Employing Data Analytics to Create a Customer-Centricity for Insurers

(1)

Employing Data Analytics to Create a

Customer-Centricity for Insurers

Dr. Toa Charm

Founder & Chairperson, BI and Big Data SIG

Vice President (Professional Development)

Hong Kong Computer Society

DBA , MBA, B.Sc. , CBIP (TDWI), Big Data Cert. (MIT)

Jun 11, 2014 Hong Kong

(2)



Industry Experience in Business Intelligence and Big Data

–

Associate Partner, Business Analytics, Greater China, IBM GBS

–

Regional Head of BI Competency Centre (BICC), Asia Pacific, HSBC

–

General Manager, Business Intelligence, Greater China, Oracle

–

Managing Director, Greater China, Hyperion

–

General Manager, Asia Pacific, Kingdee International Group



Accomplishment in Business Intelligence and Big Data

–

Established 1st Business Intelligence Competency Centre (BICC) in HSBC Asia Pacific

–

Founder and Chairperson, BI Special Interest Group, Hong Kong Computer Society

–

Won Hong Kong Computerworld Best BI Award, Hyperion’s Asia/Pacific Best Partnership Award, Hyperion’s Best

Marketing & Best Consulting Award

–

Forum Chairs/Moderators: Big Data Business Forum (US), Hong Kong BI and Analytics Forum (2011-2013), Retail

Analytics Forum, Cloud Asia, Insurance Analytics, Finance Innovation, BankTech Forum, etc.



Qualification and Publications

–

Doctor of Business Administration, MBA, B.Sc.

–

Doctoral Thesis: Impact of Organizational Capabilities on Business Intelligence Maturity and Customer Relationship

Management Performance

–

Author of two books to be published in 2014 – “Strategic Success of BI and Big Data Journey in Greater China” and

“Strategy - Make or Break Our Company and Career Lives”.

–

Adjunct Professorship: University of Hong Kong, Fudan University (Shanghai), University of Macau

–

Completed senior executive programs from Harvard, UC-Berkeley, MIT, CEIBS

Dr. Toa Charm 湛家揚博士

Founder & Chairperson, BI and Big Data SIG

Hong Kong Computer Society

(3)

Today’s Agenda

• Big Data Collaborations in Hong Kong

• A Sense of Urgency for Insurers

• Innovative Use of Big Data in Financial

Services

(4)

Hong Kong Computer Society

Special Interest Groups (SIG)

Industry Advisors

•Financial Services

•Logistics & Transport

•Retail & Hospitality

•Health Care

BISIG

Business

Intelligence and

Big Data SIG

CCSIG

Cloud Computing

SIG

EASIG

Enterprise

Architecture SIG

ISSIG

Information

Security SIG

MoSIG

Mobility

SIG

QMSIG

Quality

Management SIG

SMSIG

Social Media

SIG

DESIG

Digital

Entertainment

SIG

SMGSIG

Service

Management SIG

(5)

BISIG

BI & Big Data Special Interest Group

• Site Visits (e.g. GP, TVB, HKAA, etc.)

• Big Data Competition for Students

• Big Data Mentorship to University Students

• Talks to Business, IT and Academia

• Big Data Workshops

• Advisors, Moderators,Speakers for Big Data Conferences

• Big Data Survey and Research

• Big Data Analysts Sharing (e.g. Gartner)

(6)

Today’s Agenda

• Big Data Collaborations in Hong Kong

• A Sense of Urgency for Insurers

• Innovative Use of Big Data in Financial

Services

(7)

C2B > B2C

Customer

Experience

Big Data

Analytics

is

Not an Option

(8)

Digital Disruption to

Traditional Leaders

(9)

New Players Disrupt and Re-Set

the Rules of Our Games

(10)

Source: Forbes Retrieved from

http://www.forbes.com/sites/gilpress/2013/10/30/top-10-most-funded-big-data-startups-updated/

on Jan 18, 2014

Big Data

Innovators

and New

Challengers

(11)

New entrants are cherry-picking select customer segments and banks are taking notice:

28% of executives mentioned online and mobile players as potential threats

wesabe

Source: IBM Institute for Business Value analysis, Company web sites and Annual Reports

FinTech’s Innovations Disrupt

Financial Services Industry WW

(12)

