The insurance industry: How does AI change insurance distribution?

According to a recent PWC survey, 25% of insurance companies report the widespread adaptation of insurance across their value chain. Another 54% have begun implementing AI capabilities and are looking to scale up [1]. Artificial intelligence is already revolutionizing the insurance industry by providing enriched solutions that vastly improve collaboration, remove manual dependencies, gauge risks, and offer tailor-made insurance pricing. This benefits not only the insurance companies but also their customers.

Below, we’ll look at the impact of AI on insurance distribution and future trends that could revolutionize the insurance industry. Read on for more insights.

The role of AI in insurance distribution

The growing competition among major players in the insurance sector has prompted insurance companies to adopt new ways to provide value-added services at lower costs in a bid to meet customer expectations and remain competitive.

Investments in AI capabilities like deep learning, machine learning, and big data analytics are currently a top priority among decision-makers in the insurance industry, with 76% of executives reporting that the stakes for innovation in these capabilities have never been higher [2].

A McKinsey report estimates that AI implementation and its potential use cases across the insurance value chain could rack in about $1.1 trillion in potential annual profits in the insurance sector [3]. Here’s how AI impacts the insurance value chain:

Read more about AI in the insurance industry

Streamlined claims processing

AI-driven automation capabilities have the potential to drive the best ROI for standardized, repetitive, and attention-demanding workflows. A good example is claims management. Claims management is largely paper-based and rarely digitized end-to-end. The result? Insurance companies lose up to 50-80% in premium revenues due to inefficiencies in claims management processes [4]. And since it is a primarily manual undertaking, claims management is also prone to errors and inefficiencies, which could increase operating costs.

At the beginning of 2021, many insurance companies started investing heavily in emerging technologies such as AI, RPA, and IoT to boost operational efficiency. The recent significant increase in connectivity across various systems such as telematics, fitness trackers, and other IoT devices has enabled insurance companies to collect more comprehensive customer data.

They can then infuse collected data into their underwriting and claims management processes to make them faster, more inclusive, and less prone to error. Essentially, the more data they collect, the better equipped they are to make data-driven decisions with minimal risks.

However, larger data volumes require more advanced and secure means of processing and analyzing them- that’s where AI algorithms come into play. AI capabilities like machine learning can analyze collected data and provide faster claims processing.

What’s more impressive is that with time, machine learning and deep learning algorithms can train themselves on collected data without necessarily requiring explicit programming. The resulting effect is faster and more accurate insights.

Implementing other AI-powered solutions such as AI chatbots can boost engagement, improve customer satisfaction, collect and analyze customer data, and even help in claims processing while streamlining workflows and reducing operational costs.

Faster and more accurate underwriting

Insurance companies have traditionally used risk engines and rule-based evaluation to provide premiums estimates. But as insurance scenarios get more complex and fraud methods more elaborate, these methods no longer suffice.

Fortunately, given the rise in connectivity across all sectors, insurers who leverage digital assets can now devise better and more effective ways of doing appraisals. Technologies like IoT and computer vision can help insurers effectively record an asset’s state at the time of appraisal and keep making adjustments in real-time.

For example, by incorporating a Geographic Information System (GIS) into your analytics system, insurance companies can eliminate in-person property inspections and monitor the state of the property over time to adjust the price of the customer’s policy.

The system can also be used to assess industrial infrastructures for operational mishaps and damages. Sectors like the oil and gas industry generate terabytes of operational data on a daily basis. Insurance companies can collect and use the data in predictive analytics, performing automatic defect inspections, anticipating the levels of degradation, and predicting potential future failures. This enables them to adjust insurance premiums accordingly.

Rapid document digitization

Insurance companies still largely rely on paper-based documents, which are inefficient and contribute to office clutter. By leveraging technologies like OCR and AI capabilities like automation and machine learning, they can effectively reduce operational inefficiencies and improve service delivery.

