Machine Learning in Insurance – Ensuring the Future Growth

Artificial Intellignecne in Insurance and the risk

David Beckham, a famous soccer player, has insured his toes and legs for millions of dollars. A common man too, buys insurance for the things important to him such as house, car, life, pets etc. No wonder, at present, the global insurance industry is worth $5 Trillion. As compared to the rest of the world, the insurance industry in the US and Europe is more mature and focused on retail customers which includes small businesses and their consumers.

As the insurance industry continues to grow globally, it faces challenges related to fraud, high costs of sales, and pricing. Given the complexities of the insurance industry, advanced analytics including machine learning algorithms, offers solutions to the challenges. But the industry is cautious in adopting the technology. Business leaders are curious about use cases of machine learning in the insurance industry.

InsurTech is a rapidly growing segment

Some noteworthy applications of ML are:
Sales improvement: A recent survey indicated that 74% customers were happy with the insurance advice given by a bot. Additionally, ML algorithms identify patterns in the interaction between customer and sales agent that led to a successful sale or an unsuccessful conversation. These patterns are used in training human salespersons and bots for improving sales.
Risk modeling: Insurance companies are collecting customer data from various resources, which includes data points such as customer’s age, duration of the policy, products used, payment details, and household information. Upon analysis, these data can provide meaningful insights related to risk modeling. A proficient analyst is able to model for risks such as churn or claims.
Customer acquisition: Since the cost of acquiring a new customer is high, insurance companies approach their current customer base to promote a new or an existing insurance product. ML technology helps in identifying potential client within general customer base for a specific insurance product. For instance, if a current customer rents a home, they are a more likely to buy renter’s insurance than others.
Claim prediction: For insurance companies, ML algorithms that can predict future claims are powerful tools. For example, ML technology has the ability to predict that a driver will initiate an auto insurance claim in the near future based on their driving history, age, and type of vehicle. In health insurance sector, this ability drives various free wellness programs such as smoking cessation classes. ML tools are useful in claims triage i.e., conduct targeted investigations and fast settlement.
Fraud detection: In 2016, the insurance companies in the US lost approximately $50 billion due to fraudulent claims. For insurance companies, identification of fraud is a difficult task when left to humans alone. ML algorithms identify patterns or anomalies to detect fraud.
Churn prediction: Most insurance companies focus on customer retention and want to prevent attrition (or the churn) of customers. ML reveals key influencing factors that lead to customer churn. Then the operations team is able to take timely measures to prevent churn of customers.
Price optimization: Based on more accurate claims forecasting, ML tools assist in price optimization of the insurance policy. This helps insurance companies to keep their premiums low, provide higher benefits and thereby gain more customers.

7 Noteworthy Applications of Machine Learning in Insurance

ML tools have potential to analyze various formats of data. At present, ML tools are primarily applied to analyzing four main types of data:
Structured data: These data are collected in a well-organized manner, in standard documentation and have been analyzed using traditional methods for a long time. ML tools are useful in obtaining further insights by noticing patterns in these data. For instance, after an accident recording information about the type of loss, amount of loss, or physician ID of the care provider. ML tools have potential to detect if one business is reporting accidents repeatedly or at regular intervals.
Text: Text analysis allows us to get detailed information of the cases. When done by humans, text analysis takes several days to months. Notes, diaries, medical bills, accident reports, depositions, social data, invoices, etc. are easily and quickly analyzed using ML tools.
Spatial, graph: ML applications are able to analyze spatial or graphical data too. For instance, from data related to accident location, work location, the relationship of parties (doctors, customers, repair facilities), etc. ML tools are able to identify existing networks and also detect anomalies, if any.
Time series: ML applications also help in drawing inferences from the sequence of events, claim date, accident date, the duration of events/action, etc.

Machine Learning and Insurance Umbrella

InsurTech or the use of ML technologies for the insurance industry is a rapidly growing segment of FinTech. Insurance companies that seek competitive edge are developing data science teams and adopting ML technologies. Some insurance companies are collaborating with external data science companies for ML solutions. Irrespective of the option chosen, time to include ML algorithms in a company is now.

To further discuss, challenges faced by your company and possible ML solutions email us at [email protected]

InsurTech: Identify anomalies with ML tools

December 19, 2017 by Aditi Joshi

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