Using AI to personalize insurance policies for your clients

The insurance industry has historically been slow to embrace technological innovations. However, artificial intelligence (AI) is revolutionising the industry by enabling companies to offer personalised policies tailored to the unique needs of clients. Insurers are beginning to realise that insurance policies are no longer about one-size-fits-all, instead, policies can be customised based on the lifestyle, preferences and behaviour of clients. AI also enables companies to boost accuracy and makes premium calculation more efficient.

Technology makes it possible for insurers to collect vast amounts of client data such as personal information, demographics, past claims, risk probabilities and market trends, and then use machine language (ML) algorithms to analyse the data. The results include valuable insights that human underwriters may miss. The insights can then be used to create customised coverage options after understanding patterns and relations within the data. Companies can also use the information to address the risks posed by clients and in the bargain, safeguard their own interests too. When insurers incorporate AI into the underwriting process, the chances of undercharging high-risk clients or overcharging low-risk ones are reduced.

How is AI used to personalise insurance policies?

AI tools have garnered a lot of attention in the insurance industry for claims processing, underwriting and fraud detection. Here is a more detailed discussion.

  • Data collection and analysis: AI algorithms can gather data from various sources, including social media, IoT devices and traditional sources like application forms and claims histories. This data is then analysed to identify trends, assess risks and predict future behaviour.
  • Risk assessment: AI can help insurers assess risk more accurately by analysing a client's data in real time. For example, AI can analyse a driver's behaviour, such as speed, braking patterns and location, to determine their risk of being involved in an accident. This information can then be used to adjust premiums accordingly.
  • Tailored coverage options: Based on the data collected and analysed, AI can recommend tailored coverage options that meet the specific needs of individual clients. For example, AI can suggest additional coverage for a homeowner based on factors such as the location of their property, the value of their belongings and their lifestyle.
  • Personalised pricing: AI can help insurers offer personalised pricing based on a client's risk profile. For example, a client who demonstrates safe driving behaviour may be eligible for lower premiums as compared to a high-risk driver.
  • Claims processing: AI can streamline the claims process by automating tasks such as document verification and fraud detection. This helps insurers process claims faster and more efficiently thereby improving the overall customer experience.
  • Customer service: AI-powered chatbots and virtual assistants can provide clients with instant support, answering questions in real time and providing information about their policies. This helps insurers provide a more personalised and responsive customer service experience. By using sentiment analysis and natural language processing, chatbots powered by AI can grasp client requirements and even provide recommendations that align with their specific circumstances. Such services not only boost the customer experience, but also allow insurers to focus on other complex tasks.
  • Predictive analytics: AI can use predictive analytics to forecast future trends and behaviours, helping insurers anticipate their clients' needs and offer proactive solutions.

Challenges and risks

Security: With so much emphasis on data collection from all possible sources, data security has become a huge challenge and will continue to do so. Insurance companies must adopt robust security measures to ensure that the data is protected from cyberattacks or any other security threats.

Bias: It is also important to ensure that no unintended biases creep into the decision-making process and that can be achieved by ensuring that AI and ML algorithms do not discriminate against any particular individual or group. Algorithms must be designed and tested ethically and responsibly. After all, algorithms will be as unbiased as the data used to train them. Certain clients should not feel alienated or discriminated against in terms of coverage or premium pricing.

Future of AI-powered insurance

AI algorithms will continue to advance and evolve with time and enable insurance companies to continue meeting the needs of their clients. The understanding of client needs, and risk profiles will get deeper and more detailed with AI being able to gather data from almost any electronic platform and device, for example wearable devices, online transactions and social media interactions, to name just a few. The wealth of data will continue to be leveraged so that insurance policies can be uniquely suited to individual clients at some point in the future. Recommendations would get more specific and unique, and potential gaps in coverage would be identified and managed. All of this would translate to greater customer satisfaction, increased retention rates and streamlined operations, ultimately leading to a more efficient and profitable business.

And finally, a note of caution. Insurance companies must function with caution and ensure all offerings are compliant with the existing rules and regulations of the land.

How can Infosys BPM help?

Infosys BPM’s Insurance BPM Services comprises a robust practice that includes partnerships with over 45 insurers. Our collaborative approach focuses on cost reduction for our clients while driving a transformative journey for them. Our goal is to align their business processes so that they can exceed customer expectations, and meet market demands and the continuously evolving needs of intermediaries.

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