtful" stage 2015-2019 competitors embraced on-line and invested in it

2019: revenues drop 5.6%

COVID-19 forced the resolution

2020 Bankruptcy

@ 118 years old

40% reduced cost in hospital admissions

14% cost reduction per patient

25% reduction in hospital stays

Customer interactions data allowed increase its customer retention by 87%

> 80,000 big data experiments a year

> 70% prediction accuracy

83% cost reduction of customer acquisition in 2 years

Vitality integrates with over 100 wearable technologies

Discovery launched its car insurance product (Similar to Drive Safe and Save) in 2011

$4.7M medical cost savings

By 2012 - DBS was considered a failing bank. Start Digital Transformation in 2013

By 2017, application costs reduced by over 80%

Record net profit of SGD $4.39B income up > 4% over last year

9% CAGR in income and 13 % in net profit

> 10,000 training sessions by DBS Academy yearly

-

AI Enablement top priority now

“Ithinkit’sabitofafool’serrand…to chasedigitalforthesakeofcost reduction”

RichardFairbank,CEO

AI Transformation will Super-Charge the Digital Transformation

MajoreconomicimpactfromAIandGenerativeAI

Knowledge worker tasks - 15% to 80% acceleration - an efficiency driver → new types of jobs

Newprocessesfromcustomeracquisition,toretention,toservicing,togrowth

Leveraging BigData and new information sources - Data assets drive new models

Dataoncustomersandbehaviors

○ DataontheWorld - e.g.climateandsustainability

● New ways of risk modeling and assessment

○ Anewgenerationofriskmodelsandstrategiesareenabled

○ Anewgenerationoffrauddetectionandcounter-measures

● A deeper understanding of the customer

○ Consumers,businesses,enterprises

Example AI Use Cases in Insurance

Underwriting, Pricing & Risk

● Underwriting & pricing model optimization

● Use of 3rd-party and unstructured data

● Climate and catastrophe modeling

● Risk modeling and scenario analysis

Customer Service & Personalization

● Dynamic customer profiling & segmentation

● Product recommendation engine

● Intelligent call routing and service intervention

● Agent support

● Chatbot service augmentation

Responsible AI

● Data ethics & privacy

● AI governance

● Model bias audit

● Model transparency and explainability

● Training

Claims Process Improvement & Automation

● Document capture & review

● Claims fraud detection

● Claims scoring and triage

● Intelligent IBNR & Reserving

Product Development

● Parametric Insurance

● Extracting insights from call/chat logs to drive customer insights and new product ideas

New Models of the World

● Climate prediction

● ESG and Sustainability

● New data sources on everything - hyper local and extremely granular

AI is already transforming ‘Digital’ into ‘Smart Digital’

ChallengersusinginnovationsinData,AIandDigitaltodisruptInsuranceindustry

Online Only Insurance Providers - Zhong An is the first online-only insurance provider in China, and since 2013 has sold 7.2 billion insurance products to 429 million customers. DataandAIforonboarding, risk,fraud,underwritingandclaimsprocessing.

Insurance company that’s also hardware company - Neos provides tech that makes gas leaks, water damage and home intrusions less likely, they pass those savings in the form of lower premiums to their customers. AIforinterceptionofissuesandChatbotforcustomerinteractiontoavoidlossandclaims

• Digital, self serve buying process - In January 2017, the life insurance startup Lapetus made headlines by offering a service for people to buy life insurance using a selfie. Biometricanalyticsusedforrisk profiling,coverageandpremiumusingAIandML,enablingdigitalflow.

• Faster, customized claim settlement - Lemonade’s AI Jim made headlines in January 2017 by purportedly settling a claim in less than three seconds. Chatbot,AIforfraudandclaimprocessing.

Example: Automated Claim Processing in Auto Insurance

Improve customer satisfaction and reduce fraud by reducing manual touchpoints in the end-to-end process

User records video footage of the damaged car and uploads on the App

Based on the previous recordings, app identifies the damage

Real time claim assessment is provided to the customer

Customer approves the assessment

Near by service Centre details are provided and claim settled.

Video is stored in the central location

• Tagsgenerated

• Basicmetadata collected

• Entityextraction

• Entityrelationship

• Video Analytics

• Entity Resolution

• Video/Image comparison

Customer 360 provides the Claim history

Real time decisioning engine

• Case flow management system

• Captured document proof for future reference

Unintended Consequence of Digitization: LostCustomerIntimacy

Traditionally, businesses relied on employee interactions to understand (formally or informally)

Digitization of workflow → can no longer know:

● Are customers happy ?

● Are services and products being delivered effectively ?

● Why are customers leaving us ?

● Where are they going ?

● What is making customers unhappy ?

● What is delighting them?

How can we restore this intimacy on the digital channels?

Moving from Transactional View to Intimate Intent Understanding

IllustrativeExample: consider a typical Insurance transaction

Kelly buys new home insurance coverage with her husband

What the insurance agency knows about Kelly

● Kelly buys new home insurance

● Kelly has good standing with the agency

End of Story...

