As the insurance industry continues to navigate the pace of change, complexity and uncertainty in our world, consumers continue to respond, expecting companies to be more responsive to their needs. This year’s underwriting predictions offer guidance on how carriers can respond faster.
With Data-Driven AI models, insurance companies can do more personalized recommendations to consumer as well as to build the appropriate products for segments of clients by optimizing earnings and customer satisfaction (see how Using AI, Analytics & Cloud to Reimagine the Insurance Value Chain). AI can also determine an individualized price based on consumer behavior and historical data.
Insurance analytics also utilizes predictive modeling. This helps insurance companies determine the effect of implementing a particular change on the insurer’s business books, or how a change in policy price will affect sales.
When carrying out insurance analytics, insurance companies use predictive analytics tools that collect relevant data from a wide variety of internal and external sources to try and understand, and then predict the insured’s behavior. The property insurance sector, for instance, collects data from sources such as social media, intelligent homes, customer interactions, agent interactions, and telematics. This data is then analyzed in order to provide insights.
Evolving cognitive technologies will help insurers capture opportunity
Technological advances in AI and data analytics are helping insurers further refine market segments. As these more discrete segments grow, so too does the opportunity for insurers to address them with new products and services offered through a wider range of digital distribution channels (see 9 New Technology Trends by Insurance Sector).
One such channel is embedded insurance—placing insurance in the customer journeys of non-insurance companies—for example, offering life insurance during the process of applying for a mortgage.
New cognitive insurance platforms underpin these new products and distribution channels providing life carriers with a way to capture that opportunity, and as these platforms evolve, they hold tremendous potential for the underwriting function.
Already, these insurance platforms are automating evidence gathering and providing recommendations based on a continuously updated data analytics engine (see How Metaverse & Visual Intelligence Can Transform Insurance Industry?). With this level of automation and intelligence, underwriting decisions can be made in real time. Those cases requiring further scrutiny are then automatically referred to a human underwriter (see How Technology Can Help to Tackle Insurance Fraud?).
With much of the evidence gathering already completed, the human underwriter is free to focus on further analysis, leading to more efficient decision making—a clear competitive advantage in fast-moving digital distribution channels.
We believe innovation in this area will continue to evolve over the next year. In fact, our report Fuel the Future of Insurance describes on page 11 how a life insurer in China is improving operating efficiency and customer experience by leveraging AI and a smart algorithm.
Manual processing or the use of basic computer programs can only deal with small amounts of predominantly localized data. Consequently, calculating premiums and underwriting was relatively slow and mainly depended on human judgment. Since the process depended on human agency and heuristics, there was also a tendency towards making mistakes in the calculations, requiring other people to check the numbers.
Insurance analytics is applied in all stages of insurance coverage, including policy creation, marketing, and even in fraud investigations.
Insurance firms need insurance analytics to optimize the processes that are involved in the evaluation and calculation of insurance risks and decisions around insurance products. There are six stages to using insurance analytics.
Conversely, insurance data analytics automates most of the calculation process. Consequently, the process is more accurate and relies less on human input since data analytics software makes calculations based on the predetermined formulas fed into the system.
5 Key benefits of Data Analytics in Insurance
Managing risk while offering competitive policies is one of the main drivers of using data analytics for insurance. Many insurance companies offer a variety of insurance products covering everything from cars and property to health and long-term care—each with its own risk profile.
Regardless of which products they offer, all insurance companies seek to reduce costs, increase profit, and build lasting customer relationships—and many rely on data-driven predictive analysis to make sound business decisions that support these goals.
While insurance analytics can improve the efficiency of claims, policy, and sales processes, that’s just the tip of the iceberg. Read on to learn five key benefits of data analytics for insurance.
- Lead Generation
There are a lot of challenges out there for lead generation insurance companies. Watching the competition and taking the lead on the market, bringing in high-intent insurance leads, and always having excellent customer service, just to name a few of the challenges lead generation companies are facing. Data analysis helps in many different ways to solve those problems.
- Better Customer Satisfaction
Insurance companies have caught wind of these benefits and have begun analyzing data trends in an effort to predict their customers’ needs. This makes it easier to provide support, suggest relevant products, and close sales. Using data analytics to uncover customers’ most common support inquiries enables insurance companies to provide comprehensive self-service tools that provide around-the-clock support and boost customer satisfaction scores.
- Less Fraud
Data analytics in insurance makes fraud-detection processes faster and more accurate. Analytics make it easier to spot trends while advanced analytics and predictive modeling use historical data (e.g., past claims, frequency of claims) and externally sourced information (e.g., credit scores) to flag claims with a high probability of being fraudulent.
- Faster Underwriting
Data analytics for insurance are changing this by using predictive trend data to produce comprehensive risk assessments. These assessments reduce the time burden of manually assessing risk profiles while improving underwriters’ ability to set premiums that accurately reflect each policyholder’s level of risk.
