The market for Artificial Intelligence (AI) in the insurance industry is expected to reach a valuation of USD 4.5 billion by 2026 with a CAGR of 24%. And while the industry has responded with the widespread adoption of AI technologies, it does come with its fair share of implementational challenges. The following are some issues with AI for insurance companies and possible solutions to overcome them. Need for Data Handling Training Models like machine learning in insurance get smarter the more you put it to use. But what happens when the data quality is suffering? Naturally, when a major chunk of your business decisions depends on data, the first line of action would be to ensure data hygiene. Even while training your AI and ML models, you will need a wide variety of structured and unstructured data containing inputs like historical claims, transactions, personal documents, GPS data, rich media, investigative reports, etc. Furthermore, all this information needs to be organized, labeled, and contained in their respective training datasets. Throughout this process, you would require expert data handlers who can retain and maintain data fidelity without affecting the quality. Even at later stages, while handling data, you will need to ensure that your data is insulated from dilution. As such, your team will have to undergo regular training for handling and maintaining data quality. Disparate Data and Data Silos The insurance sector heavily depends on customer data generated at various touchpoints. Whether it is the information collected by a lead capturing form or rich media associated with FNOL - customers are continuously required to share data with insurers. Hence, a major concern of using artificial intelligence in insurance revolves around making this data universally available. Since these inputs are available at different stages, the AI system can only function correctly when your organization breaks through data silos and disparate data storage frameworks. To ensure optimum performance of AI for insurance companies, the data must be centrally located with an active data validation and updating system to keep it consistent and uniform. Technology and Vendor Selection As AI continues to grow sharply through the insurance sector, several vendors have entered the market to woo this crowd. Several vendors are trying to harvest the hype and make big gains by pushing for big investments. Insurance companies have yet to understand the nitty-gritty of the technology involved but are afraid of missing out on the next high-tech wave do not wish to waste any more time. This pressing FOMO pushes them to accept recommendations at face value from domain “experts.” However, a lot goes into becoming an AI expert for insurance companies and claiming to be one. Any vendor worth their salt will first carry out an assessment to evaluate the scope for introducing AI in various processes. Next, they will audit the existing process to carry out a cost-benefit assessment to measure the impact of AI or machine learning in insurance processes. Finally, they will formulate a detailed roadmap of how the transformation will take place. Additionally, the vendor will aid you in determining the technologies, offer training and onboarding, and reliable post-sales support. Organizational Support Finally, when it comes to introducing new technology within a company, organizational support plays a crucial role in determining the success of its adoption and implementation. Smooth AI integration requires the active involvement of company leaders and the C-suite to inspire an institutional change. These industry leaders will also have to ensure that infrastructural, training, upskilling, and other forms of support are made readily available to the workforce to eliminate any form of hesitance one may have towards this change. The leaders will have to convince and inspire others that the use of AI for insurance companies is not an imposed tool but a facilitator to enhance productivity. The top-down trickle approach will build and sustain momentum while accepting change. Conclusion Quite often, businesses tend to overlook the challenges that come with artificial intelligence or machine learning in insurance and focus merely on the opportunities presented by the technology. However, having an understanding of the potential obstacles can be an excellent way to prepare oneself holistically. As one can see from above, artificial intelligence in insurance may present a few challenges but none of them are insurmountable. Eventually, it is all about persistently working towards the goal of embracing artificial intelligence and you will reap several benefits in the long run. The market for Artificial Intelligence (AI) in the insurance industry is expected to reach a valuation of USD 4.5 billion by 2026 with a CAGR of 24%. And while the industry has responded with the widespread adoption of AI technologies, it does come with its fair share of implementational challenges. The following are some issues with AI for insurance companies and possible solutions to overcome them. reach a valuation of USD 4.5 billion USD 4.5 billion by 2026 Need for Data Handling Training Models like machine learning in insurance get smarter the more you put it to use. But what happens when the data quality is suffering? Naturally, when a major chunk of your business decisions depends on data, the first line of action would be to ensure data hygiene. But what happens when the data quality is suffering? Even while training your AI and ML models, you will need a wide variety of structured and unstructured data containing inputs like historical claims, transactions, personal documents, GPS data, rich media, investigative reports, etc. Furthermore, all this information needs to be organized, labeled, and contained in their respective training datasets. Throughout this process, you would require expert data handlers who can retain and maintain data fidelity without affecting the quality. Even at later stages, while handling data, you will need to ensure that your data is insulated from dilution. As such, your team will have to undergo regular training for handling and maintaining data quality. Disparate Data and Data Silos The insurance sector heavily depends on customer data generated at various touchpoints. Whether it is the information collected by a lead capturing form or rich media associated with FNOL - customers are continuously required to share data with insurers. Hence, a major concern of using artificial intelligence in insurance revolves around making this data universally available. artificial intelligence in insurance Since these inputs are available at different stages, the AI system can only function correctly when your organization breaks through data silos and disparate data storage frameworks. To ensure optimum performance of AI for insurance companies, the data must be centrally located with an active data validation and updating system to keep it consistent and uniform. Technology and Vendor Selection As AI continues to grow sharply through the insurance sector, several vendors have entered the market to woo this crowd. Several vendors are trying to harvest the hype and make big gains by pushing for big investments. Insurance companies have yet to understand the nitty-gritty of the technology involved but are afraid of missing out on the next high-tech wave do not wish to waste any more time. This pressing FOMO pushes them to accept recommendations at face value from domain “experts.” However, a lot goes into becoming an AI expert for insurance companies and claiming to be one. Any vendor worth their salt will first carry out an assessment to evaluate the scope for introducing AI in various processes. Next, they will audit the existing process to carry out a cost-benefit assessment to measure the impact of AI or machine learning in insurance processes. Finally, they will formulate a detailed roadmap of how the transformation will take place. Additionally, the vendor will aid you in determining the technologies, offer training and onboarding, and reliable post-sales support. first carry out an assessment to evaluate the scope cost-benefit assessment a detailed roadmap Organizational Support Finally, when it comes to introducing new technology within a company, organizational support plays a crucial role in determining the success of its adoption and implementation. Smooth AI integration requires the active involvement of company leaders and the C-suite to inspire an institutional change. These industry leaders will also have to ensure that infrastructural, training, upskilling, and other forms of support are made readily available to the workforce to eliminate any form of hesitance one may have towards this change. The leaders will have to convince and inspire others that the use of AI for insurance companies is not an imposed tool but a facilitator to enhance productivity. The top-down trickle approach will build and sustain momentum while accepting change. Conclusion Quite often, businesses tend to overlook the challenges that come with artificial intelligence or machine learning in insurance and focus merely on the opportunities presented by the technology. However, having an understanding of the potential obstacles can be an excellent way to prepare oneself holistically. As one can see from above, artificial intelligence in insurance may present a few challenges but none of them are insurmountable. Eventually, it is all about persistently working towards the goal of embracing artificial intelligence and you will reap several benefits in the long run.

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4 Challenges Of Implementing AI In The Insurance Industry and How To Overcome Them

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