Hadtheagencysynthesizedvarious interactionsandbuiltanunderstandingof Kellyandherintents -

What they could have known

● Kelly just got married

What they could have inferred

● Kelly just started a new family

● Kelly is thinking of buying a new bigger car

What they could have done

● Insurance agency could have offered home and auto insurance bundling offers

● Insurance agency could offer better rates for cars with certain dealers

Digitization Produces 100x the Data Flux

Butmostbusinessesarenotequippedtoeffectivelymanagedataasanasset

How do we make this Data work for the business?

New economy of Interactions is rich with unstructured data in fact, 90% of Data in any organization is UNSTRUCTURED Without proper Data, AI cannot work: ML needs high quality and granular training data

Usama Fayyad Executive Director, Institute for Experiential AI

Professor of the Practice, Khoury College of Computer Sciences, Northeastern University

David Messinger Director and Associate Actuary

Fullscope, a division of Sun Life U.S.

Sun Life/Fullscope

Case Study

FiresideChat

Responsible

AI Governance for Insurance Companies

Ricardo Baeza-Yates,

Director of Research, Institute for Experiential AI (EAI) and Professor of the Practice, Khoury College for Computer Sciences, Northeastern University

Industry

Non Profit

Goal: Make Responsible AI the Norm

Education

● Ph.D. Computer Science

● MSE (CSE), M.Sc. (Computer Science)

● BSE (EE)

Academia

● Fellow: Association for Computing Machinery (ACM) and Institute of Electrical and Electronic Engineers (IEEE)

● Over 500 technical articles on search, data mining, data science, AI/ML, NLP, databases, and algorithms.

● Over 40 edited proceedings, 12 patents, and 8 technical books,

● Chaired major conferences on the Web, Search, Data Mining, and Data Science.

● Member of ACM Technology Policy Committee, IEEE Ethics Committee, Global Partnership on AI, among others.

Why Responsible AI?

• Ethical AI?

• Ethics, justice, trust, etc. are human traits

• So, we should not associate “ethical” to a machine

• Trustworthy AI?

• Trust something that does not work all the time?

• Puts the burden in the user

Systems do not need to be perfect, but seems that people wants them to be better than us [Hidalgo

Risks

Main Problem: Our Cognitive Biases

It Can Be Subtle

• Belmont Report for biomedical and behavioral research (1979)

• 3 Basic Values

• Autonomy

• Beneficial & No harm

• Justice

• Applications

• Informed consent

• Risk & Benefits Assessment

• Subject selection

Principles Conflict!

Insurance Specific Regulation on the Use of AI

ACM US TPC Statements

Algorithmic Transparency and Accountability (1/2017)

Responsible Algorithmic Systems (10/2022)

e[Baeza-Yates & Matthews, 2022]

1. Awareness

2. Access and redress

3. Accountability

4. Explanation

5. Data Provenance

6. Auditability

7. Validation and Testing

1. Legitimacy and competence

2. Minimizing harm

3. Security and privacy

4. Transparency

5. Interpretability and explanation

6. Maintainability

7. Contestability and auditability

8. Accountability and responsibility

9. Limiting environmental impacts

Awareness

Data provenance

Legitimacy & Competency

Minimizing Harm

Safe & Effective Systems

Algorithmic Discrimination

Protection

Security & Privacy

Transparency

Data Privacy

Notice & Explanation

Explanation

Interpretability & Explainability

Access & Redress + Auditability

Contestability & Auditability

Human Alternatives, Consideration & Fallback

Accountability

Accountability & Responsibility

Validation & Testing

Maintainability

Limiting Environmental Impacts

AI Governance as a Process

Idea Design & Development Operation When It Fails When It Harms

Ethical Risk Assessment

Algorithmic Audit Validation & Testing Monitoring Tools

Minimizing Harm

Legitimacy & Competence

Transparency

Security & Privacy

Interpretability & Explanation

Limiting environmental impacts

Contestability & Auditability

Accountability & Responsibility

Maintainability

AI Ethics Advisory Board at the Institute for Experiential AI

● 40+ multidisciplinary experts

● Helps organizations develop and deploy AI responsibly

● On-demand, top-level independent ethics guidance

● Tailored to meet company needs

Whoisresponsible?

RAI in Insurance:

• Key for underwriting and pricing models adopted by insurance regulators

• Key for model development process without discrimination

Responsible AI Practice at the Institute for Experiential AI

Based on the Puzzle-Solving in Ethics (PiE) Model, developed by the AI Ethics Lab.

The Global Nexus for

The PiE Model lays out four core components of AI ethics implementation: Roadmap, Strategy, Analysis, and Training.

