For insurance companies that generate huge amounts of data on a daily basis, big data is enabling them to mine information for insights in innovative ways. Through such analysis, organisations are able to achieve intuitive insights and make more accurate decisions. It is helping them streamline claims procedure, make it more transparent while proactively monitoring risks and creating value for end customers.
Here are top big data and analytics use cases in the insurance industry:
- Fraud detection: In the insurance industry, frauds are widespread. According to a report, insurance firms lose over $80 billion a year to fraud. However, the big data use case for fraud detection is extremely effective. By using data management and predictive models, big data can compare variables in every claim against past claims which were fraudulent. When there is a match, the claim is stalled and pinned for further investigation. Such analysis can highlight the behaviour of the person making the claim as well as that of partner agencies involved in the claim, allowing firms to successfully detect fraud and avoid costly deceptive claims.
- Predictive analytics: Big data can play a significant role in proactively predicting disasters, protecting lives and preventing costly claims. For example, in an age where climate change has become a big risk factor for insurance companies, big data can collect and analyse data from countless environment sensors and monitor climate on an ongoing basis. Instead of relying on historical data to predict a natural calamity such as a storm, insurance firms can leverage real-time geographical data to proactively envisage an impending catastrophe, determine risk areas, warn customers about the danger and avert large insurance claims – thereby saving hundreds of thousands of dollars, ensuring customer safety, and increasing overall customer loyalty.
- Real-time risk analysis: Since the insurance industry primarily works on the principle of risk, big data and analytics can enable organisations to conduct the real-time risk analysis, enabling them to be extremely responsive in an increasingly volatile risk environment. For example, connected cars can continuously send thousands of data points to servers every second; insurance firms can get minute details of the location of the cars to their speed as well as braking behaviour. Drivers can be sent high-resolution road conditions – from traffic to roadblocks to potholes – on a real-time basis and mishaps can be averted. Auto major Ford has partnered with IVOX to develop an app that provides a personalised score on driving behaviour, that helps lower insurance rates.
- Telematics: Big data can be used to determine policy premiums by health insurance companies. With the growth of IoT devices and wearable technology such as Fitbit and Apple watch, insurance companies can track customers’ health in order to predict and calculate risks. By monitoring behaviours and habits, companies can provide ongoing assessments of their activity levels and enable customers to take better care of their health. Insurance companies can then offer services and discounts based on the use of these devices and minimise claims.
- Customer behaviour analysis: Big data can also be used to acquire a comprehensive understanding of customer behaviour. By studying habits and needs, insurers can identify the right segments, anticipate future behaviour and offer relevant products. Firms can build unique customer profiles from information gathered from various data sources and get insight into various aspects such as when a customer is likely to leave, what kind of policy will best suit a particular customer and more. Such insight can help firms build trusted relationships with customers and engage with them in the right way with accurate information as well as help in upselling and cross-selling products. For example, when a customer intends to buy a home insurance policy, big data can provide firms with important information that can help them accurately calculate safety levels based on the neighbourhood in which the customer resides as well as his/her previous claims. Using this information, companies can calculate risks, as well as decide on the premium and cover.
Streamline Insurance Process
With insurance companies generating huge amounts of data, the challenge lies in processing the surge in data and making sense of it. With big data and analytics, insurance companies can expand their analytics practices beyond traditional ad-hoc reporting and make predictions about future trends. By continuously monitoring data and unearthing important insights, insurers can efficiently detect fraud, proactively predict disasters, warn customers about impending dangers, get insights into customer behaviour and suggest products based on their individual profile and preferences. It is only through big data and analytics that insurance firms can gain a comprehensive understanding of markets, customers, products, regulations, and competitors on an ongoing basis, streamline the insurance process through data-driven decisions and outdo the competition.