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Guest Lecture

by Professor Dr. Gene Lai

ジン・ライ教授による講演会

（from17:00 to 19:00 at the large conference room on the 9th floor）

（於 日本交通協会９階 大会議室 17時00分から19時00分終了）

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Technology, Big Data, and Insurance Industry （Gene Lai）

Thank you so much for inviting me. It is a great honor. Before I start my

presentation, I want to tell you that my parents speak Japanese. If they

wanted to say something that don’t want me to know, they would speak

Japanese. As a result, I learned some Japanese. And my favorite food

actually is not Chinese food, actually it’s Japanese food. And I use a

Japanese car. I had a two car both of them are Japanese car. My stereo

system was Sony. And even for the electronic shaver, it was made in Japan.

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The shaver is still working well after I bought it 18 years. In other words,

I love Japanese product culture.

So today I want to talk about technology, big data and insurance industry.

Even I do a lot of academic research, I have never done big data academic

research. But I thought big data was an interesting area and spent some

time on it. I hope that you will find my speech helpful. Mainly I will

talk about the trend about the technology and big data and how we can use

in the insurance industry. Please note that my speech is not my

original contributions. I got the materials from various sources.

First, I would like to provide some background about technology. The

growth of internet connected devices and sensors, which are projected to

reach 50 billion by 2020. The growth in smartphones and tablets, coupled

with cloud computing, which provide constant access to the internet. In

addition, International Data Corporation (IDC) reports that the digital

universe will grow 300-fold between 2005 and 2020 to a total of 40 trillion

gigabytes. Yet, according to IDC, only one percent of this data is currently

being analyzed.

Advances in Artificial Intelligence techniques, such as machine learning,

natural language understanding and intelligent decision-making will allow

insurers to advance from using technology for transaction processing to

decision-making. For example, Tobias Preis, and two physicists published

a paper titled “Quantifying Trading Behavior in Financial Markets Using Google Trends.” They found that as the volume of searches for

words like “debt,” and “stocks” fell, the Dow Jones tended to rise.

Another great example is Gilt Groupe, which reached $500 million in sales

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in just five years. Areport from McKinsey & Co. describes, Gilt Groupe

mines extensive customer data to personalize all e-communications.

By 2020, a number of biotechnologies will be available at the nanoscale,

providing the ability to embed devices and sensors unobtrusively within the

human body. The nanotechnology drug delivery market is expected to grow

at CAGR of 21.7% between 2009 and 2014, and reach almost $16bn by

2014. Such nanotechnologies have the potential to dramatically improve

health outcomes through enhanced monitoring and preventive control of

chronic disease.

Social media has drastically altered the landscape of personal and

professional communication as we know it. Companies across financial

services have adapted to involve social media as part of their core marketing

initiatives, and at an accelerated pace. At the midpoint of 2013, the majority

of financial institutions are on Facebook, Twitter and YouTube, as well as a

host of additional social media mediums.

Customer expectations: Customers are increasingly demanding simplicity,

transparency and speed in their transactions with businesses, including

insurance agents/advisers and carriers. The relentless march of online and

mobile technology is continuing to fuel this change in customer

expectations.

In a recent survey of US consumers, more than 32% of all respondents

and 50% of those aged 18 to 25 said they prefer to work directly with

insurance carriers.

More and more insurance will be ‘bought’ by customers as opposed to

being ‘sold’ by agents destroying the age old wisdom of ‘Insurance is sold

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and not bought’.

Foster innovations in product/service design and delivery. Leading insurers

will get better at targeting customers and customising product and service

attributes to meet their specific needs.

Please also note that information security and risk is a top priority. Many

corporations and universities suffered from cyber attacked.

Next, I will like to talk about how to address the issues related to technology

changes. There are a lot of investments in mobile and interactive

technologies for multimedia content creation from insurance companies.

For example, Zurich Insurance, a multi-line insurer, is using a

MicroStrategy Mobile app that helps ensure transparency of operations and

compliance with financial regulations. We also need to discuss who is liable

for new kind of risks (e.g., driverless cars, software bug downloaded to

millions of cars wreaks havoc, or a hacker taps into the transportation

network)?

