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Source: Global Fintech Series

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The innovation of new technology has impacted many sectors; the insurance sector is no exception to this trend. The ongoing wave of Artificial Intelligence has had a significant impact on the insurance industry as it transforms traditional insurance operations and enhances customer experience and risk management processes. In this article, we will explore how AI is reshaping Insurance Industry.

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The Evolution of InsurTech

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The use of technology in the insurance industry has evolved a lot over the last few decades. The earliest use of AI for insurance processes dates back to the 80's, used for underwriting and accurate policy pricing. While the technology back then was not as sophisticated and complex as current tools, it helped lay a foundation for more advanced applications.

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The emergence of more powerful computers, digital data collection and advancements in machine learning algorithms pushed more mainstream usage of AI in insurance processes. The overall digitalisation of the insurance sector has helped eliminate intermediaries, highlight the inefficiencies of traditional methods, offer more personalised and accurate services, estimate risks accurately, and make policies more accessible and seamless.

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Current Impact of AI on InsurTech

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Source: ET

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The ability of AI to process large amounts of data, machine learning and predictive analysis is changing every aspect of insurance for policymakers and customers.

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Refined Underwriting Process

Conventional underwriting processes involve a lot of manual labour and are usually time-consuming and less accurate. Furthermore, they need a lot of resources and leave room for human error and bias as they rely on the competence and experience level of the underwriter. These reasons make the traditional methods inefficient and unsuitable for the current market needs.

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AI algorithms can look through large data sets, including credit scores, health records and other information, to make more accurate risk assessments. It enables insurance companies to provide tailor-made services that suit user needs. A case study by Accenture found that up to 40% of underwriters spent their time on non-core and administrative activities.

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Deploying AI to perform these tasks will help reduce the time spent on them and free up underwriters to focus on more crucial tasks. According to the case study, implementing an AI-based system led to a 10% improvement in claims accuracy.

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Furthermore, with the help of AI, underwriting can be done within a matter of a few minutes, if not seconds, which reduces the issuance time for policies and boosts customer satisfaction.

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Improved Claims Processing

Another area where AI has been impactful is claims processing. Generally, it is a labour-intensive, error-prone, lengthy process and just like underwriting, it depends on the knowledge and judgement of a human.  

AI models can check the claims data for inconsistencies, identify potentially fraudulent claims and save insurers billions of dollars annually. The report by Accenture also stated a 73% increase in claims process cost efficiency in the AI-based system.

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Personalised Customer Experiences

With the growing number of digital products, customer experience has become a key differentiator. AI helps insurers provide personalised and touchless experiences while boosting customer satisfaction and loyalty. Here's how AI-based customer experiences are impacting InsurTech:

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AI Chatbots and Virtual assistants:‍

AI Chatbots and Virtual Assistants are on the rise as they can provide customer support 24/7 and assist with queries. These virtual assistants can offer tailored recommendations based on user data and preferences.

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Personalised Experiences

‍AI algorithms can analyse behavioural patterns and preferences to present customers with products and services more suited to their requirements. Usage-based models, pay-as-you-drive policies, and leveraging telematics to customise premiums are all great examples.

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A case study by McKinsey predicted 5-15% increases in revenue and 10-30% increases in marketing-spend efficiency through personalised product recommendations.

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Risk Management and Predictive Analysis

Risk management is at the heart of the insurance business. AI assists insurers in mitigating risks through accurate assessments and comprehensive predictive analysis methods.

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Predictive Modelling

‍AI models are capable of analysing historical data and identifying patterns to get an estimate of potential liabilities. It helps companies take on calculated risks and prepare for future losses, improving financial planning and stability

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Live Monitoring

‍AI works with IoT devices to monitor real-time data like driving info, weather conditions and health reports for effective risk management strategies.

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Increased Operational Efficiency and Cost Reduction

Apart from standard insurance processes, AI can be used for designing efficient workflows and minimising repetitive tasks, allowing insurers to allocate resources more effectively. Below are some ways in which AI helps enhance company-wide operations:

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Process Automation

‍AI automates routine tasks like data entry, document processing and compliance checks, allowing employees to direct their time and attention towards more important ones.

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Reducing Errors

‍AI minimises the scope and margin for human errors, improving efficiency and reducing costs associated with claims processing, underwriting and customer service.

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IBM conducted a study about the adoption of AI in organisations. It was found that executing AI can provide a cost efficiency of 44%, showcasing the fiscal benefits of AI integration.

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Challenges in AI Adoption in InsurTech

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Despite the several benefits it has, adopting AI in the insurance industry at present poses multiple challenges.

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Source: Appinventiv

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• Data Privacy and Security: AI models parse large amounts of users' data and have raised concerns surrounding the usage of AI globally. The study done by IBM found that nearly 52% of companies that have employed AI have not taken any steps to address privacy concerns.

