Use of Advanced Analytics in Insurance

The insurance industry has always been a high-risk sector. Piloting through difficult claims procedures, pricing, promotion, underwriting, cash repression, ensuring compliance are some of the problems that pose the industry.

Moreover, for a long time, insurance sectors have been dependent on statistics, data, and legacy systems to drive their decisions, as there is an excess of data gets generated in this industry on a regular basis. Hence the incorporation of advanced analytics in the insurance sector has proved to be a boon to achieve the business goals.

In the insurance sector escalation of data is on a rise presenting multiple opportunities for insurers to develop actionable insights on potential markets, customers, risk, natural disasters, and competitors. Hence, it has become necessary for insurers to infuse advanced analytics capabilities into their DNA to operate more efficiently.

So how advanced analytics has proven to be a valuable asset for insurers?

Advanced analytics helps to mine through big data for actionable insights that can be further used for a plethora of business use cases. Insurers are increasingly adopting advanced analytics to protect their businesses from risks and identify new growth opportunities by using customer information. It also helps insurers to calculate the right insurance amount for individuals and digitize the underwriting & claims management process.

Let’s have a look at the use cases of advanced analytics in the insurance sector.

  1. Detection of false claims: According to Gartner, insurance companies incur huge losses every year due to false claims estimated to be $40 billion per annum. Boost in data science technologies has made it possible to detect fabricated claims incorporating predictive analytics. It provides statistical models for efficient fraud detection. The model uses historical data on false activities to reach specific conditions that predict the possibility of claims being fake.

  2. Detecting and minimizing risk in real-time- Insurance's underlying nature involves risk and advanced analytics helps to conduct a real-time risk analysis that enables organisation to be quick on their feet in an unpredictable risk eb=environment.

  3. Channelizing customer behavior- Insurance companies use advanced analytics to analyze data and influence customer behavior. For example- health insurance companies can acquire data from wearable technologies like fitness trackers and analyze it to track variables that determine the health of a person and assess risk.

  4. Value Prediction: Customer Lifetime Value (CLV) is predicted using customer behavior data to determine the customer’s profitability for the company. Behavior-based predictive models are used to process all the data on customers and arrive at a forecast on customer buying and retention. These models provide insights on the likelihood of customers’ behavior in the maintenance or surrendering of a policy. CLV can also be leveraged for developing market strategies as it reflects one of the important customer characteristics.

  5. Claim Predictions: Advance analytics drives complex processes involved in building financial models that have a large number of variables affecting the outcome.

  6. Algorithms are developed to recognize relationships between vast numbers of variables and detect several important parameters that are essential to building a customer portfolio.

  7. Innovating underwriting: High variability in decision processes and long wait times — result in application drop-outs. To overcome this, a leading insurer built an AI-enabled learning model and initiated testing using historical claims experience & underwriting decisions. This enabled a significant reduction in underwriting cost per application along with faster turnaround time and increased sales through better pricing.

    In a nutshell, the scope of advanced analytics to yield business benefits is excessively spanning across the insurance value chain- sales, product & pricing, underwriting, claims management, customer data protection, and customer experience.

    Artivatic incorporates emerging technologies like Artificial intelligence, Machine Learning, Image Recognition, API Integration, and advanced analytics to identify new growth opportunities for insurance industries.

    Artivatic is transforming legacy insurance into digital, personalized, and customer-centric while considering the affordability of our clients. We leverage AI and Machine Learning in our daily life and aim to integrate the same to uncomplicate extreme archaic and manual industry to increase the number of insurance policyholders in the long run.

    The platform pioneers in the new age digital innovations in insurance (Life & Health) with 400+ modular API infrastructure and Integrated core products like:

    INFRD: Integration of Cloud APIs Platform with 400+APIs to provide a holistic solution for all insurance & healthcare services.

    ALFRED: One integrated platform for all your claims needs. Provide end-to-end claims automation and assessment platform.

    ASPIRE: An end-to-end personalized & affordable solution for employee health & business insurance.

    AUSIS: A customized AI-built & 3rd party data-driven smart, real-time & personalized Underwriting Platform for Insurance.

    MiO: AI embedded sales, communication & marketing platform for complete insurance & financial services providers.

    ProdX Design: A holistic Next-Gen rapid product designing with risk insights.

    PRODX DISTRIBUTION: A customized B2B2C distribution and embedded insurance platform for businesses.

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