The ability to measure and monitor everything that those insurance products

cover in real-time (including people) raises the profile for behavioral

sciences

Machine learning is here to solve workforce issues. The insurance

workforce is aging, and as has happened in many other industries,

automation is filling the gaps left by retirements. Automated underwriting is

already relatively popular, but soon artificial intelligence and machine

learning platforms could pop up in other areas as well

I would like to talk about Social Networks. In just 8 years since its launch,

for example, Facebook has attracted over 1 billion users. The experience

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in in other industries will be discussed below. For example Yelp for food

or lodging and product reviews from Amazon. Amazon show you both

the positive and negative comments.

People want access to better health care or insurance product or services

using social media. People will look up the reputations of doctors, merits

of insurance products, and rankings of services of insurance companies.

The medical service and treatment model is evolving towards the

customization of healthcare. Consumers will eventually use personalized

medicine to create highly customized healthcare solutions that actively

change the body’s biochemistry in response to risks and conditions that are

unique to each person.

Sam Friedman, author of Mobile engagement: Insurers look to connect with

consumers on the go posits that the industry faces two basic challenges.

First, raising awareness and adoption and second, moving beyond the basics

like bill-paying to stickier, more frequent interactions.

Next, I would like to talk about big data. There are many definition of Big

Data. There are many definitions. In general, there are three

requirements: too large for common database, the data set must be too

large for common database management tools to handle, and the data must

come from multiple sources. Finally, the data must provide insights that

improve decision-making.

The computer industry had always operated under something called

Moore's Law, which presumed that computing speed would improve by a

factor of 10 every six years. Now, companies now can analyze data that

previously was too voluminous, or came from too many different sources.

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Next, we like to address how big data is classified. First, by structure (or

lack thereof). Structured data is information stored in numerical formats

recognized by computers, such as spreadsheets and customer purchases.

Unstructured data, meanwhile, is the stuff that has no intrinsic numerical

framework—such as videos, photos, text and social media commentary. In

fact, some people believe “big data” applies only if part of the data is

unstructured.

Big data can be classified by source. Some people like to categorize big

data according to where the data came from. For example, there is mobile

data (such as information produced by smartphones), social media data

(such as tweets and website comments), and public data (like census

information).

Third, big data can be classified by analytics program. Liebowitz says a

third common way to categorize big data is by the type of analytics

programs needed to make sense of the data. These include such examples as

social media analytics, mobile analytics, video analytics, sensor-based

analytics, etc.

Insurers who are able to use real-time ‘big data’ and advanced

forward-looking simulation techniques will establish a significant

competitive advantage.

Next I would like to talk about IBM general solutions for big data. We

will start with big data exploration. Find, visualize, understand all big data

to improve decision making. Big data exploration addresses the challenge

that every large organization faces: information is stored in many different

systems and silos and people need access to that data to do their day-to-day

work and make important decisions. Second, extend existing customer

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views by incorporating additional internal and external information sources.

Gain a full understanding of customers—what makes them tick, why they

buy, how they prefer to shop, why they switch, what they’ll buy next, and

what factors lead them to recommend a company to others. Third, we will

talk about security intelligence extension. Lower risk, detect fraud and

monitor cyber security in real time. Augment and enhance cyber security

and intelligence analysis platforms with big data technologies to process

and analyze new types (e.g. social media, emails, sensors, Telco) and

sources of under-leveraged data to significantly improve intelligence,

security and law enforcement insight. Please note that operations analysis

is very important. Analyze a variety of machine and operational data for

improved business results. The abundance and growth of machine data,

which can include anything from IT machines to sensors and meters and

GPS devices requires complex analysis and correlation across different

types of data sets. By using big data for operations analysis, organizations

can gain real-time visibility into operations, customer experience,

transactions and behavior.

Finally, we will talk about data warehouse modernization. Integrate big

data and data warehouse capabilities to increase operational efficiency.

Optimize your data warehouse to enable new types of analysis. Use big data

technologies to set up a staging area or landing zone for your new data

before determining what data should be moved to the data warehouse.

Offload infrequently accessed or aged data from warehouse and application

databases using information integration software and tools.

IBM Solutions for the Insurance Industry will be discussed next.

First, insurance companies need to create a customer-focused enterprise,

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improve customer retention and satisfaction, accelerate offer acceptance,

learn customer attitudes, optimize cross-sell and up-sell. They can sell

from auto insurance to life insurance or from life insurance to long-term

care insurance and from low benefit to high benefit. As far as optimize

enterprise risk management, insurance company should prevent, predict,

identify, investigate, report and monitor attempts at insurance fraud. They

need to identify patterns and trends that can pinpoint fraudsters quickly and

improve fraud prevention in the future. Insurance companies can use big

data to prevent unexpected losses, civil and criminal penalties. Finally, big

data should help to accurately manage underwriting, reinsurance and

catastrophe bond pricing.

