Introduction
In recent years, artificial intelligence (AI) has been making waves in the insurance industry. In 2019, the global market size for AI in insurance was valued at $1.5 billion and is projected to reach $11.96 billion by 2026, growing at a CAGR (Compound Annual Growth Rate) of 34.2% from 2020 to 2026, according to a report by Allied Market Research. Why not, as found out in McKinsey & Company’s study, 86% of insurance executives believed that AI in insurance would lead to better decision-making and process automation.
However, with these benefits come risks that must be addressed. In this blog, we will explore the 5 types of risks associated with AI in insurance and how they can be mitigated.
Risk 1: Problem of Bias
One of the biggest risks associated with AI is bias, and AI in insurance isn’t immune to this problem. AI is only as unbiased as the data it is trained on. GIGO, the “Garbage In, Garbage Out,” is a common phrase in the field of computer science, which means if the data contains biases, the AI will perpetuate these biases. This can lead to discrimination against certain groups, which can result in legal and financial implications for insurance companies.
To reduce bias, it is important for insurers to use diverse and representative data when training AI algorithms. This data should represent all types of people across the demographic strata. It’s also important to have diversity within the development team responsible for creating AI for insurance. By having a diverse team, different perspectives can be considered, and potential biases can be identified and addressed before deployment.
Risk 2: Security threat
The implementation of AI in insurance opens up new avenues for cyberattacks. AI systems handle sensitive customer information, such as medical records, financial data, and personal details. This makes them attractive targets for hackers who look to exploit vulnerabilities and gain access to valuable information. According to a report, the average cost of a data breach for companies in the United States is $8.19 million.
To address security risks, insurers must implement 360-degree robust cybersecurity measures, from securing network architecture, limiting data access, encrypting the data, to regularly testing for vulnerabilities, and updating vulnerabilities with patches. It’s important to note that cybersecurity is an ongoing process that requires regular updates and maintenance.
Risk 3: Privacy concern
AI technology goes hand in hand with data collection, which can be problematic when it comes to privacy. A study by the National Association of Insurance Commissioners (NAIC) found that over 70% of insurance companies collect data using social media. Customers may feel uncomfortable knowing that their data is being collected and analyzed without their knowledge or consent. This can lead to a loss of trust and damage the company’s reputation.
To overcome privacy risks, insurers must implement strong data management practices and comply with relevant privacy regulations. Such as establishing transparent policies around data collection and uses. This involves obtaining informed consent from customers and also providing the right to access, rectify, and erase customers’ data, among other rights.
Risk 4: Reliability Issue
AI systems are only as reliable as the data they are trained on. It is also a proven fact that AI algorithms can produce unintended consequences that may not be immediately apparent. For example, an insurance algorithm may incentivize providers to focus on economic treatments rather than the most effective ones. This can lead to costly errors and reputational damage for insurance companies.
According to a study, only 36% of consumers trust AI-driven decisions. To overcome such reliability risks, insurers must ensure that the data fed in AI for insurance companies is accurate, comprehensive, and transparent for their customers to make their decisions more explainable and transparent. This includes regularly checking the quality of the data and updating the training models accordingly.
Risk 5: Lack of Fairness and Regulatory Compliance
Insurers must comply with various regulations when using AI for insurance operations. Failure to comply with these regulations can result in significant fines and reputational damage. Furthermore, a study by the Consumer Federation of America (CFA) found that some auto insurers use non-driving related factors such as occupation, education, and credit score to determine premiums, which could disproportionately affect low-income and minority communities.
To overcome this risk, insurers must stay up-to-date with regulatory requirements and ensure that their AI algorithms comply with these requirements. They must also regularly review and update their policies and procedures to reflect any changes in regulations.
AI in Insurance: The Conclusion
AI in insurance has the potential to revolutionize the insurance industry, but it is not without its risks. Implementing technologies of AI for insurance can be expensive, especially for small and mid-sized insurers. An independent survey report says 46% of insurers face significant challenges in funding initiatives related to AI for insurance and face challenges in hiring people with the necessary technical skills. However, by implementing robust strategies to address these risks, insurance companies can unlock the benefits that AI can offer.
Contact us today to know more about outsourcing your insurance back office work and learn more about the AI in the insurance industry. It’s essential to remember that the implementation of AI for insurance companies is an ongoing process that requires regular updates, maintenance, and testing. By addressing the risks associated with AI, insurance companies can leverage its benefits to make better decisions and automate processes.