For instance, instead of entering customer data manually into their systems, insurance companies can use OCR to accurately capture and reconcile data from paper-based documents whilst augmenting the data with information from other sources.
Also, when combined with computer vision, OCR can accurately render every bit of information, translate it to a corresponding digital input, and validate the submission against other entries in the common database. The increased level of automation leveraged can drive cost savings up by 80% for individual processes [5].

OCR is also very beneficial in customer onboarding. Insurers can collect all necessary data from ID photos and other submitted documents and add it to the customers’ profiles in seconds. According to a 2021 report by EY Insurance Industry Outlook, 69% of insurance customers prefer to buy insurance online [6]. By leveraging AI-powered OCR technologies, insurance companies can onboard customers digitally through mobile apps and web portals.

Fraud detection and prevention

Insurance fraud doesn’t just affect insurance companies alone-it also extends to the customers. While insurance companies lose up to $40 billion a year (not accounting for health insurance fraud), customers lose at least $80 billion a year to insurance fraud [7][8].

It’s not surprising to learn that these staggering losses occur due to the fact that most insurance companies rely on outdated rule-based systems, which are literally incapable of detecting well-orchestrated insurance fraud. AI capabilities like machine learning and deep learning help address the shortcomings of these legacy systems and provide human employees with valuable intel that ultimately improves the rate of fraud detection, even on well-orchestrated schemes.

Artificial intelligence technologies can, for example, detect recurring patterns in claims data, making them strong contenders to legacy systems in terms of capturing irregularities in claims data. These systems also come in handy in fraud prevention by running automatic background checks during the onboarding process and accurately calculating the risks involved with individual customers and businesses.

Accelerated claims adjudication

Faster claims processing is beneficial to both customers and insurers. By leveraging AI capabilities, insurers can significantly reduce the time it takes to process a claim and eliminate the dangers associated with in-person property inspections.
In the US, for example, it’s estimated that insurance adjusters are four times more likely to get injured than construction workers [9]. To curb this, insurers can use AI systems and other data collection technologies to gather evidence and make appraisals much safer and faster.

For instance, property adjusters can use drones to safely and efficiently assess roof damage. When combined with other technologies like GIS and IoT, this gives a wholesome picture of the property and provides more accurate appraisals with machine learning and deep learning algorithms.

Offering competitive premius for drivers

The global automotive insurance industry was valued at $739.30 billion in 2019[10]. Considering the fact that people bought 66.7 million vehicles in 2021, up from 63.9 million vehicles in 2019, [11] it’s clear that the automotive insurance sector is ripe for business.

That being said, the advent of connected vehicle technologies like telematics has created a new avenue for insurers to offer more competitive premiums and reinvent their business models to cope with increasing consumer demands.
AI-powered technologies like machine learning and data science play a major role in providing predictive cost analytics for auto insurance companies. This is because they give accurate average cost estimates for different customer segments, which can help them adjust premiums pricing and manage cash flow.

Likewise, insurance companies can monitor driver performance by analyzing the drivers’ behavior through various connected systems like telematics, internal and external cameras, and ADAS systems. The information gathered can help insurance companies in the KYC process, which ultimately results in better insurance pricing and personalized services.

AI trends shaping the future of insurance distribution

Data collection from IoT-connected devices

Over the past decade, smart devices like fitness monitors, smartwatches, and home assistants have become integral to our daily lives. Some experts even predict that medical devices and smart clothing may soon join the bandwagon.

Insurance companies can use data from these devices to better understand their customers. The result will ultimately be more personalized service offerings and tailor-made insurance pricing based on individual customer assessment as opposed to demographic classifications.

Data ecosystems

Data-driven organizations are 23 times more likely to attract and acquire new customers [12]. Also, businesses that take advantage of big data analytics drive profits up by about 8%. As more companies realize the importance of data in improving service delivery and boosting profits, we’re likely to see an increase in cross-sector data sharing, with the insurance industry as the major beneficiary.