Smart Traditional and Technology Companies

Leverage on Big Data to

(13)

Finance & Insurance

has the highest value potential of

using big data

(14)

Big Data Adoption

in Asia Pacific

- by industry

(15)

Today’s Agenda

• Big Data Collaborations in Hong Kong

• A Sense of Urgency for Insurers

• Innovative Use of Big Data in Financial

Services

(16)

BI is Necessary and Good

But it is NOT Good Enough

Executive

A global vision

Highlights, Trends, Metrics & Goals

Manager & Analyst

A consolidated vision

Ad-hoc, impact & root cause

analysis

All levels are connected by goals, metrics, people & performance

End User

A detailed vision

(17)

Today’s Executive Dashboard shows

lag indicators only

(18)

Increase

Profitable

Revenue

through

Improved

Insight

Identify and Provide Value-Added Services

Identify Quality Leads / Referrals

Improve Rating Insight

Acquire New Customers Manage Cost Through Improved Insight Enhance Customer Relationship

Improve Loss Prevention Innovative Service Offerings Increase Cross Sell and Account Rounding

Increase # Profitable Agents Optimize Agent Production

Increase Profitable Agent Retention

Increase Policy Retention Flexible / Innovative Product Offerings

Reduce Adverse Selection Increase Share of Book

Business

Objective

Goals

Activities

Business Lever/Benefits

Geographic Expansion / Market Research

Improved Incentive & Goal Setting

Develop competitive products and pricing Improve Market Segmentation and Identify customers that meet Company Profile

Improved Agent Segmentation / Analytics

Leverage Existing Markets for other LoBs

Metrics

Diversify Across Industry Value Chain

Provide Sales Training and Support

Performance Management Contract Agents meeting Company Profile

Improve Ease of Doing Business Provide Sales Training and Support

AGENTS

CUSTOMER

Assist with Perpetuation Plans

Identify and Provide Value-Added Services / Products

Identify Cross Sell Opportunities Improved Customer Satisfaction

Improved Customer Segmentation / Analytics

Client

Recruit Underwriters with strong industry relationships and/or knowledge

Improved Business Management Reporting Optimize Cost to Serve

Align Performance Management to Strategic Objectives

Identify and Retain Customers meeting Company Profile

Increase Premium Per Agent Increase New Business

Develop Human Capital / BI Competency

Submissions/Quotes/Binders Benchmark Rating

Loss Ratio

Agency Growth Rate

Loss Ratio Trend % Cross Sell Agent Retention Ratio

Expense Ratio Premium Growth rate

Combined Ratio Agent Satisfaction Score* Customer Satisfaction Score* Hit Ratio

Customer Retention Ratio

Agency Profitability Score* Effective Rate Change*

LAE Ratio Commissions Ratio

Analytics

formulate

objectives

and drive

actions

Traditional

BI

monitors

the results

of

the

activities

(19)

Big Data brings us a new set of lead indicators

to lead the direction of our business and

resources allocation

e.g. Customer Segmentation, CLTV, Customer Churn Prediction,

Sentiment Analysis

(20)

Big Data’s Value Potential

for Insurers

(21)

Business

Metamorphosis

Data

Monetization

Business

Optimization

Business

Insights

Business

Monitoring

Big Data Business Model

Maturation Index

Measures the degree to which your

organization has integrated big data and

advanced analytics into

your business model

Traditional BI

(22)

Business Optimization

- Next Best Action (NBA)

(23)

Business Optimization

- Next Best Action (NBA)

(24)

(25)

Understanding that clients are

infrequently engaged in “buying

episodes

”.

Focus marketing efforts on those

episodes to improve sales,

effectiveness, and customer

experience

Accept that communicating less

frequently could be more

valuable.

Event-based interactions are

most effective when

implemented as part of an

overall client communications

strategy that leverages mass

(brand) communication and

propensity (modeled)

communication.

Identification Behavior Interpretation Prioritization Dialogue Tracking

Lifestage, product and behavioral changes can

signify when the company could be of

service to the client.

Interpret these changes (events), their significance to each client, and the opportunity

it may represent for the company. Prioritize messages, contacts and

channels for optimal results Engage the client

in a two-way “dialogue”.

Track communication and client responses to become more effective, iterative, shorter-cycles.

