Connected Intelligence - TIBCO Software · IDC InfoBrief Connected Intelligence in Insurance 2 Regulatory burden is an opportunity cost for real transformation 30% of insurance companies

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New Generation of Capabilities Propels Insurance into the FutureInsurance as we know it is transforming dramatically, thanks to capabilities brought about by new technologies such as machine learning and artificial intelligence (AI).

Welcome to the new breed of insurers that are more personalized, more predictive, and more real-time than ever.

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An IDC InfoBrief J U N E 2 0 1 8

IDC InfoBrief Connected Intelligence in Insurance 2

Regulatory burden is an opportunity cost for real transformation

30% of insurance companies worldwide confirmed they faced more pressure in complying with new risk and cybersecurity guidelines8

40% of total IT spending likely to be in business risk management and regulatory compliance.9 Leading insurers worldwide believe that they can reuse analytics capabilities initially developed for regulatory compliance for other activities such as marketing

Growth of on-demand product platforms

Next-gen technologies such as advanced analytics and AI-based rules engines will power contextual marketing services such as “pay-as-you-go” protection cover, which appeal to millennials

Digital customers expect more personal, location-based, real-time services

Personal services: 40% of customers will share their personal information in exchange for personalized recommendations and offerings (Asia Pacific)1

Service quality: More than 60% of customers will not mind higher prices for better service quality (United Kingdom)2

Real-time services: More than 50% of customers consider “faster sign-up process” as extremely important for brand preference (North America)3

Rise of virtual ecosystems: Online platforms, with insurance as a lifestyle

purchase3x increase in conversion rates for online

platforms (e.g., web aggregators) in the last three years (2015–18)4

90% of worldwide customers will use AI bots by 2021 for various support services5

Customer engagement standards are higher

Infrequent communication is a turn-off for customers, especially for millennials

Non-insurance brands known for great customer engagement (think Amazon) are developing various propositions for insurance

Globally, at least 40% of customers are ready to switch insurance providers if not satisfied with their experience6

Insurtechs: Setting new standards in customer service

Policy purchasing and quote generation are at least 10x faster in comparison to incumbent insurers7

Many Trends Can Jumpstart the Insurance Industry’s Move to the Digital WorldInsurers that fail to act on the trends driving demand for insurance worldwide will eventually lose to more agile, flexible competitors who are adapting to the new world.

Source:1. IDC research and estimates, 2017-182. The Institute of Customer Service 2017 Study3. 2017 Insurance Barometer Study, LIMRA 4-5. IDC research and estimates, 2017-186. EY Global Customer Study 2014 Report and EY Sweeney Reports7-9. IDC research and estimates, 2017-18

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Insurers of the Future

Source: IDC research and estimates, 2017-18

C A P A B I L I T Y 1

Real one-to-one marketing: Improves

customer engagement with contextual services

By 2020, 35% of insurers worldwide will deploy cognitive

systems for improved contextual services


Existing distribution channels complemented

with data-driven analytics tools

Between 6-15% of insurers worldwide are already using cognitive-based models.

Distribution partners’ use of AI/cognitive models is still at

a nascent stage due to resistance to change


New customer-centric experiences from deep insights derived from

multiple sources of data

By 2020, 62% of insurers in Asia Pacific excluding Japan (APEJ),

45% of United States, and 40% of Europe, Middle East, and

Africa (EMEA) insurers will drive insight-driven customer-centric

experiences by leveraging big data analytics


Dynamic and real-time pricing from traditional

risk underwritingBy 2020, 15-20% of the total

vehicle insurance market worldwide will be driven by usage-

based insurance. In particular, Asia Pacific has seen a growing

number of use cases in the last two years


Business processes optimized for automation

70% of total spending on AI technology comprise automated claims processing, intelligent

process automation, and fraud analysis as the three major

components worldwide

The new breed of insurers will distinguish themselves with new capabilities.


