How Leading Insurtech Companies Make Use of AI Solutions?

Both traditional players and insurtech disruptors leverage their strategies on big data analysis and machine learning models for customer service automation, claim processing, underwriting, or fraud prediction.

AI is also shifting the insurance business model with hyper-personalization and value-based healthcare, while IoT is enabling a widespread use of geographic information systems for risk assessment across the insurance value chain.

The pandemic has accelerated the shift to online distribution and self-service. The companies that can build on the common AI experience will have a better chance to succeed in digital transformation.

The development of open-source frameworks drives the rise of AI in the entire insurance industry. Companies have also learned how to collect and process big data sets, which are shared among organizations – also across the sectors. Finally, automation and optimization help the best scale their operations, manage risk, and streamline the underwriting process.

Although the international expansion in insurance is constrained by local regulations, the current technological revolution has already led to an even faster consolidation. The insurance industry has experienced an increased M&A activity in recent years, and AI development as well as the pandemic could increase the trend, particularly in claims processing within the broader financial services sector.

Insurtech startups and scale-ups such as Clover Health, Fabric, GetSafe, Trov, Lemonade, BIMA, Slice, neos, ZhongAn use AI to successfully challenge traditional companies. Many tech disruptors focus on delivering their services to the insurance industry, improving risk assessments and reducing human error.

Quantemplate is a good example. The startup offers self-service, cloud-based insurance data analytics, and automation tools to large and medium-sized companies. According to the company’s statements, Aegon Blue Square Re N.V., XL Insurance, SageSure, Chubb, RenaissanceRe are among its clients. These tools provide significant cost savings and leverage diverse data sources.

Meanwhile, the most prominent players don’t stay behind, making use of advanced chatbots, fraud detection algorithms, or mechanisms improving cost-effectiveness and productivity. According to a report by Deloitte, 98% of insurance executives say that cognitive computing (models simulating the biological brain, such as neural networks) will play a disruptive role in the insurance industry, particularly in detecting fraudulent claims and enabling usage based insurance.

A year ago, we named six ways machine learning (the most popular AI application in business) changed the insurance industry: process automation, better rating algorithms, improved underwriting, predicting customer lifetime value, fraud detection, and marketing personalization. The market has advanced since then. Here is how the applications of AI in insurance have matured over time, enhancing services for existing customers and providing a competitive advantage.

AI is also transforming commercial insurance and enabling insurance carriers to innovate. Insurtech companies are enabling insurers to leverage generative AI and other advanced tools to improve their offerings.

Quantemplate’s platform, for example, enhances risk modeling and utilizes connected devices to gather data. The integration of artificial intelligence (AI) and ai technologies is driving significant changes in the industry.

The importance of data driven insights and high quality data cannot be overstated. Emerging technologies and data driven analysis are crucial for the future of the industry.

AI in insurance trends common with other industries

Insurance uses AI for recommendation engines, marketing automation, and retention management systems. AI-driven chatbots and virtual assistants help insurers ease the burden of standard customer service, just like in fintech companies, where AI-based communication solutions such as Cleo, Eno, or Wells Fargo Bot work great to enhance the customer support process.

However, the greatest value lies in automating core insurance processes such as profiling and underwriting, thereby improving operational efficiency. In the next part of the article, we’ll take a closer look at the trends specific to insurtech, where AI is used to automate some key processes, improve essential KPIs, or turn the current business model upside down.

Insurance hyper-personalization

This is one of the strongest disruptions in the industry. In healthcare or car insurance, big data analysis is used to assess each individual’s risk, providing data driven insights that enable more personalized service.

Wearables and telematic devices collect and send data about customers’ lifestyles or driving habits. The data is used to train neural networks to predict the probability of an accident. Customers opt-in for this program to earn a substantial premium discount, while insurers can estimate risk better. It's a mutual benefit.

Wearables are conquering the private healthcare insurance market. UnitedHealthcare Motion is a wellness opt-in program by an American insurance company that nudges participants into a healthy lifestyle.

Vitality is a behavior change platform launched by the South African insurer Discovery, and it’s also present in the US and the UK. Customers buying Vitality Heat insurance get a deal on an Apple Watch and can collect "activity points" for walking, running or having their blood pressure checked. The program is targeted at employers who want to improve the health of their teams.

While insurance personalization opens possibilities for microsegment marketing and timely assistance, privacy concerns limit hyper-personalization.

A Gartner survey among over 2,500 US customers showed that 38% of them would stop doing business with a company if they would find their efforts towards product or services personalization "creepy", or in other words too intrusive, making them feel uncomfortable. Insurance companies offering such products need to bear in mind that hyper-personalization is more a communication than technological challenge. As Luca Russignan, EY global insurance knowledge leader, points out, when it comes to trends “storytelling is more important than data-driven approaches”.

