The insurance business was conservative quite a long time; however, the appropriation of new advances isn't only a cutting edge pattern, but a need to keep up the competition. In our computerized era, Big Data innovations help to process various data, improve work process productivity, and reduce operational expenses. Learn more about the advantages of Big Data for insurance from this material.

How Big Data is Revolutionizing Business

Current society is constantly delivering noteworthy measures of ongoing data. Processed by AI, it turns into an important data source which is crucial for most business models, as well as insurance.

Big Data is used for:

  • New distribution models. Virtual helpers, robotic guides, and chatbots enhance client actions and make promoting more targeted;
  • New suggestions. It empowers making elective plans of action, for example, peer-to-peer concepts or digital insurers.
  • Process automation. It substitutes difficult work and improves the proficiency of the inside work process;
The Role of Big Data in the Insurance Industry

Anna Maria D'Hulster, Secretary General at The Geneva Association, recommends, "Going forward, access to data and the ability to derive new risk-related insights from it will be a key factor for competitiveness in the insurance industry. New approaches to encourage prudent behavior can be envisaged through Big Data, thus new technologies allow the role of insurance to evolve from pure risk protection towards risk prediction and prevention."

Big Data and Insurance: Implications for Innovation and Competition

Insurance is based on data investigation: accident statistics, policyholder's personal information, as well as third-party sources help to divide people into different risk categories, prevent fraud losses, and optimize expenses. The quick development towards the Digital Society opens new sources of data that can be used to make a complex behavioral pattern for every specific client and correctly decide their risk category.

There are two new data sources:

  • Online behavior - this incorporates actions in the internet;
  • Sensor data - from gadgets in the Internet of Things, for example, drones, Smart homes, vehicles.

Such personal data can complement the conventional sources in insurance, creating constant experiences about an individual's way of life and habits that can be used for competitive advantage.

Big Data solutions for insurance examples.
Client Acquisition

Each individual creates huge amounts of data by social networks, emails, and feedback, which gives significantly more exact data about their preferences than any poll or overview. Investigating such unstructured data, insurance organizations can expand their effectiveness by making focused on promoting campaigns that will get new clients.

Client Retention

Based on activity, algorithms can distinguish early indications of clients' disappointment so you can rapidly respond and improve your administrations. Using gathered insights, insurers can focus on solving client's issues, offer discounts, and even change the valuing model to build the dedication of every specific client.

Risk Assessment

Insurers were constantly centered around the check of clients' data while evaluating the dangers, and Big Data innovations can expand the productivity of this procedure. Prior to an ultimate conclusion, an insurance organization can use predictive modeling to estimate potential issues dependent on the customer's data and definitely decide their risk category.

Fraud Prevention and Detection

As indicated by Coalition Against Insurance Fraud, every year US insurance organizations lose more than $80 billion because of fraud, and this outcomes in expanded premiums for each partner. Using predictive modeling, insurers can think about an individual's data against past fake profiles and recognize cases that require more examination.

Cost Reductions

Big Data innovation can robotize numerous manual procedures, making them progressively effective and reducing the costs spent on dealing with cases and administration. In a competitive environment, this will result in lower premiums, which will pull in new customers.

Customized Services and Pricing

The examination of unstructured data can offer services that will meet the client's needs. For instance, life insurance based on Big Data can become more customized by considering the client's medicinal history as well as habits detected by activity trackers. It can be also used for determining pricing models that will both guarantee benefit for organizations and fit clients' financial limits.

Consequences for Internal Processes

The execution of big data algorithms can improve the productivity of most procedures that require a ton of examination. Technology can help insurers rapidly check the policyholder's history, automate cases processing, and deliver better services to clients. According to McKinsley, automation can spare 43% of the time for insurance representatives, so they can concentrate on money-generating assignments.

How is the Market Evolving by Segments?

The insurance business has just begun profiting from Big Data; notwithstanding, the circumstance is marginally extraordinary for every specific circle.

Big Data in Health and Life Insurance

Including new data sources, the industry can grow new insurance models that won't just be more targeted but will also encourage consumers to improve their lifestyle by offering discounts for higher activity. John Hancock has officially declared changing to intuitive arrangements dependent on data created by wellness trackers and health apps.
In any case, the implication of Big Data in health insurance causes concerns identified with data security, privacy, and ethics. This field still expects legislation to ensure that penalizing unhealthy behavior doesn't harm those who really need protection.

Big Data in P&C Insurance

The situation is more promising for property and casualty insurance, as Big Data can help to detect empirical links between customer behavior and risks. For example, car insurance companies can grade roads based on the reported accidents and check their clients' tracks. With Big Data, car insurance can get a highly personalized customer profile based on drivers' GPS location data and use it to make the final decision. As GPS data is encrypted, such a process doesn't breach clients' privacy.

Big Data in Travel Insurance

Contrasted with different sections, travel insurance receives big data and AI advances especially well. The moderately low strategy value makes travel insurance a fairly quick decision, so this industry manages an amazing number of requests. Technologies can accelerate the interaction with clients, give progressively customized items and services, robotize straightforward correspondence, improve consumer loyalty, and rapidly arrange the most useful offer.

What Will the Market Size Be in the Next 3 Years?

The adoption of Big Data is constantly increasing, and insurance organizations are expected to invest in these technologies up to $3.6 billion by 2021, according to SNS Telecom&IT. Big Data implementation results in 30% better access to insurance services, 40–70% cost savings, and 60% higher fraud detection rates, which is beneficial for both insurers and stakeholders. The combination of Big Data and insurance will facilitate the adoption of on-demand models and new under insured risks, for example, cybercrime.

Predictive Modeling and Big Data Are Insurance Industry Powerhouses

The continuous analysis of consumer data makes it possible to understand client's behavior and gather real-time insights for both established insurance enterprises and InsurTech startups. Using Big Data analytics, insurance can offer personalized policies, precisely assess risks, prevent fraudulent activities, and increase the efficiency of internal processes.