The insurance industry is one of the most competitive industries that is heavily reliant on vast amounts of raw paper data or documents. Data entry and document conversion services play a key role in converting paper documents into various digital formats, which helps streamline management and allows the information to be shared and analyzed. By clearly analyzing and identifying data, businesses can identify current trends and patterns and derive valuable insights for strategic decision making. Data Analytics – that involves using advanced techniques to analyze data for constructive use – is proving critical for revenue management. Insurance companies collect huge amounts of data from their customers and hand it over to their team of underwriters who help in evaluating the amount of risk involved. As part of the risk evaluation, underwriters use a wide range of techniques and use various forms of data (including actuarial data, claims data, policy data, etc). However, the major challenge is that this process is not scalable. The pivotal role of data analytics in insurance becomes evident at this point.
Significance of Data Analytics in the Insurance Industry
Data analytics has many applications in the insurance industry including optimizing customer acquisition, delivering personalized service, processing claims efficiently, intelligently underwriting policies, and detecting fraud more effectively. More and more insurance companies are upgrading their systems so that they can leverage the benefits of insurance data analytics in their business.
As data analytics has been changing the way the insurance industry operates, it is essential for insurance companies to become fully aware about the core benefits that they can leverage to improve their business processes and make use of the data in the best possible form. It is important to have a clear understanding about how data affects internal processes and about the impact of data analytics.
With real-time data analytics, insurance businesses can easily figure out –
- How profitable their business is
- Tweak sales practices to improve those profits
- Reduce wasted time with policyholders
- Maximize overall performance
- Increase per-agent and per customer profitability
Here discussed are some top ways in which data analytics is transforming the insurance industry –
- Providing Client-centric Services – Insurance is a highly client–centric industry. Clients are always looking out for a trusted and reliable consultant to meet their insurance needs. Insurance data analytics can help companies and brokers to provide customized services to their clients. Companies can leverage intelligent insurance management platforms and other technologies to gain key insights based on customer data to provide clients exactly those products and services they are looking for. For instance, intelligent management platforms feature smart dashboards that an agent can access to get a complete overview of each client’s portfolio. If one of the clients has a gap in coverage, the system will automatically generate an alert and offer an opportunity to bring added value to the client. This feature enables companies to contact clients only at a specific point when a particular data or information is missing. This will create a feeling among clients that the company is providing importance to them, which in turn results in more sales.
- Pricing Premiums Accurately – When insurance companies price policies and premiums, a common challenge that they face is to accurately price the premiums for each policyholder. In certain cases, the policyholder may face unfair or high premium amounts for no mistake of theirs. To be more competitive in the market, insurance companies have come up with new methodologies using insurance analytics. By deriving actionable insights from data analytics and tracking individual policyholders’ behavior, the prices of the premiums can be set accurately. For instance, an insurance company may use predictive modeling to forecast the probability of a policyholder being involved in a car accident. Insurers can gain actionable intelligence on an individual policyholder by monitoring their driving habits and behaviors and then comparing them against other policyholders in their database. For auto insurance, a small box can be installed inside vehicles or an app can be downloaded onto a Smartphone through which the insurer can then monitor driving habits over time. The data that is collected via this predictive technology can be used to re-price policy premiums so the policyholder pays a fair premium for coverage.
- Preventing/Reducing Fraud and Waste – Fraudulent claims are common in the insurance industry. There are certain policyholders who believe the insurer is out to cheat them somehow, so they decide to cheat the insurer first and “get theirs.” Data analytics can help reduce these attempts of fraud and waste to a considerable extent. Insurance companies can make use of this actionable data intelligence to figure out who may be a likely fraudster even before it happens. For instance, an agent can monitor data in real time from various social media platforms to see if a policyholder may be engaging in fraud–related activities. For example, if a policy-holder files a claim for a tree branch falling on his house in the middle of winter due to snow accumulation and he is posting some of the grand rooftop Christmas decorations and posting those images via social media platforms, then there is obviously a clear discrepancy.
- Self-servicing of Policies – Regarded as one of the major innovations in the insurance industry, self-servicing of policies is being increasingly used by organizations. Companies, (including brokerages) provide a customer portal for policyholders wherein they can manage their own policies. This will ensure that companies have ample amount of time to focus on other key issues of their business. Use of data analytics can help insurance companies to make smart recommendations to the client when they are buying a new policy or making changes or renewing existing policies. In addition, there is another hidden benefit of Big Data for brokerages and agents. Many life insurance businesses, for instance, want to shift from complicated and expensive medical underwriting. But, traditionally, this has been nearly impossible. With Data Analytics, and with customers managing their own policies, insurers can use a multitude of non-medical data points to eliminate the traditional medical underwriting process. This means underwriting can be more streamlined, leading to faster policy issue times.
- Claim Management – Data analytics help insurance companies to analyze the large amounts of data at the underwriting stage of the policy and detect any discrepancy. For instance, when a customer makes a claim, the insurer can easily detect whether the claim is legitimate or not. Data analytics can be leveraged for real-time monitoring through digital channels and social media and this in turn help in the digital transformation of traditional insurance companies.
- Restructuring the Insurance Industry – Data analytics will make the insurance industry even more competitive and companies will implement various strategies to be noticeable in the crowd. Offering low-cost products (when compared to competitors) and excellent customer service will be the topmost priority. In short, data analytics will enable insurance companies to transform their existing processes and easily meet the evolving regulatory requirements.
Over the years, data analytics in the insurance sector has evolved significantly and is helping insurance firms to take key business decisions. While more and more challenges appear to alarm the present market, insurers need to recognize the immense, hidden potential of data analytics in the insurance industry. To reap the full range of benefits, they need to devise an enterprise-level strategy to implement data analytics in such a way that it offers more than just customer experience. Effective data analytics depends on having clean data in the required format. Not only will it improve the overall customer experience, but can also provide valuable savings by reducing labor costs, identifying fraud, and managing risk. Outsourced data conversion services can help in clear and efficient organization of client data ready for analysis. Error-free data conversion services can help consolidate data, reduce the total expenses, enhance customer experience, and substantially reduce fraud.