Identifying the potential events that may drive a buying episode

Event Based Customer Management (EBCM)

e.g. Real-time One-Time Insurance (OTI)

Translating the Customer Focused Enterprise communication strategy into real actions by

using lifecycle event analytics to drive event-based interactions

(26)

Business Optimization

- Customer Life Cycle

(27)

Business Optimization

- Cross-Selling

(28)

Two customers with house contents/buildings insurance, due for

renewal in the near future

Actively manages finances and household affairs. Already shopping

around.

Insurer uses predictive analytics to pre-emptively target customers unlikely to renew

Retention risk … If female And age 26-34 And claims > 0 And service_call > 1 And used “amount” (sentiment NEGATIVE)

Then defection risk is HIGH …

Standard renewal letter: “you need do nothing…”

No great awareness of insurance policy, likely to renew by default

Likely to defect?

Executive reports on renewal rates and patterns

Retention Offers … 1. Cost €44, prob 0.23 2. Not applicable 3. Cost €52, prob 0.78 4. Cost €67, prob 0.44 … Current policy holders Renewal imminent

Early contact letter: benefits, options

Call with “favored customer” incentive –

no claims protection, free for 1 year

Business Optimization

- Insurance Renewals

(29)

Segmentations Analysis

Enabling Partners

to Do More Business

(30)

30

Sophisticated Pricing Approach Simplistic Pricing Approach

Characteristics

 Limited data points

 Simplistic models

 “Coarse grain” pricing

 Product based

 Driven by collateral valuation

 Tens to hundreds of price points

Example

 Mortgages

Characteristics

 Many data points

 Complex models leveraging permutations of many data points

 “Fine grain” pricing based on interest rates, fee structures, reward programs, interest free days

 Micro Segment & Campaign based pricing

 Driven by ability to repay loan

 Potentially millions of price points

Example  Credit cards

R

isk

Pricing

segments

R

isk

Pricing

segments

Risk-Based Premium Pricing

for Target Customers

(31)

Insurance Companies Use Big Data

for Risk Assessment

(32)

Risk-Based Premium Pricing

for Target Customers

(33)

Two Leading Big Data Fintech Companies

ZestFinance and Kabbage

(34)

Zest Finance

https://www.youtube.com/watch?v=18CyX5sJx5I#t=86

Douglas Merrill, CEO of ZestFinance

Former Chief Information Officer of Google.

(35)

Zest Finance

- Big Data Based Underwriting Model

A new set of underwriting models that allow ZestFinance to extend credit to 25 percent

more Americans and increase repayment from customers by 20 percent.

(36)

Kabbage

(37)

Kabbage

– Partners

(38)

Behavioral-based Credit Scoring

• Entrepreneurial Finance Lab

, for example, wants to

solicit potential borrowers to take psychometric tests

to help determine how truthful they are and prescreen

their creditworthiness

• Lenddo

analyzes social media data to help score the

credit risk of middle class borrowers.

• RevolutionCredit

's software is designed for partner

creditors to invite borrowers to view courses at points

of transaction, such as when a person takes out a

credit card or wants to waive a fee for a one-time late

payment. The hope of the young company is to show

that a person who is willing to go through one more

hurdle is a less risky borrower than his credit score

implies.

(39)

Progressive Auto Insurance

- Disruptive Premium Pricing

Source:

(40)

RevolutionCredit

(41)

Centrifuge

- Fraud Network Visualization

(42)

Financial Services Use Analytics to

Detect Suspect Employee Behavior –

Compliance & Due Diligence

Ten large U.S. and European banks are using natural language processing technology from Digital

Reasoning — one of Bank Technology News' 'Top Ten Tech Companies to Watch for 2012' — to

(43)

Hearsay Social

Social Compilance and Marketing

for Financial Services

(44)

Alert

Understand

Discovery Point of View

Analyzing Taxonomies Analyzing Relationships Create Point of View

Persistence Queries

URL’s List of Blogs/

Boards

Analyzing Sentiment Analyzing Influencers Discovery

Monitor

Discovery Topics

Visual / Faceted Search Create the Landscape

Backend - Building the System

Frontend - Discovery

O n lin e N e w s Information Extraction S o ci a l M e d ia Internal Customer Data News Feed / Wires Targeted Websites