Insurance: More PERSONALIZED

Source: 1-2. IDC research and estimates, 2017-18

Develop expertise in personal advisory servicesInsure niche protection requirementsTake control of “direct-to-customer” user experience

Mobile devices will be ubiquitous platforms to engage with customers on a one-to-one basis

30-45% of the processes will be automated and serviced through mobile devices in the next 12-18 months1

When analytics and insights are brought to the frontlines, staff can engage more meaningfully with customers at the point of interaction

High penetration of digital tools has resulted in

the continuous extraction of data through

connected platforms

AI/cognitive computing technologies sieve through

vast amounts of data to enable development of

“personalized” protection based on customer

behavior across multiple channels

Agents can obtain targeted customer

insights and provide 24/7 support across any digital touchpoint, with

algorithm-based rules engines

Maximize productivity through AI tools, such

as natural language processing (NLP), voice

recognition, and speech analytics

Key Technology Capabilities

Device-agnostic self-service portals result in at least 10% reduction in call center operations while giving full control to customers

Integrated chatbots can administer 60-70% of customers’ queries without manual intervention2

The “Personalization” Opportunity

1 2What Being Personalized

Means


Insurance: More PREDICTIVE

Source: 1. IDC research and estimates, 2017-18 2. IDC research and estimates, 2017-18; will be subject to regulatory and data-sharing guidelines

Develop predictive analytics based on traditional and non-traditional data about customers’ preferences

Enable intelligence-based capabilities for personalized messaging services to customers’ queries

Implement adaptive strategies to deliver bespoke insurance propositions

Build continually evolving data platforms

Improve intuitive understanding of customer behavior and preferences

Up to 20% improvement in customer conversion rates with contextual marketing and pricing capabilities1

Unified data from multiple sources — regardless of whether it’s structured

or unstructured35% of APEJ and 70% of EMEA insurers

have shown interest in integrating risk-mapping parameters from

social media platforms2

Dynamic underwriting models for facilitating

cross-selling across distribution channels

with the help of proprietary algorithms

Platforms of data, insights, and actions that unify “next best actions”

“Actionable intelligence” that improves

the relevance of point-of-sale offerings, claims management,

and claims triage


The “Predictive” Opportunity

1What Being Predictive

Means

Ongoing testing and system learning will enable analytical models to simulate multiple scenarios for improved accuracy in predicting future outcomes

Improve retention ratios through real-time-based sensing of customer preferences and free up resources for other priorities

An agile, adaptive, and dynamic model will assist risk underwriters with individually tailored solutions

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Insurance: More REAL-TIME

Source: IDC research and estimates, 2017-18

Real-time situational awareness of what is happening and dynamically developing next best actions

Make the shift from “risk avoidance” to “risk mitigation”

Ability to make sense out of huge volumes of data that change quickly

Dynamic models that are able to work on quickly changing inputs

Automation in business processes will lead to real-time decision execution capabilities with significant cost benefits

At least 20% risk optimization and operational savings achieved; mutually benefits insurers and customers

The “Real-Time” Opportunity

1What Being Real-Time

Means

Real-time fraud detection limits impact of losses by spotting fraud before it impacts the organization

Fast-track claims servicing and settlement procedures

Increase agility: Augment intelligence with real-time data capture and algorithmic models for better precision in pricing and user experience

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Empower your staff to focus on new

innovations with the help of autonomous bespoke

models to deliver real-time checks or decisions

Real-time monitoring of portable assets

(e.g., vehicles) allows insurers to adopt effective

risk management with appropriate guidance

Caution is needed in the choice of platforms and

databases to avoid conflict in achieving business outcomes

Edge analytics done as close as

possible to the data



A Whole Spectrum of AI Capabilities Predictive analytics, machine learning, and AI will transform insurance ― creating AI capabilities everywhere ― from customer interactions, to redesigned business processes, and new pricing models.

Move from “basic” to “best possibilities” scenarios

Increase real-time monitoring and identify possible future outcomes with machine-learning technologies

Reimagine customer dialogues with intelligent conversations

Simplify customer conversations with needs-based response

Focus on tailored offerings and unique risk coverage for individual customer(s)/group(s)

Being more relevant with AI-based underwriting and risk-mitigation models

Gain actionable insights on customers’ profiles with detailed understanding of their preferences and buying patterns

New Customer Capabilities

New “Intelligence-Based” Automation Capabilities

New Risk Mitigation Capabilities

Look beyond the traditional automation of standard workflows

AI-based intelligence not only automates processes but also enables autonomous decision-making in prioritizing or addressing queries. E.g., autonomous claims settlement with image-to-text conversion ability

Adapt to systems that can continuously evolve and learn

Algorithm-based models empower insurers to dynamically change “business rules” for more accurate, reliable, and timely outcomes

Identify and detect the unseen anomalies Partnership of “human-based” expertise and AI-based intelligence allows more precision in data-intensive processes, such as in risk selection mastery

Optimize risk mitigation strategy with predictive-analytics-based tools

Explore and identify high risk-based outcomes using algorithm-based models

Augment existing capabilities with AI-based intelligence in identifying “acceptable risks” with more precision

Complement capabilities for easier and faster discovery of high-risk and fraudulent claims

Enhance analytical capabilities that will gather, analyze, and predict high-impact losses

Improve speed and accuracy in detection of fraud “on the spot”


Who Leads Now?The Future is Now for many insurance companies worldwide. Here are some notable leaders from IDC Financial Insights research.