Value-based healthcare insurance

A value-based system may be more beneficial for both the insurance companies and customers than the traditional one. In value-based healthcare, risk management and AI-driven solutions play a crucial role in enhancing patient outcomes and operational efficiency.

In private healthcare systems, insurance companies are the primary payers. Customers pay their monthly premiums to be served in case of getting sick. When it happens, the insurance pays the clinic or hospital for the treatment. Data science is pivotal in analyzing patient data to optimize treatment plans, leading to significant cost savings for both insurers and patients.

In the US, this payment-per-procedure model proved to be unsustainable. The cost of healthcare is ballooning, while the results are shaky. The critics point out the system is mostly beneficial to pharmaceutical corporations and hospitals at the customer and insurance company’s cost. AI-driven technologies can help detect fraudulent claims, ensuring that resources are allocated efficiently and reducing unnecessary expenses.

Insurtech companies see the crisis as an opportunity. According to Oscar Health CEO Mario Schlosser, the system in the US is broken. The company wants to fix it by simplifying the medical insurance experience. Clover Health uses AI models to assess the risks of each patient’s condition and reduce unnecessary hospitalization. Generative AI can further enhance healthcare by providing personalized treatment recommendations and improving patient engagement.

Value-based healthcare emphasises the effectiveness of treatment. AI drives the trend. It allows the insurance companies to pay for the procedure’s effect, not for spending three days in a hospital. Pharmaceutical companies are being disrupted, too, as they need to prove that a drug is effective. Risk modeling and data driven analysis are essential in evaluating treatment outcomes and ensuring that patients receive the most effective care.

Fraud detection

This is a typical use of neural networks in both fintech and insurtech. While real-time identification of suspicious activity can save bank customers from falling victim to theft, it is equally useful in the insurance business. AI's impact on insurance claims and the claims process is transformative, optimizing the management and resolution of claims.

Marine insurance companies use satellite photos and ML image-recognition solutions to verify a claimant’s credibility and claim integrity. Claims management is significantly improved through advanced analytics, which streamline operations and enhance decision-making.

Paris-based startup Shift delivers AI-based software that helps insurers detect fraud patterns – their software-as-a-service solution flags suspicious claims with a 75% hit rate.

According to a customer story presented by Dutch fraud detection company FRISS, Turkish insurer Anadolu Signorta reached 210% ROI within 12 months of using their platform.

IoT in property and casualty insurance

Household insurance will be affected by Internet-of-Things devices, enabling geospatial analytics in insurance, which would affect events such as in-home flooding. The proliferation of connected devices will generate vast amounts of data from various data sources, enhancing insurers' ability to gather and analyze information for better risk management.

AI is used to analyze big data sets and geographic information systems (GIS) to map risk better. The growth of GIS in insurance is driven by the popularity of IoT. Insurance companies can collect geophysical and topographical data, cross-check it with addresses to provide location-based insights. High-quality data is crucial for accurate risk assessments, ensuring that insurers can manage risks effectively.

Although GIS systems have been present in insurance for decades, only the recent development of AI has made the practical use of the big data sets possible. The inclusion of unstructured data and data-driven insights allows for more comprehensive analysis, improving decision-making processes in complex claims.

Data-driven analysis and advanced risk modeling are transforming the insurance industry. AI and machine learning leverage vast amounts of data to enhance risk assessment and underwriting processes, enabling insurers to prepare for unexpected events more effectively.

Advanced analytics play a crucial role in improving risk management. By leveraging data technology to assess risk, insurers can optimize their pricing and policies, fostering innovation and creating a culture that embraces these capabilities.

Automatic underwriting

Shift to digital distribution and self-service

Face-to-face interactions between insurance agents and customers are no longer necessary. This is true for both sales and underwriting. The lockdown caused by the COVID-19 pandemic has accelerated the changes.

In a McKinsey survey among US life insurance agents from January 2020, 90 percent of sales conversations and almost 70 percent of ongoing client conversations took place in person.

The study was done again in May, and less than 5 percent of agents had any face-to-face conversations with clients at all. The social isolation creates pressure for developing self-service models. Automation with the use of ML methods offers a great potential in this field.

Thanks to AI, customers buy insurance products effortlessly, and much faster. As McKinsey's Insurance 2030 outlook points out, AI solutions enabled the insurers to create high-quality risk profiles automatically.

By using neural networks plugged into sources coming from internal and external data providers (including reinsurers and product manufacturers), insurers can present instant quotes. As a result, a commercial, car, or life insurance purchase can take mere minutes or even seconds.

While bottom-up automation of cross-selling and underwriting is already well-developed, top-down data extraction and performance monitoring is still a challenge for many companies, the recent McKinsey "How insurance can prepare for the next distribution model" report indicates.

Wrap-up