- Business Growth
The intelligent and ongoing use of data analytics should culminate in business growth. Targeted marketing messages, better customer satisfaction, less fraud, and faster underwriting can only translate into improved revenue. By selling more effectively, cutting fraud-related costs, and improving risk assessment, companies that choose to harness the power of insurance data analytics improve their bottom line.
Customer experience will continue to drive underwriting innovation
In last year’s underwriting predictions, I discussed how customer experience will determine who wins the digital competition for new business. We expect this trend to continue, but with a heightened awareness of consumer expectations and how insurers can respond more quickly to their changing needs.
For example, Accenture Insurance Consumer Study research identified that millennial and younger consumers aren’t the only cohort embracing a digital experience.
The 55 and older cohort is becoming more comfortable with digital interactions. And if insurers are to attract and retain customers, a digital customer experience is table stakes.
Underwriting plays a pivotal role in supporting the digital customer experience, especially with the proliferation of customer experience technologies available through ecosystem partners.
As our industry shifts from indemnity to protection products, digital technologies will be essential to providing differentiated experiences that leverage these platforms and ecosystems to capture opportunity from new product innovations.
We believe product and underwriting innovation will provide a significant source of revenue over the next several years. However, it will require expanded use of AI, automation, data analytics and cloud to profitably drive revenue.
As insurers modernize their legacy core systems, freeing siloed data, they’re able to automate their underwriting workflows to provide a faster digital buying experience, while connecting to additional data sources that help them apply the appropriate level of risk management.
Not only does this shorten underwriting timeframes and reduce costs, it also improves the underwriter and customer experience. Likewise, it supports the advanced experience consumers are looking for—seamless, proactive, and personalized.
According to a Gartner repor, by 2027, digitally engineered underwriting will have reached mainstream adoption in the global life insurance industry, resulting in significantly increased revenue and underwriting profitability and improved customer experience.
Human + Machine operating models will help alleviate underwriting skills
Digital technologies such as AI and automation are not replacing underwriting jobs. On the contrary, these technologies will become even more necessary as insurers face continued skilled labor shortages.
They will need a talent and investment strategy that targets digital skills in data analytics and no-/low-code capabilities along with the use of flexible workforces to optimize the underwriting function.
With the growing use of third-party data, AI and automation provide an efficient way to ingest data and make it useful to underwriters.
This frees underwriters to do what they do best—assess and price risk—while driving timely, effective decision making. What’s stopping them is the administrative work that takes up 40 percent of their time, according to our survey of 500 U.S. life insurance underwriters.
The first step is to improve the efficiency of back-end underwriting operations.
Interoperability is key to simplifying all customer-facing functions including product distribution, marketing, sales, service and commerce in addition to using an integrated technology stack across platforms and ecosystems.
The cognitive platforms described above can help here too. As insurers improve their digital capabilities to quickly address consumers’ ever-changing needs with even more discrete insurance products and distribution channels, underwriting capacity will have to keep pace. This human + machine combination can facilitate a better experience for underwriters and potential policyholders.
How technology is being used in insurance industry?
An insurer can provide more customized premium offerings to customers if in fact they have a holistic view of the pertinent data. Pricing strategies, claim fraud mitigation, lead generation, and customer satisfaction are a few of the areas where data analytics can provide competitive advantages.
How AI can help the insurance industry?
Some of the emerging AI use cases for auto insurance include: Predictive cost analytics for claims: Leverage machine learning techniques and data science to estimate the average claims cost per different customer segments. Adjust premiums respectively and manage your cash flow better.
How are AI and machine learning used to transform the insurance industry?
AI algorithms can identify likely fraudulent claims and highlight them for further investigation and action by humans if necessary. This allows an insurance company to take action much more swiftly than relying on humans alone.
How artificial intelligence will impact the insurance industry?
AI is enabling insurers to apply machine learning, data modeling, and predictive analysis to the entire insurance value chain, and the results have been favorable in the form of an increased bottom line and enhanced customer satisfaction.
How data helps insurance companies?
Insurers use big data in a number of ways. Insurers can use it to: More accurately underwrite, price risk and incentivize risk reduction. Telematics, for example, allows insurers to collect real-time driver behavior and usage data to provide premium discounts and usage based insurance.
How is data analytics used in insurance?
Using the plethora of data now available, here are some ways predictive analytics in P&C insurance will change the game in 2023: Pricing & Risk Selection, Identifying Customers at Risk of Cancellation, Identifying Risk of Fraud, Triaging Claims, Focusing on Customer Loyalty, Identifying Outlier Claims.
Why is analytics important in insurance?
Applying analytics to this data is helping insurers get the insights they need to personalize products and services, improve operations, make faster and more strategic business decisions, and drive more value across the insurance value chain.
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AUTHOR: Shay Alon – MD, Global Lead for Accenture’s Customer Engagement Products and Platforms