Second, insurance can optimize multi-channel interaction to increase sales

channel productivity, minimize infrastructure costs, increase availability

of low cost self service labor options, and increase flexibility and

responsiveness to changing customer preferences.

Big data can be used to increase flexibility and streamline operations.

Specifically, insurance companies can Increase revenue from subrogation,

increase revenue per customer, lower payout of fraudulent claims, reduce

operating costs, increase response time to new compliance mandates.

Insurers already hold vast amounts of data, but now they can gather even

more from new sources such as GPS-enabled devices, social media postings

and TV footage.

Big Data has been used as Predictive Models. The key to unlocking this is

through data analytics, the process of examining large amounts of data of a

variety of types to uncover hidden patterns, unknown correlations and other

useful information. Such information can provide competitive advantage

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and result in business benefits, such as more effective marketing

and increased revenue. For example, brokers are using advanced

technology to better analyze risks for clients to lower costs and manage

claims. Many easy to use software has been developed.

One survey shows that only 17 percent of insurers said they routinely use

analytics as part of an integrated, enterprise-wide approach, while 36

percent of insurers (compared with 20 percent of the total sample) have the

resources and ability to use analytics, they apply them in tactical—rather

than strategic—applications. Notably, 20 percent of insurers said they

“don’t know” how their organization was using data and analytics. In a

data-dependent business like insurance, that’s an eye-opener.

In its industrywide survey on the topic, Accenture finds that insurers

actually have an edge over other industries when it comes to big data, but

aren't taking advantage of it. While 36 percent of insurers (compared with

20 percent of the total sample) have the resources and ability to use

analytics, they apply them in tactical — rather than strategic —

applications.

It should be noted that insurers should use big data strategically and to serve

the entire enterprise, versus a single department or business line. “There is

huge potential in data analytics in insurers to better understand customers,”

says Costonis. “This includes the ability to make smart marketing decisions,

to introduce refined products and pricing, and finally to decrease losses.”

2013 Insurance Predictive Modeling Survey by ISO and Earnix. I

summarize the results below. Insurers already hold vast amounts of data,

but now they can gather even more from new sources such as GPS-enabled

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devices, social media postings and TV footage. The key to unlocking this

is through data analytics, the process of examining large amounts of data

of a variety of types to uncover hidden patterns, unknown correlations

and other useful information. Such information can provide

competitive advantage and result in business benefits, such as more

effective marketing and increased revenue.

The survey shows that Larger companies make more use of predictive

modeling than smaller ones. 82% of the respondents use predictive

modeling in one or more line of business. Models are more commonly used

in personal lines than in commercial lines. Specifically, models are more

commonly used in personal lines than in commercial lines. The most

common use of predictive analytics is found in Personal Auto (49%),

followed by Homeowners (37%), Commercial Auto (32%) and Commercial

Property (30%). The benefits of the use of predictive modeling include

profitability (85%), risk reduction (55%), revenue growth (52%), and

operational efficiency (39%).

The most common use of predictive analytics is in pricing. Loss cost

modeling accounts of 75%. The use of predictive modeling in

underwriting includes risk selection (78%), additional underwriting

information (52%). Some examples are stated below. Auto underwriting

uses Telemetry-based packages. In other words, actual driving

information is fed back to insurers’ system to predict likelihood of an

accident or car stolen. To deal with privacy issue, insurer can use “Grade

A” or “Grade B” instead of tracking particular roads. Customers don’t mind

giving up some data if you’re transparent about what data you’re asking for,

and they are getting real value back for it.”

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Another Example is related life and health insurance. Apple Watch and

Fitbit activity trackers are used by John Hancock to offer users discounts

on their premiums and a free Fitbit wearable monitor. It should be noted

that life style is controllable and can be used to for premium underwriting.

Believe genetic disadvantage is not controllable and should not be used for

premium.

The use of redictive modeling in marketing area includes target marketing,

customer retention (44%), cross-selling (32%), and agent performance

(28%).

Specifically, algorithms comb through the unstructured data in telephone

calls, emails and the information divulged on social media networks about

what policyholders do or do not like to create personalized marketing

strategies for each customer.

Insurers also track policyholders behavior while they logged into their

insurer’s web portal – such as length of time browsing FAQ and help

sections, as well as participation in user forums and message boards, can be

recorded and built into a customer’s unique profile.

