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5 Impactful Ways to Apply Insurance Data Analytics

Today insurers are leveraging data analytics, artificial intelligence and machine learning to achieve better business outcomes.

The insurance industry has traditionally relied on historical data to assess and predict risks occurring in the future. However, digitization and digital transformation of entire business processes has resulted in data being collected at every touchpoint of the customer buying journey.

Today, insurers have an enormous volume of data, even real-time data that they combine with historical data to get business insights which they then apply to their products, services and processes. Their ultimate goal: to drive business value across the insurance value chain.

The numbers say it all.

90% of the world's data has been created in the past 2 years and according to research reports, the insurance data analytics market will grow with a 12.6% Compound Annual Growth Rate (CAGR) between 2020 and 2025.

The growth of data creation in the last years is exponential and business intelligence is the key to unlocking it.

To achieve better business outcomes, make more efficient business decisions faster and design innovative products and services, today insurers are leveraging data analytics, artificial intelligence and machine learning, since undouctedly data has become the center of focus for the insurance industry.

Here are five impactful ways in which data analytics is applied in insurance :

  1. Faster claims processing. Time efficiency is often the most critical factor in determining the overall outcome of claims.The need for automated data processing and faster transmission has led insurance companies to employ data processing services which process all important documents into digital formats. The overall process of claim settlements is simplified and processed faster thanks to data analytics that process huge datasets

  2. Fraud detection. Studies show that 1 in every 10 insurance claims turns out to be a fraudulent one. The need to early detect these fraudulent cases is undoubtedly of critical importance, since in this way insurance companies save huge costs. Big data analytics is a key tool in detecting these fraudulent claims through giving insight in past behavior, frequency of claims, and credit score. In this way, insurers know whether they need to seek additional information about the applicant or to deny a policy completely.

  3. Underwriting Automation. Saving time and resources involved in underwriting the process is another benefit of insurance data analytics. From data gathering to data validation and real-time recommendations, the “virtual underwriters” driven by data analytics provide faster and more accurate solutions. In addition, they help overcome challenges like manual data gathering from different types of application formats and channels.

  4. Risk Assessment. Identifying and assessing the risk of each applicant before a policy is issued to them is another powerful advantage of data analytics. By analyzing data from credit agencies and third-party vendors and combining them to social media interaction, a customer's risk profile is now measured more accurately and these insights may be applied to pricing.

  5. Targeted Marketing Strategies. According to a 2019 study, 90% of consumers say that they don’t engage with messages that are not personally relevant to them, while around 50% is willing to switch to brands that offer more personalized marketing communications. To attract and engage new customers today, insurers definitely need a targeted marketing approach to increase the effectiveness of their campaigns. Data analytics allows to analyze customer behavior so as to later send customized messages, promote suitable to the target audience products, and in the end improve customer conversion rates.

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