Machine Learning in Insurance – The power behind the data

Machine Learning in Insurance – The power behind the data

Insurance companies are only processing 10-15% of their available data, failing to unlock crucial insights. Potential data can highlight risk appetite, premium leakage, fraud mitigation, expense management, and more. 

Machine learning is a branch of artificial intelligence (AI) focused on solving problems without explicit instruction through analyzing and learning similar problems. According to recent insurance technology literature, these are four areas in the P&C insurance market that are using machine learning to improve profitability: 


We’ve all heard the sayingthere are no bad insurance risks, just bad pricing. This saying has never been more accurate in our current climate. Even so, how we presently calculate risks are becoming antiquated. Relying on credit scores, past claims, and postal codes does not paint an accurate risk picture. 

Machine learning can take the data we are already collecting and that is readily available via social media, telematics, rates, policies, demographics, and spit out a rate based on a myriad of real-time data points. Machine learning, by far, supersedes the traditional actuarial methods of deriving insurance rates based on demographics, postal code, credit, and historical data, to name a few. 

Trufla is currently beta testing their rate predictor module built upon the foundation of machine learning. So far, the program can predict rates with 92% accuracy using only four underwriting questions.  

Claims Management  

Many insurers are using machine learning to improve and streamline claims processes. Machine learning is already being applied to claims automation, as we see with Lemonade, effectively speeding up claims servicing and settlement, thereby reducing operating costs. Other applications currently under development include using machine learning to analyze the cost of claims, such as repairs and other services, significantly reducing the amount insurers will spend on claims.  

For example, an AI claims chatbot could interact with clients, reduce the number of interactions and interventions, calculate damage, and calculate repair costs while assessing the likelihood of fraud.  

Automation and Insurance Advice 

Using an AI chatbot for claims management has been found to reduce costs considerably, as we have shown above. However, numerous AI applications in automation could be used in every aspect of the insurance cycle. 

Machine learning can predict what insurance products your clients may need next, the best time to connect with them and can predict the probability of cancellation. By using behavioural data, machine learning can predict future behaviour with unprecedented accuracy. The famous film, Minority Report may not just be a piece of fiction. Food for thought! 

AI chatbots will interact with insurance shoppers or customers on a similar level as a licensed broker or agent. They will be able to give advice and learn the needs of their clients to anticipate and answer questions. Chatbots will allow brokerages to scale their business and increase the number of policies a broker can manage, leading to more profitability. 

Trufla is currently working on an AI tool that will allow brokerages to “X-ray” their book of business to determine who is likely to cancel their policy upon renewal. The Broker X-ray also conducts premium and coverage analysis to determine whether a client is over or underinsured. The data alone will enable brokers to proactively connect with clients to increase retention and upsell products, year over year. 

Fraud Prevention 

Canada’s insurance industry suggests that between 5 and 15% of the premium drivers pay for car insurance goes toward covering undetected fraudulent claims.  In the USA, the FBI estimates that 40 billion goes into insurance fraud payments every year. Fraud drives up premiums for everyone. AI may be the solution to prevent fraud, lower loss ratios, and stabilize insurance rates across the board.  

AI algorithms can analyze a massive amount of data in a brief period to identify anomalies that do not fit subscribed patterns, such as unusually high repair or settlement costs. AI can also use satellite or drone imagery to learn and make decisions about a claim’s estimated cost vs the actual payout.  

Where do we go from here? 

There are many ways in which the insurance industry can tap into the plethora of data readily available to us. From insurance data, social data, IoT data, and more, the industry can build machine learning algorithms to improve all aspects of how we do insurance today. 

Technology advancegrow exponentially, which means that in a few short years, how we interact with, and manage insurance may look completely different than what we see today. From rating to risk management to process improvement, we have barely scratched the surface of what we can do with so much data.