Met Auto and Home: Use CLUE (Comprehen Loss Underwriting

Exchange). Armed MetLife is able to pre-fill most of the demographic

information of the prospect who indicates interest in getting a quote, with

that consumer asked only to make changes if information is incorrect. Then,

once submitted, the company accesses the data bureaus via API and comes

back with a quote in a short time.

“At a minimum, it takes the process from 20 minutes down to two,”

For homeowners insurance, there’s a lot more that has to be collected, and a

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lot more nuance. Yet, customers still demand a shorter process, and an

Xcelerate-style process might not be that far off.

“I know there’s a company trying to stitch together all the municipal

databases [that are needed for homeowners insurance] out there.”

A quarter of consumers surveyed by LIMRA and the Life Insurance

Foundation for Education (LIFE) think they need more life insurance, and

more than twice that many have no plans at all to purchase life insurance in

the near term. While cost is cited as the largest barrier to purchase,

significant portions of respondents to a 2014 survey said they felt they

“wouldn’t qualify for coverage” (19 percent) or simply “hadn’t gotten

around to it” (30 percent). The overall results show there is a market out

there.

With a long application and full medical exam waiting for those customers

that do show up, the industry is working hard on making a better initial

impression by leveraging emerging technologies.

A good example is Lincoln Financial which has moved away from asking

producers to walk customers through a full application

Now, the advisor collects some key demographic data, then ships that data

off to a call center where that data is used to call APIs at major data bureaus

and are correlated with the medical information that would’ve had to

confirm the information anyway – similar to the MetLife approach for auto

insurance.

The Timetric report cites South Africa-based insurer AllLife as using big

data to underwrite entirely new risk that could not previously be covered

profitably. AllLife offers life and disability insurance at low premiums for

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manageable diseases such as HIV and diabetes. Using Big Data analytics,

AllLife assesses policyholders' risk every three to six months, and clients

who do not adhere to strict medical protocols will have their benefits or

cover reduced. As a result, AllLife aims to insure 300,000 HIV patients by

2016.

The use of predictive Modeling in Claims includes claim loss forecasting

(44%) and fraud (50%).

Fraud is a big issue for insurers. The Insurance Information Institute

(III) estimates that 10 percent of property-casualty insurance industry

losses each year are attributable to fraud, to the tune of $32 billion.

The III reports that 95 percent of insurers say they use antifraud

technology, but about half say a lack of information technology

resources prevents them from fully implementing it.

At the beginning of 2016, Towers Watson reported that 26 percent of

insurance companies were using predictive analytics to address fraud

potential. In the next two years, that number is expected to jump to 70

percent—more than any other big data application.

Many are looking to text mining as a crucial analytical tool for decoding

enormous amounts of unstructured data. Text mining is a way to scan

large amounts of data for keywords. Text mining can interpret claims

adjusters’ handwritten notes and scan a claimant’s social media accounts for

suspicious activity in nearly real time. Text mining and other tools

represent a fundamental shift.

The focus of fighting fraud has been changed from claims-centric to

person-centric. In other words, insurers will shift from focusing on the

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claim itself to the individual filing the claim. Claims models will pull

information about the would-be beneficiary from across claims, policies and

external data sources, including information from other insurers, medical

professionals, police, auto body shops and a host of other sources.

This shift raises issues regarding privacy and data quality. Individuals may

opt out of sharing information with their insurers through vehicle telematics

or social media, lessening the impact of insurer data analytics initiatives and

creating a competitive advantage for less scrupulous organizations willing

to harvest data against consumers’ wishes. Compounding the issue is the

fact that data collected is not always accurate or easily manipulated.

Concerns about privacy and access to personal information are not

exclusive to the insurance industry. Encouraging insurance consumers to

share data by spelling out how the increased data benefits all parties. The

benefits of additional data for reducing claims fraud, and the subsequent

reduction in coverage costs, is a good place to start.

Insurers also use big date to predict claim losses and catastrophic events.

The severity and frequency of catastrophic events, both natural and

man-made, have been increasing over the past 20 years. Between 1990 and

2009, hurricanes and tropical storms accounted for 45.2% of total

catastrophe losses and the rate and intensity of these storms is predicted to

increase with global climate change.

