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Artificial Intelligence in Insurance Industry
Tech Talk
Tech Talk Updated on Apr 2, 2024  |  5 Min Read

Artificial Intelligence (AI) is finding widespread usage in several industries, and the insurance sector is not immune to its impact either. The advancements in AI, in the form of Machine Learning, Deep Learning, Natural Language Processing, Generative AI, and Convolutional Neural Networks, to name a few, are bringing about a seismic, tech-driven shift.

In fact, it is believed that insurance will soon let go of its ‘detect and repair’ approach and embrace a more futuristic ‘predict and mitigate’ focus. This dramatic transformation unlocks various avenues for AI to infiltrate the insurance sector. Hence, it should come as no surprise that McKinsey projects the annual value of AI tech in insurance could touch a valuation of USD 1.1 trillion.

Artificial Intelligence in Insurance

By leveraging Artificial Intelligence in insurance, the insurance players can gain a host of benefits such as better productivity, enhanced customer experience, efficient claims management, reduced frauds, and more. Let us look at the role played by AI in the insurance industry and how it’s revolutionizing it.

Role and Use of AI in Insurance

Artificial Intelligence technology is playing a groundbreaking role as the leading transformative technology in the insurance industry. It has upgraded how insurers carry out underwriting, adjudicate and process claims, manage policies, and detect fraud – to name just a few common AI in insurance use cases. In short, it can influence the entire insurance value chain – just like predictive analytics, which in itself is a thriving example of AI in action! Add to this the fact that companies are now swimming in data, which makes it far easier to train AI/ML models to perform efficiently across the different implementations in the industry.

Much like other emerging insurance technologies, AI can be implemented across different value chains of insurance. AI in the insurance industry can help play a preventive and curative role in a number of ways. Generative AI, a subset of Artificial Intelligence, adds a new dimension to the capabilities of AI in insurance. For instance, Generative AI models can be used for simulating different scenarios and their potential impact on insurance portfolios, enabling insurance businesses to enhance risk management.

Benefits of AI for Insurance Companies at a Glance

Benefits of AI for Insurance Companies

1. Claims Reporting: First Notice of Loss (FNOL)

Based on how advanced the AI systems are, insurers can report, route, triage, and assign claims with or without any human intervention. Digital assistants, paired with Natural Language Processing (NLP) and automatic speech recognition, can both effectively and efficiently manage the First Notice of Loss (FNOL) reporting process. This not only enhances productivity but also makes processes lean.

There are numerous advantages of implementing conversational AI for insurance companies. Insurance chatbots can efficiently facilitate the claim reporting process. Customers can use the chatbot for reporting an incident from any place and at any time. The AI-driven chatbot can thereafter disseminate the information to the concerned individual for further processing.

2. Better Insurance Claims Management and Investigation

The conventional investigation method used for identifying and detecting dubious claims consumed a lot of manual effort and time. It required thorough monitoring and the following of claimants for suspicious activities. Due to the lack of time available to get the job done, this approach is not successful in a competitive industry like insurance. Also, increasing the staff for investigation adds up to the cost.

Insurers can leverage AI tools to streamline their claims assessment and settlement process. AI can regulate all of the processes from data-capturing, claims creation, authorizations, approvals, and payment to recovery tracking. When paired with other applications, such as fraud detection, companies can develop a streamlined, automated, and data-driven end-to-end claim processing ecosystem devoid of any human error or bias. All in all, adopting AI in insurance claims management and settlement will offer numerous benefits.

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3. Improved Loss Estimation for Low Claims Leakage

Businesses are migrating to digital platforms to leverage a multitude of benefits, and the insurance sector is not lagging behind. The advent of disruptive technologies like deep learning algorithms, image recognition systems, and Artificial Intelligence in the insurance sector has contributed to transforming the business landscape. By harnessing the potential of machine learning in insurance, insurers can predict or assess the damage based on the photo of the damaged object. Hence, AI for claims management has proven to be the ideal technology. Implementation of AI for insurance companies helps in the assessment of potential loss and recommends the parts that may need to be repaired. This, in turn, makes the process of loss estimation quick and efficient.

4. Optimized Routine Operations for Improved Efficiency

AI-driven chatbots have already gained traction in the sphere of customer service. In the insurance industry, these chatbots can enhance scalability and take the load off of human resources for more critical matters. At the same time, the chatbots can play to their strengths and cross-sell or upsell products depending on the customer profile and history. In short, AI can help tap into various thrust areas of improving overall customer experience.

5. Proactive Fraud Detection and Prevention

Insurance fraud accounts for losses amounting to approximately USD 40 billion per year. And this figure applies to the US alone. Naturally, this number gets magnified by many folds on the global stage. Various surveys have highlighted that a considerable chunk of insurance providers are rooting for technology to detect and prevent such fraudulent activities.

AI can play a pivotal role in finding patterns in historical data, which helps in the early detection of claims fraud and prevents it from taking place. Resultantly, insurance businesses can perform a holistic risk assessment before offering their services.

6. Personalized Insurance Policy Underwriting

The traditional insurance policy underwriting processes relied heavily on standardized criteria and were hands-on and labor-intensive. Underwriters would manually evaluate the insurance risk of a customer, based on different factors such as income, medical history, etc. By leveraging AI in insurance underwriting, insurance businesses can create personalized risk profiles for individual customers. AI algorithms can assess troves of data from an array of resources to offer meaningful insights into customer behavior and risk propensity. Insurers can also use data-driven insights to customize insurance offerings and pricing, leading to improved profitability and competitiveness in the insurance market.

Concluding Thoughts

AI in insurance brings about a wave of uniformity across different market segments, industrial verticals, and service providers. As a result, obtaining insurance and claims settlement procedures can be more standardized throughout. Other benefits of AI in insurance that we can anticipate would be greater operational excellence, lower costs, and enhanced customer experience. Clearly, the future of AI-driven insurance is a bright one, and the use of AI in the insurance industry will see a massive boost in the coming future.

Case in Focus

A leading insurance claim adjusting firm based in the USA was on the lookout for digital solutions that leveraged cutting-edge technology to enhance their Property Claims Estimation System. Damco catered to this requirement with a powerful AI-powered solution that focused on eliminating human errors. The AI-assisted property damage assessment tool introduced a no-touch approach to assessing property conditions. At the same time, it integrated with the existing digital ecosystem to allow seamless data transfers. For more details, refer to the complete case study.

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