How AI Can Change The Insurance Industry For The “New Normal” Adoption
No longer used solely by innovative technology companies, AI is now of strategic importance to more risk-averse sectors such as healthcare, retail banking, and even insurance. This article explores a few ways in which artificial intelligence is changing the insurance industry for the “new normal” adoption after this COVID-19 pandemic.
How might AI change insurance?
Artificial intelligence (AI) is an increasingly pervasive aspect of modern life, thanks to its role in a wide variety of applications. The technological advancement and applicability of AI systems have exploded due to, cheaper data storage costs, increased computing resources, and an ever-growing output of — and demand for — consumer data. As such, we expect to see a change in several critical aspects of the insurance industry.
- Matching customers to products. AI can be used to process a wider range of data to produce better outcomes in terms of underwriting. By using data more traditionally captured and analyzed during underwriting in conjunction with data collected from new sources, for example, social media and wearables, AI can better assess an individual’s risk than traditional risk scoring methods. For example, such use of AI may reveal that an individual led a healthier life than their age and occupation might suggest. This would potentially make them eligible for a wider range of or more favorable insurance policies that better suited that individual than in the past. Also, the application of AI in the online advertising space has meant that insurers can make smarter decisions about whom to target for their products, and in what manner.
- Streamlining customer interactions. By making customer interactions more seamless and the conversations more accessible, the use of AI could encourage greater take up of insurance products. Insurers regularly use AI-assisted chatbots with natural language processing and generation to answer customer queries and offer quotes, and the chatbot market is expected to reach USD$1.25 billion by 2025.
- More accurate pricing. Insurance relies heavily on algorithmic approaches to help people prepare for unforeseen events. In particular, sophisticated statistical models are used to determine how best to allocate risk, and understand how this risk balances against premiums and pay-outs. These methods have not changed much over the last few centuries: but thanks to AI, advanced data-driven tools can make rapid and insightful inferences by identifying new patterns in historical data. When combined with a real-time collection of data through sensors, insurers may have the ability to create hyper-personalized risk scores. This would allow premiums to be based on actual behavior (for example, exercise habits), instead of relying on just the risk profile of certain categories such as age and gender.
- “Nudging” the insured’s behaviors. Insurers can utilize AI learnings to better advise customers on how to avoid risks. Known as “nudging”, this is a form of choice architecture that presents (or incentivizes) certain options, without forbidding any alternatives. Although most nudges currently rely on conventional data analytics rather than AI per se, there is clear potential for AI nudges for policyholders in due course. It is easy to imagine an AI-empowered nudge to drivers to follow low-risk travel routes, which is then rewarded with lower premiums or other incentives for following the advice.
- Fighting fraud and improving claims management. The identification of fraudulent behavior can help insurers improve claim management, which may lead to fairer outcomes. Artificial intelligence can be used for finding out the attempts to deceive by submitting fraudulent claims. By processing and analyzing huge amounts of disparate information, AI can also reveal the connections between multiple factors. After the AI is trained enough, it can detect fraud with high accuracy, basing on the previously analyzed cases. An AI program could also be developed to provide a clear description of why it regards a given claim to be fraudulent, and then transmit it for further investigation to human specialists, and thus improving claims management.
Of course, it is important to note that insurance is a large and complex industry. Even in light of the perceived advantages discussed above, insurers may not always find it easy to integrate AI within products or backend systems. A survey revealed that as of 2018, only 2 percent of insurers worldwide have seen the full-scale implementation of AI within their business, with a further 34% still in “ideation” stages. Furthermore, there are important ethical considerations that have yet to be addressed, with critics warning that AI could lead to detrimental outcomes, especially about personal data privacy and hyper-personalized risk assessments. While more work needs to be done to understand the various implications of AI in insurance, it nevertheless remains an important and fascinating space to watch.
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