Various data-sharing platforms use a common cybersecurity framework to grant access to numerous users. Companies like Amazon, Google, and Apple are already using these platforms to gain useful customer data. By sharing collected data, these companies gauge customer preferences and general behaviors. Likewise, insurance companies can use the platforms to gain said data and provide personalized real-time assistance to their customers.

Widespread digitization

Insurance companies are at the forefront of adopting digital strategies. Digitization not only helps them boost cost savings and operational efficiencies but also increases customer satisfaction. With 74% of insurance customers reaching insurance companies online, we’re likely to see more insurance companies embrace digitization in the future.

On the downside, transitioning from paper-based to digital service delivery is not easy. In fact, 9 out of 10 insurance companies say they’re struggling to develop the digital infrastructure they need to make the change, with a wide majority citing legacy software as their main pain point.

On the upside, companies that have managed to adopt digitized services have managed to improve service delivery and eliminate obsolete roles like insurance brokers. Apart from vastly improving their service delivery and dealing with customers directly, the level of automation that comes with digitization can significantly cut operational costs.

Advances in cognitive technologies

Cognitive technologies like deep learning and neural networks, primarily used for voice, image, and unstructured data processing, might evolve into numerous other use-cases. These cognitive technologies, which mimic the human brain’s ability to learn, may become the standard approach for processing the vast and complex data streams generated from various connected systems and data ecosystems.

The rapid adaptation and commercialization of these technologies give insurance companies access to AI models that are constantly learning and adapting to the world around them. Ultimately, this will result in the inception of new product categories and engagement techniques. Insurers will also be able to respond to shifts in underlying behaviors and risks in real-time.

Summary

The insurance industry has seen a dramatic change from the traditional service delivery models that typically involved overreliance on paper-based documents and assessing risks based on demographic data to more digitized and personalized models. AI has already made significant improvements in insurance distribution. As more companies implement AI capabilities, the industry is bound to see major improvements for insurers and customers alike.

References

[1] Pwc.com. A Business Survey. URL: https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-business-survey.html. Accessed August 22, 2022
[2] Accenture.com. Accenture Technology Vision for Insurance. URL: https://www.accenture.com/_acnmedia/PDF-120/Accenture-Technology-Vision-for-Insurance-2020-Summary.pdf. Accessed August 22, 2022
[3] Mckinsey.com. Our Insights. URL: https://www.mckinsey.com/business-functions/quantumblack/our-insights/the-executives-ai-playbook?page=industries/insurance/. Accessed August 22, 2022
[4] Mckinsey.com. Successfully Reducing Operating Costs. URL: https://mck.co/2rrIBVz. Accessed August 22, 2022
[5] Mckinsey.com. Evolving Insurance Cost Structures. URL: https://www.mckinsey.com/industries/financial-services/our-insights/evolving-insurance-cost-structures. Accessed August 22, 2022
[6] Assets.ey.com. Global Insurance Outlook. URL: https://go.ey.com/3PCvb1a. Accessed August 22, 2022
[7] Fbi.gov. Insurance Fraud. URL: https://bit.ly/3R4JNaE. Accessed August 22, 2022
[9] Itprotoday.com. Kespry CEO Takes a Bird’s Eye View On AI. URL: https://www.itprotoday.com/iot/kespry-ceo-takes-bird-s-eye-view-artificial-intelligence. Accessed August 22, 2022
[10] Alliedmarketresearch.com. Auto Insurance Market. URL: https://bit.ly/3cktYhj. Accessed August 22, 2022
[11] Statistica.com. International Car Sales Since 1990. URL: https://www.statista.com/statistics/200002/international-car-sales-since-1990/. Accessed August 22, 2022
[12] Mckinsey.com. Growth Marketing and Sales. URL: https://www.mckinsey.com/business-functions/growth-marketing-and-sales/our-insights. Accessed August 22, 2022