While data has always played a pivotal role in the insurance industry, today, insurers have access to more data than ever before. Insurers — like organisations in most industries — are often swamped by the massive volumes of data that are generated from all their sources like telematics, online and social media activity, voice analytics, connected sensors and wearable devices. These massive volumes of data need to be processed and analysed by machines for the invaluable insights that can be derived.
However, while most insurers are struggling to maximise the benefits of machine learning, there is a slow but steady change that is discernible across the insurance environment. This change is driven by heightened competition, flexible marketplaces, complex claims, more sophisticated fraudulent activity, ever higher customer expectations and increasingly tighter governmental regulations. Insurers have little option but to explore ways to use predictive modelling and machine learning to retain a competitive edge, give a fillip to business operations and increase customer satisfaction.
Insurers are keenly exploring ways that they can take advantage of the recent advances in artificial intelligence (AI) and machine learning to solve business challenges across the insurance value chain. Today, the sector is undergoing a profound digital transformation and looks forward to a phase of rapid growth by leveraging on technologies such as machine learning.
Here are 6 ways by which machine learning is changing the insurance industry:
- Automation and process improvement
As a part of their normal business, insurers process many thousands of claims and respond to many more customer queries. This is a perfect opportunity for machine learning to make a difference. Machine learning can easily improve the claims process and automatically progress claims through the entire claims procedure — right from the first report of a claim to analysis, contact with the customer if needed and claims payouts. Most claims can be processed in this way with only complex claims requiring human intervention. Insurance companies who have automated parts of their claims processing are seeing benefits in terms of significant savings in time and improved quality of service. And, both of these benefits to insurers also result in happier customers — a win-win situation all around!
- Tools and sophisticated rating algorithms
Rating is the bedrock of insurance companies and this calls to mind the famous saying in the insurance world: “There are no bad risks, only bad pricing.” This means companies should be able to accommodate almost all risks as long as they are able to accurately price them.
But, many insurers still rely on the age-old methods of evaluating risks. For example, when pricing property risks, they could consider only the historical data for a zip code in which the property is located. Individual customers could be assessed using indicators that are passé, such as credit score and loss history. Machine learning can fill this gap by offering agents new tools and techniques to classify risks and to calculate more accurate predictive pricing rates that will keep loss ratios low. For example, in auto insurance, rather than going by traditional determinants like age and gender, vehicle telematics brings together various aspects of a driver’s driving behaviour to arrive at a far more accurate assessment of risk.
- Improved underwriting capabilities
Another significant area where machine learning can bring benefits in the process of underwriting is in healthcare. Healthcare insurance covers costs incurred due to disease, accident, disability or death. Machine learning can provide immense benefits to this sector by its data-driven approach and analytics of the healthcare market. Machine learning-powered tools can consolidate and very effectively analyse information from immense volumes of highly varied data such as insurance claims data, membership and provider data, benefits and medical records and many others. These machine learning solutions can structure and process data to offer healthcare insurance businesses valuable business analytics that they can act on to provide reductions in costs, efficient fraud detection and a higher quality of care.
- Better customer lifetime value (CLV) predictions
The customer lifetime value (CLV) metric is a complex one. It depicts the value of a customer to a company as the difference between the revenue gained and expenses incurred. And this is projected into the insurer’s relationship with the customer — taking into account historical data as well as future predictions. This allows insurers to predict the potential profitability of a customer to the insurer and allows them to create marketing offers that are personalised to individual customers.
Behaviour-based machine learning models such as these can be very effectively applied to forecast the probability of retaining a customer or to predict the possibilities of cross-selling the customer other insurance products. Both of these aspects are critical factors in the company’s future income. Machine learning tools can also help insurers predict the likelihood of particular customer behaviours — like a continuation of policies by way of regular renewals or surrender/abandonment of their policies.
- Personalisation in product and service marketing
Thanks to machine learning algorithms that are mapped against data sets, customers can receive personalized services that match their specific needs, preferences, and lifestyles. Creating highly personalized insurance experiences using advanced analytics and machine learning is now an option companies have to improve their marketing ROI.
Machine learning analytics helps companies to understand and draw valuable insights from accumulated data with regard to individual preferences, behaviours, attitudes, lifestyle details, and hobbies to create products such as bespoke policies, loyalty programs, and recommendations that are personalised to each customer. Machine learning solutions give insurers insights and suggestions that fit specific customers through sophisticated selection and matching mechanisms.
- Improved accuracy in fraud detection and prevention
Insurance fraud is a very serious concern — one that costs the US insurance sector over $40 billion annually. By mitigating the loss caused by fraud alone, insurance companies positively impact their profit and loss statements. This is where machine learning algorithms help insurers. Many insurers are already using machine learning tools and algorithms to identify claims that are possibly fraudulent and refer them to further investigation by human employees. The speed at which machine learning tools and algorithms work, allow insurance companies to take action against fraud much faster and more accurately when compared to human analytical skills.
For example, an insurer that implemented an AI-powered solution to better spot potential fraud among insurance claims has achieved a 75% accuracy rate in fraud detection which is twice the market standard.
Neutrinos is helping leading insurers take impactful digital transformation decisions. With its vast technical expertise and experience in integration of new-age technologies like AI, ML, RPA and IoT, Neutrinos has implemented numerous insurance-specific solutions. We would love to set up a 30-minute conversation with you. Reach out to our experts today!
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