Move from “basic” to “best possibilities” scenarios

Ping An (China): Facial recognition for multiple scenarios with accuracy greater than 98% to reduce processing time and improve user experience

Reimagine customer dialogues with intelligent conversations

Tokio Marine (Singapore): Self-learning chatbot TOMI which, unlike other counterparts, responds more accurately even for unstructured queries

Being more relevant with AI-based underwriting and risk-mitigation models

QBE (United Kingdom): Machine-learning-based algorithms to improve overall risk selection in commercial property business

New Customer Capabilities

New “Intelligence-Based” Automation Capabilities

New Risk Mitigation Capabilities

Look beyond the traditional automation of standard workflows

Liberty Mutual Insurance (United States): AI-backed “auto damage estimation” app to provide users an estimation of the post-crash damage costs on a real-time basis

Adapt to systems that can continuously evolve and learn

Zurich: Deployed machine learning for automating claims processing in the personal injury line of business, reduced the claims-processing time to five seconds

Identify and detect the unseen anomalies AXA (Japan): Used machine learning in a proof of concept to predict which drivers are likely to cause “large-loss” accidents currently with an accuracy level of 78%

Optimize risk-mitigation strategies with predictive-analytics-based tools

AA (Ireland): Uses predictive analytics in pricing models for better optimization of price in real-time for each client ― takes into consideration several factors without any time lag

Complement capabilities for easier and faster discovery of high-risk and fraudulent claims

Asurion (United States): Uses advanced analytical tools for both rules-based assessments and events-based outcomes for fraud mitigation and prevention ― leading to a decrease in a fraud-dispute ratio by 50% in the first four years

Source: Press releases, company websites, and others


Accelerating More Personalized, More Predictive, and More Real-Time Insurance

The current reality, however, is that insurers still have limitations in diving headlong into the future.

These are ways by which they will resolve these limitations:

Dependence on legacy applications; lack of transparency in existing functionalities

Incomplete customer data integration

Disparate systems; non-uniformity of customer data

Slow marketing response ― lack of speed in design of “best fit” proposition to a particular customer

Risk of losing manpower

Bringing disparate sources of data into a data platform that can stage configurable, interactive dashboards

Build tactical capacity to capture and engage with any “identified” user

High-scale, super responsive “sense and response” geared toward a mobile-centric digital customer

Ability to A/B or “test and control” any interaction in order to determine the best offer or engagement, or better quantify lift, cost, or profitability

Easy-to-use data platforms so that more staff have customer insights and are making fact-based decisions

Essential Guidance

This IDC InfoBrief was produced by IDC Custom Solutions. Copyright 2018 IDC. Any IDC information or reference to IDC that is to be used in advertising, press releases, or promotional materials requires prior written approval from IDC. For more information, visit: www.ap.idc.asia or email: [email protected]

Artificial intelligence has immense potential to mine significantly vast, diverse, and multiple data sets. This is in contrast with the other traditional analytical tools which are limited to only a finite number of data points for risk pricing.

The capabilities in AI-based modeling not only augment and complement human skills, but can also be customized to detect anomalies and unseen data patterns, and gather rich insights which could have an outreaching effect on multiple business outcomes. While the human workforce can be freed to focus on higher value tasks, AI-based intelligence can be applied to a wide spectrum of applications with the added advantage of “on-spot” or real-time-driven decision-making capabilities.

With continually adapting systems, the AI-based machine learning techniques become adept in identifying additional drivers of risks. They eventually develop capacity to “predict” outcomes on the basis of frequency and severity, and speculate the extent of impact on the insurance business.

Organizations should move beyond process automation abilities and scale up analytical capabilities based on current, reliable, accurate, and trusted sources of data.

While the human workforce can be freed to focus on higher value tasks, AI-based intelligence can be applied to strategic, real-time decision-making.

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Connected Intelligence - TIBCO Software · IDC InfoBrief Connected Intelligence in Insurance 2 Regulatory burden is an opportunity cost for real transformation 30% of insurance companies