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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.
Connected Intelligence InsuranceIN
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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IDC InfoBrief Connected Intelligence in Insurance 3
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
C A P A B I L I T Y 2
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
C A P A B I L I T Y 3
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
C A P A B I L I T Y 4
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
C A P A B I L I T Y 5
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.
IDC InfoBrief Connected Intelligence in Insurance 4
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
IDC InfoBrief Connected Intelligence in Insurance 5
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
Key Technology Capabilities
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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IDC InfoBrief Connected Intelligence in Insurance 6
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
Key Technology Capabilities
IDC InfoBrief Connected Intelligence in Insurance 7
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”
IDC InfoBrief Connected Intelligence in Insurance 8
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
IDC InfoBrief Connected Intelligence in Insurance 9
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: gmsap@idc.com
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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