The insurance industry is heavily reliant on predictions of risks based on characteristics of potential customers. Although the use of said models is common, researchers have long pointed out that such practices perpetuate discrimination based on sensitive features such as gender or race. Given that such discrimination can often be attributed to historical data biases, an elimination or at least mitigation is desirable. With the shift from more traditional models to machine-learning based predictions, calls for greater mitigation have grown anew, as simply excluding sensitive variables in the pricing process can be shown to be ineffective. In this article, we first investigate why predictions are a necessity within the industry and why correcting biases is not as straightforward as simply identifying a sensitive variable. We then propose to ease the biases through the use of Wasserstein barycenters instead of simple scaling. To demonstrate the effects and effectiveness of the approach we employ it on real data and discuss its implications.

With the advent of technology, it has become easy for insurance companies to carry out insurance renewal and other processes. Over the years, artificial intelligence has developed to a great extent, making it possible for companies to carry out tech driven operations in an easy way, including the insurance industry. With the impact of AI, application of machine learning, data modeling, the entire insurance process has been smooth, thereby increasing customer satisfaction to a great level. AI has played a major role in the motor insurance industry, making it easy for companies to carry out car inspection processes, thereby automating purchase, claims and renewal processes to a great extent. The Motor Vehicles Act, 1988 makes it mandatory for a vehicle owner to drive with a valid car insurance policy.

The Insurance industry has been dealing with vast volumes of data for years, but analytics, Artificial Intelligence (AI) and Machine Learning (ML) techniques are increasingly being used to help insurance providers make faster data driven decisions. Given the exponential level of data available today with AI/ML, insurance providers can now efficiently extract new insights into their customer's needs and create stronger long-term value. Starting with how the market calculates premiums, the insurance sector now has access to thousands of data points to help them calculate premiums. Machine learning algorithms expedite the identification of the most predictive attributes driving claims losses – the most recent data points being historical cancellation data and gaps in cover. This helps insurers become more competitive, match their risks to the most appropriate pricing strategies and write the risks that meet their underwriting appetite.

Smart IT systems are now calculating claims costs and attributing fault for accidents without any human involvement, speeding up the resolution of claims. Technology is set to transform motor insurance in the next five to 10 years, revolutionising both the claims process and repair. Artificial intelligence (AI) is enabling insurers to evaluate vehicle damage at the scene of a collision, without the need for a claims handler or loss adjustor. By analysing millions of photos of vehicle damage and cross-referencing them with actual repairs, programmers have been able to create algorithms that can assess the scale of the damage and create a full estimate including recommended repair, paint, parts costs and labour hours. The system can determine, for example, whether body panels can be repaired or need replacing, and in worse case scenarios it ensures that no total losses are sent to bodyshops.

Insurers are now utilizing artificial intelligence to curb fraud cases in their motor insurance claim processes. Deloitte's Insurance Outlook Report 2019/2020 East Africa showed that while motor private and medical business classes are the largest classes, they are also among the most loss-making businesses. The report urges insurers to explore other emerging business classes that have a potential for growth to diversify their business mix. With over 25% of Kenya's insurance industry income fraudulently claimed, Kenindia Assurance is one of the insures who have taken steps to curb the fraud. Kenindia Assurance, Deputy General Manager Joyce Mathenge says that motor insurance is the main contributor to insurance fraud and hence the need to develop mechanisms to lower their risks.

An insurance claim is "a formal request to an insurance company for coverage or compensation for a covered loss or policy event" (source: www.investopedia.com). Once initiated, the claim often goes through a complex process with one of two possible outcomes -- the claim is either accepted, leading to a settlement, or rejected. The claims process would typically be: contact the insurance company, start of the claimant investigation, check the policy coverage, evaluate the damage and arrange compensation payment. UK insurance industry figures are staggering. On average, £33m are paid per day in motor claims, £13m in property claims, £12.5m for policy protections, and £1m for travel claims; the average bodily injury claim is close to £10k; more than 98% of motor claims have been accepted, and the yearly cost of fraudulent claims is £1.3bn.

You are in a driverless private hire vehicle that's ferrying you to work. As the vehicle drives itself, you take a nap in the backseat. Suddenly, you are awakened by a loud thud and the sound of glass shattering. You come to your senses, and realise that the car has hit a pedestrian who is now lying motionless. All this happened while you were asleep, without any human input on how the vehicle manoeuvred or behaved.