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• AI Bias (Poor Data Quality): AI algorithms can unintentionally introduce biases, leading to unfair decisions - during underwriting and claims processing. As many as 74% of organisations do not take active measures to reduce unintended bias.

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• Integration With Legacy Systems: Many companies are still operating in older modules of technologies and integrating AI technologies with these systems can be challenging and requires significant investment and technical expertise.

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• Lack of Skilled Talent: Adopting AI calls for a group of highly qualified experts, including machine learning specialists, data scientists, and AI specialists. Such expertise is in limited supply in today's work market, which makes it difficult for companies to hire and keep competent staff. A poll published in the Harvard Business Review claims that a number of variables, such as a lack of talent or problems with integration, can hinder the adoption of AI.

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Source: Deloitte 

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• Regulatory Compliances: With AI solutions for organisations becoming more and more popular, there are more and more legal regulations to follow. Therefore, businesses must adhere to these limitations and governance norms, especially if they are involved in highly regulated industries like healthcare and banking.

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• Implementation Cost: The deployment of AI is a costly undertaking, particularly for small and medium-sized enterprises, whether they are building a new AI-powered infrastructure from scratch or simply wish to enhance the current one. Therefore, many firms may be discouraged by the initial cost of investing in technology.

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• Lack of Understanding: Because they are fearful of losing their jobs to AI implementation and lack a thorough understanding of the technology's true potential, some stakeholders and employees are reluctant to deploy AI.

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For more details - Insurance website Challenges & Insurtech UX Strategies

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Future Trends in AI for InsurTech

AI is something that is yet to be explored to its full potential. With that comes the question, what does the future hold for AI in InsurTech?

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Blockchain and AI integration

‍The integration of blockchain technology with AI can enhance data security and transparency in insurance transactions. This combination promises to revolutionise how insurers handle data and execute contracts.

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Predictive Analysis

‍Predictive analytics powered by AI will enable insurers to anticipate customer needs and market trends. This will help in developing proactive strategies and offering more tailored insurance products.

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Legacy Systems Solution

‍AI can be used to extract the data and the logic from legacy systems while API enabling them to feed into AI solutions.

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Personalised Insurance Products

‍AI allows for the creation of highly personalised insurance products based on individual customer data, preferences, and behaviours. This leads to more relevant and customer-centric offerings.This could enable insurers to offer preventative measures or early intervention strategies to mitigate losses and improve overall risk management.

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Insurance as a Service (InSaaS)

‍AI can help create innovative insurance models based on usage or behaviour. Imagine car insurance that charges based on actual miles driven or health insurance that offers discounts for maintaining healthy habits.

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Case Studies: Successful Implementations of AI in InsurTech

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Lemonade

Source: Lemonade

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Lemonade Inc. is an American insurance company that takes leverage of AI for insurance.Lemonade uses AI to streamline the claims process, offering fast and efficient customer service. Their AI-driven platform reduces administrative costs and improves customer satisfaction.

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According to Tim Bixy their Chief Financial Officer (CFO) Lemonade is “AI Native” and the firm's “real advantage lies in the depth of data that we collect about each customer when we onboard them and when they get a quote and a policy” as stated in an interview with PYMNTS

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Root Insurance

Source: Root Inc.

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Root Insurance comes with a vision of bettering lives through better insurance, Root was founded in 2015 with a focus on calculating car insurance rates to be based primarily on driving behaviour and not the demographics.

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Root has partnered with Tractable AI, which specialises in AI for accident and disaster recovery, as its strategic partner to fulfil its ambitious AI strategy across its claims process. 

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Niva Bupa

Source: Niva Bupa

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Niva Bupa (formerly known as Max Bupa) is India’s leading health insurance provider which has leveraged the use of AI in their products, boasting more than 50 percent of their policies getting issued by AI and no human being involved. About 90 percent of the renewal happens online. Now they are also using AI for fraud detection, automation, and driving business revenue.

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Conclusion

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Summary of AI’s Impact on InsurTech

AI is transforming the InsurTech industry by enhancing customer experiences, improving risk assessment, and detecting fraud. Despite challenges such as data privacy and integration with legacy systems, the benefits of AI far outweigh the drawbacks.

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Future Outlook

The future of AI in InsurTech looks promising, with trends like predictive analytics, personalised insurance products, and blockchain integration set to redefine the industry. Insurers who embrace AI will gain a competitive edge and better serve their customers.

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Final Thoughts

As AI continues to evolve, its impact on InsurTech will only grow. Insurance companies must stay abreast of technological advancements and invest in AI to remain relevant and competitive in the rapidly changing landscape. The impact of AI on the InsurTech industry is undeniable. From streamlining operations and enhancing customer experience to unlocking new business opportunities, AI is poised to reshape the future of insurance. By embracing AI responsibly and strategically, InsurTech companies can gain a significant competitive edge and deliver a future-proof insurance experience for their customers.

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