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The Need for AI in Insurance
Insurance is an old and highly regulated industry. Perhaps because of this, insurance companies have been slower to embrace technological change compared to other industries. Insurance is still steeped in manual, paper-based processes that are slow and require human intervention. Even today, customers are faced with time-consuming paperwork and bureaucracy when getting a claim reimbursed or signing up for a new insurance policy. Customers may also end up paying more for insurance because policies are not tailored for their unique needs. In an age when most of our daily activities are online, digitized and convenient, insurance is not always a happy customer experience.
- Sales and marketing: machine learning can be used to price insurance policies more competitively and relevantly and recommend useful products to customers. Insurers can price products based on individual needs and lifestyle so that customers only pay for the coverage they need. This increases the appeal of insurance to a wider range of customers, some of whom may then purchase insurance for the first time.
- Risk: Neural networks can be used to recognize fraud patterns and reduce fraudulent claims. According to the FBI, non-health insurance fraud in the US is estimated at over $40 billion per year, which can cost families between $400–700 per year in extra premiums. Machine learning can also be used to improve insurance companies’ risks and actuarial models, which can potentially lead to more profitable products.
- Operations: Chatbots using neural networks can be developed to understand and answer the bulk of customer queries over email, chat and phone calls. This can free up significant time and resources for insurers, which they can deploy towards more profitable activities.