6 Ways Machine Learning and AI are Transforming the Insurance Industry

6 ways AI is transforming insurance



The insurance industry has undergone a significant transformation in recent years, thanks to technological advancements, particularly in the realm of machine learning and artificial intelligence (AI). Machine learning in the insurance industry has brought about a revolutionary shift. This transformation empowers insurers to make informed decisions by harnessing the full potential of their data.

Machine learning algorithms play a pivotal role in this evolution. These algorithms possess the remarkable ability to foresee future outcomes, identify anomalies, and streamline various insurance processes through data-driven learning. AI ML in insurance leverages historical data specific to the domain. These algorithms unravel patterns, uncover correlations, and spot trends that might elude human analysts.

The insurance sector has been sitting on a largely unused treasure trove of big data because it lies scattered across disparate systems. The implications of machine learning in insurance are profound. It goes beyond mere data analysis; it empowers insurers with invaluable insights for shaping pricing strategies, optimizing claims management, fortifying fraud detection mechanisms, and ultimately enhancing the overall client experience. In essence, machine learning and insurance have become inseparable partners in navigating the ever-evolving landscape of risk management and financial protection.

According to Precedence Research, as of 2023, the AI market is projected to surpass the $500 billion milestone. In 2022, North America held a dominant position in the global AI market, accounting for 43%. Supportive government policies and increasing demand for automation by businesses has resulted in America leading adoption. 

Areas for transformation

With the global AI market estimated to grow at a CAGR of 42.2% to $733.7 billion by 2027, AI and machine learning use in insurance are only bound to grow. Based on current trends, here are 6 ways in which AI and machine learning may find fast-tracked adoption in the insurance industry

Machine Learning And Ai Transforming Insurance Industry

1. Machine Learning in Insurance Underwriting

The underwriting process in insurance depends heavily on data and analytics. It involves risk analysis and pricing, making underwriting an integral part of the insurance process. Traditionally, this vital task was entirely dependent on humans. Using Insurance machine learning, the process of underwriting has become easier, quicker, and more accurate.

Intelligent underwriting algorithms can analyze customer information to create reliable customer profiles and detect risk more efficiently. Some uses of AI for risk analysis are

  • Photo analytics to verify damages for property and vehicle insurers

  • Geospatial imagery for property risk analysis, deep learning- and data science-driven risk comparison systems. Insurance companies can reduce risk exposure as well as improve the pricing mechanism of policies.

  • Machine Learning in insurance underwriting will move the benefit along to customers by charging the right rates for each applicant – personalization at its best.

Also Read: The 4 Disruptive Technologies Reshaping Insurance Operations

2. Improved claims management

Claims management, though an integral part of the insurance industry, is a convoluted and cumbersome process. Settling a claim involves processing swathes of data and interacting with many stakeholders such as the claimant, insurance agent, underwriters, brokers, and financial institutions. AI/ML applications can automate routine data checks as well as interactions, clearing up time for agents to focus on other important tasks.

Claims management software can streamline end-to-end processes, right from data scanning and processing to verifying policy details and identifying gaps or errors.

For example, Japanese insurance company Tokio Marine incorporated an AI-based claims document system to upload handwritten notices onto its cloud-based system. Due to the advances in machine learning and natural language processing, this AI-based system reduced input time by 50%, cut human error by 80% and fast-tracked claims payments for the company.

Another advantage of ML applications in claims management is the increased data security for customers. Using these advanced technologies, insurance companies can make the claims management process hassle-free.

Also read: Everything You Needed to Know About AI for Insurance

3. Fraud detection using insurance machine learning algorithms

In the United States alone, insurance fraud (non-health insurance) costs the industry over $40 billion in losses each year (Fbi.gov stats). From premium diversion to impersonation; scammers use numerous ways to commit insurance fraud. Insurance fraud detection using machine learning can be an efficient way to identify these scams.

Deep anomaly detection is a popular ML application to detect fraud. By analyzing large data sets of genuine claims, Insurance machine learning models train to identify typical insurance claims. Any deviation from typical claims will be flagged as potential fraud, thereby enabling insurance companies to reduce risks and costs related to fraud and achieve superior results.

To cite an example, one of Turkey’s largest insurers, Anadolu Sigorta, had a team of 50 people to check every claim using a set of loose rules and their own personal experience. The predictive analytics tool in the software-enabled real-time risk assessment speeded up the process and reduced errors. After the change, Anadolu Sigorta insurer saw a 210% ROI within a year of switching to the AI solution.

Also Read: The Rise of Open API Ecosystems is Making Insurance Processes Bullish

4. Better customer support in insurance

Relationship between AI, ML and Deep Learning

Have you ever been confused about the difference between machine learning and artificial intelligence? Machine learning is a subset of AI, where machines continuously learn from past data without any explicit programming. However, if there is any change in customer behavior, for example, then the algorithm has to be fed with the new data to build new patterns.

Artificial Intelligence, on the other hand, is used where adapting to new scenarios is required, AI is smart and can teach itself. Deep learning in insurance combined with other applications like interactive bots can improve customer-facing operations multifold. For instance, AI-based chatbots can provide speedy responses to routine insurance queries with higher accuracy than human agents. In fact, 40% of customers don’t realize that it isn't a human answering their questions in the online chat.

Additionally, using chatbots, insurance companies can save almost $1.3 billion by 2023 across motor, life, property and health insurance, according to Juniper Research.

5. New business and lead generation

AI software can help marketers collect and analyze valuable information from different sources such as website visits, chatbot conversations and social media channels. This data, when combined with tailored lead interactions, personalized recommendations and timely suggestions can help drive new business. AI software can not only point out potential leads but the best ones can also indicate which customers are ready for a sale.

Using NLP and deep learning in insurance helps in analyzing large amounts of routine conversations. The chatbots can collect data around individual preferences, behaviors, past purchasing habits, age groups, and income, all of which can help provide personalized product recommendations. This also opens up possibilities for cross-selling and upselling policies.

6. Enhanced regulatory compliance

The regulatory landscape of the insurance industry is complex and ever-changing. One of the biggest challenges that insurance companies face is keeping pace with regulatory changes and incorporating these into policies. For instance, the relatively new regulations of the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have raised the stakes for data protection and privacy for insurers. NLP technologies can help scan internal policies as well as claims documents to check their compliance with different regulatory policies.

AI and ML in insurance can augment human intelligence to help companies avoid regulatory missteps that could cost them millions in non-compliance penalties.

Statistics for AI in Insurance

The final word

If Google or Amazon were to venture into the insurance industry, imagine what a shake-up that would be because of their advanced technology. This should be a major driver for insurance carriers to adopt modern technologies to cope with industry setbacks, keep ahead of competitors while also improving their productivity.

Looking for a proven technology partner? Our Enterprise Insurance Solutions are built by insurance professionals who understand your needs; not just programmers. It hasn’t happened overnight; it spans a 20-year evolution, partnering with leading insurance carriers. Talk to us today about your challenges.

Topics: A.I. in Insurance

Antony Xavier

About The Author

Antony Xavier

Antony is the President and Co-Founder of SimpleSolve Inc. a company delivering innovative technology solutions to the insurance industry for over 20 years. He brings his decades of experience in finance, insurance and technology to develop modular and configurable enterprise-grade insurance platforms leveraging emerging technologies that bring true value to the industry. Outside of work, Antony spends time traveling, fishing and in the kitchen experimenting with gourmet cooking.

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