Climate & Weather Modeling in Insurance

Auroop R. Ganguly, College of Engineering

Distinguished Professor

Director, AI for Climate and Sustainability (AI4CaS), The Institute for Experiential AI

PI, Sustainability & Data Sciences Laboratory, Northeastern University, Boston, MA

Chief Scientist, US DOE’s Pacific Northwest National Laboratory

Career Highlights

Goal: Developtransformative HybridKnowledge–AI solutionswithHuman-in-theLoopforgrandchallengesin ClimateandSustainability

● Ph.D. from MIT (USA), B.Tech. (Hons.) from IIT (India)

● Fellow, AmericanSocietyofCivilEngineers(ASCE)

● Senior Member, IEEE

● Senior Member, ACM

● Award winning Climate-AI paper in ACM KDD

● Climate & ML papers in journals such as Nature

● 5+ years at OracleCorporation

● 7 years at US DOE’s OakRidgeNationalLaboratory

● 2 US Patents : Climate Risk & Infrastructure Resilience

● Climate Analytics Startup: risQ(Fortune 500 Acquisition)

● Weather AI Startup: Zeus AI (NASA SBIR Phase II)

● United Nations : Environmental Panels & IPCC Citations

● Climate Assessment (BRAG): ClimateReadyBoston

● Climate Risk (AGU TEX): Town of Brookline, MA

Funding Sources

● Media: New York Times , Newsweek , Independentetc.

Natural Catastrophe (“Nat Cat”) models translate disaster risks to insured losses

Catastrophe modelers, suchasAIR Worldwide, compute risks,insured losses,and insurance Premiums

Climate disaster resilience requires technological and social solutions

Performancebaseddesign, gray-greensolutions,and strategicbutequitableretreat canimproveresilience

Climate change exacerbates weather extremes causing “global weirding”

Intensification of extremes, increased variability,and challengesin predictability,are thehallmarks of globalweirding

Climate change exacerbates weather extremes causing excess insured loss

The flashiness of flashfloods show an increasing patternacross the continental United States

weather has more than doubled the insured loss over the last 1.5 decades

Lack of consideration of climatechangereducesrisk estimationfrom weather extremes

The US housing market is severely overpriced from unpriced climate risks

Private companieshave developedAI informed software for floodingin countries acrossthe globe

AI-informed data-driven solutions are being developed to manage flood hazards

Machine Learning and process transforms postprocessing and downscaling

Ourphysics-aware BayesianDeep Learningenhances functionalmapping andreduces computation

State of the art climate modeling, NatureEducationKnowledge

DeepSD:Generatinghigh resolutionclimatechange projectionsthroughsingle imagesuper-resolution.

Award-winning paper at ACM KDD Conference; Highlighted in Nature 2019 Perspectives

Ourphysics-guided anduncertainty awaredeeplearning methodsmotivated byadvancesin computervision

DeepLearningforDownscalingandBayesian DeepLearningforUncertaintyonSkewed

Data:

• 2017 KDD Conference Award-Winning Paper in the Applied Data Track

• 2018 KDD Research Track Article

Statistical downscaling fills a critical stakeholder gap in climate models

Physics-guided deep learning methods translates models to relevant scales

Our award-winningdeeplearning basedstatisticaldownscaling methodshaveshownpromise

Oceanof data from sea surfacetemperatures based onsimulationsand observationshelps process

Explainable deep learning improves water projection from models and data

Our explainable deep learning for climate uses CNN and saliency maps

Ourmethodsforexplainabledeep learningprovidesimproved projectionsandunderstanding

Bringing the power of climate analytics to socially impactful startups

ThesuccessofanSDSLabstartupfocusedonclimateand citiesdemonstratesthepossibilities

Bringing the power of AI and satellite to weather prediction startups

AstartupbyformerSDS

Lab students focused on AI and satellite data for renewableenergy sector

Northeastern University engineering alumni

Interdisciplinary

Kate Duffy, PhD’21, and Thomas Vandal, PhD’18, both worked as NASA scientists before creating the new start-up, Zeus AI, which uses AI and machine learning to analyze data from satellites to improve weather forecasting.

KEY TAKEAWAYS

• AI is becoming an imperative in the digital ageyet challenging to make work:

• Human intervention a mustforcontinuous correctionofthealgorithmicissues.

• ResponsibleAIisamusttopreserve customertrust,avoidregulatoryand reputationalrisks.

• Customer experience is a key business driver

• No Data ⇒ no working AI

• Getting the data/context story right is the key enabler - for business insights and for AI

• Climate change is increasing the volatility and severity of weather events. New AI methods show promise in helping insurers manage climate risk.

NEXT STEPS

● We at the Institute for Experiential AI are here to work on real challenges so we both learn:

○ Howdoweidentifyopportunitiesfor innovationtogether?

○ Howdoweupskillthein-house talent and leverageresearchersandfacultyaspartof ourbusiness?

● The Institute seeks to work with you to solve real problems to

○ Drive new researchdirections

○ Driveanewwayofexperientialtrainingof studentsand learners

● Get in touch! - Let’sAITogether!

Thank You!