Historically, the insurance sector has been good at developing catastrophic

models that capture known high severity/low frequency events (e.g.

earthquakes, tsunamis, etc.). However, most of these models perform poorly

when it comes to unknown ‘Black Swan’ events. Over the next decade the

insurance sector could be overwhelmed with uncorrelated catastrophic

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events reducing capacity and raising prices.

Alternatively, new sensing and monitoring technology, together with risk

transfer mechanisms, could cushion insurers and reinsurers against

abnormal losses.

Who Use Big Data

Big Data is predominantly a big company affair at this point.

Of the companies with over $1B GWP, 51% either currently use Big Data

or are evaluating / implementing Big Data initiatives, compared to 30% of

the companies with under $1B GWP.

It should be noted that life insurance just getting started with big data.

Data sources that are already being used or explored include transformation

of business model (12%), expansion of customer relationships (41%),

enhancement of customer value proposition (18)%), and improvement of

internal performance management (29%) in 2015.

The internal data source for life insurers is from administrative system,

claim data, agents, underwriting data, and ZIP code data. The external is

from medical records, prescription data, credit scores, and motor vehicle

data.

Costs of Using Big Data

Companies spend considerable time on data preparation and deployment

before and after the actual modeling. Google spent over $3 billion on big

data infrastructure in 2012 and Facebook spent $1 billion.

The future data source is predicted from email, Clickstream, Voice-to-text

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logs, credit scores, websites and social media.

Chief Data Officer (CDO)

Capital One, the Federal Reserve, Google, New York City and the U.S.

Army all have at least one thing in common: they each employ a chief data

officer to oversee their big data programs.

The emergence of the Chief Data Officer closely mirrors a trend we saw in

the 2000s when companies began to appoint CROs.

Barriers and Challenges will be discussed next. Three challenges include:

lack of sufficient data, lack of data scientists, and conflicting priorities.

The top three barriers life insurers must overcome are infrastructure

limitations, financial constraints, and lake of knowledge and/or expertise.

I would like to talk a little about data scientists. They are mathematicians,

computer scientists, academically trained scientists (astrophysicists,

ecologists, biologists, etc.), hackers and software engineers who use the

scientific method.

They ask a question, develop a hypothesis, collect data (often through an

experiment), analyze the results, report their findings and then often

engineer them into applications.

Chief Data Officer (CDO) will be discussed next. A CDO oversees a team

that handles all data analysis — from social media, marketing and

advertising to pricing, customer service and operational processes. CDOs

and their teams, unbound by departmental divisions, can become a

fountainhead of intelligence and solutions that aim to make every

department more effective.

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But because data scientists may operate across departments and frequently

challenge the status quo, they often butt heads with company culture and

co-workers. For example, Greg Linden, formerly a data scientist at

Amazon, wanted to make shopping recommendations based on

items already in a shopper’s cart. As Linden wrote in his blog in 2006, “we

had an opportunity to personalize impulse buys.”

In the absence of a CDO, someone like Linden has to risk his job just to test

a hypothesis. When a CDO makes it clear that such experimentation is

expected and enrolls fellow executives in the opportunities, the CDO can

free data scientists to investigate issues they might otherwise be afraid or

unable to investigate. Ultimately, a CDO allows data scientists to operate

free from the constraints of company politics and HiPPOs (Highly Paid

Person’s Opinions).

In the absence of a CDO, someone like Linden has to risk his job just to test

a hypothesis. When a CDO makes it clear that such experimentation is

expected and enrolls fellow executives in the opportunities, the CDO can

free data scientists to investigate issues they might otherwise be afraid or

unable to investigate. Ultimately, a CDO allows data scientists to operate

free from the constraints of company politics and HiPPOs (Highly Paid

Person’s Opinions).

Creating a culture of seeking opportunities, finding and attracting the people

to do so, and creating a business that's executing effectively.

Asian universities can educate skilled modelers. Companies go all the

way to attract talents.

For example, a few years ago, a company moved a research unit to a

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building across the street from a major university and engaged academic

department heads in technology, statistics and other areas where they

wanted to get a better draw of talent. They started offering research grants

to graduate students and created quite a bit of affinity that nurtured a flow of

fresh talent.

The Big Data Institute at Temple University has five centers with individual

specializations that include big data usage in mobile analytics, social media,

health sciences, oncology research, statistics and biomedical informatics.

These centers have used big data to connect brain imaging to successful

advertisements, to use technology to create vast amounts of DNA for

clinical study, and, in the School Tourism Hospitality and Management, to

decrease dissatisfaction in the leisure industry, among other research

projects.

THANK YOU!