The amount of data in the insurance industry is exploding, and the number of opportunities to leverage this data to achieve large-scale business value has exploded along with it. Rapid integration of technology makes it possible to use advanced business analytics in insurance to discover potential markets, risks, customers, and competitors, as well as plan for natural disasters.
Insurance companies have been turning to external data sources, such as social media, to add more information about a claimant or injured party. However, there are limits to this.
With data analytics driving virtually every aspect of the insurance business today – from premium pricing and customer experience to claims management and fraud prevention – insurers need the right analytical tools and methodologies to access relevant data to make smart decisions.
This is where insurance data analytics come in.
Insurance data analytics is the process of collecting, analyzing, and extracting relevant insights from various data sources to manage risks for insurers effectively. The primary goal is to reduce costs, optimize customer relationship processes, and use predictive analytics in insurance models to create reliable reports.
Here are the key reasons why insurers should use data analytics:
Preventing insurance fraud is one of the biggest drivers of using insurance data analytics. Sophisticated data analytics tools help insurers identify whether an applicant is likely to make a fraudulent claim by studying the applicant’s historical data or past behaviors.
The tools also identify past convictions for fraud and an applicant’s criminal records. Such predictive modeling methods – which utilize machine learning and artificial intelligence – enable agents to make decisions about issuing policies or rejecting claims.
Insurance data analytics enables detailed risk assessments. Through big data analytics, insurers can identify the amount of risk presented by each applicant before making a decision about issuing a policy by again by sourcing crime databases, but also investigating social media accounts.
Through data, insurers can identify behavior patterns and common characteristics to target their marketing efforts. Because insurers can anticipate trends before they happen by using predictive analytics, they can position themselves to outsmart the competition.
Data analytics optimizes the internal insurance-related processes. For example, analytical tools can be used to assess customer feedback and check customers’ responses to various policies, sales techniques, and promotions, enabling insurers to make informed decisions.
This also helps leadership identify skills gap areas that require training. Analytics helps agents in the field maximize their productivity.
Data research helps insurers offer customized policies that are aligned with prevailing trends at a much faster rate. Data algorithms can assist with customer retention by predicting which plans would benefit individual customers. And they give insurers a comprehensive view of each applicant, giving agents actionable insights based on the prospective client’s insurance history.
Subrogation – one of the biggest pain-points for insurers – lets an insurer pursue the person at fault to recover money paid out for a policyholder’s claim. Such claims are common in auto, health, and homeowner insurance.
Data analytics can trawl through the unstructured data of police records, medical records, and adjuster notes to identify subrogation opportunities, which saves insurers large sums of money by reducing loss expenses.
Predictive analytics can be an incredibly powerful and valuable tool if used effectively, helping you identify patterns, possible future scenarios, and actionable insights.
Our research below shows that digital champions (the 7% of companies that succeed with digital transformations) are way ahead in applying predictive analytics models to their data.
Copyright © 2021 Trianz
The cost of processing claims has a large impact on any insurer. In addition, the costs of fraud and litigation make claims more expensive. We provide end-to-end claims analytics solutions to accelerate claims processing and reduce the risk of fraud. Our offerings include data mining, data visualization, forecasting, and optimization.
Fraud costs organizations billions of dollars each year, and early detection is key. Our comprehensive fraud analytics solutions leverage text mining to tap both structured and unstructured data – such as claim notes, call-center notes, medical reports, police reports, e-mails, and web-based content – to highlight anomalous transactions.
We use predictive analytics to identify subrogation opportunities to help you increase your yield in the arbitration process. Our subrogation analytics solution leverages advanced text analytics and predictive modelling to discover data, patterns, business rules, and other related characteristics to drive successful subrogation recovery.
Our customer analytics solutions enable your sales and marketing teams to leverage the power of your own customer data for effective resource allocation. Our data models can help you gain valuable behavior insights, improve interactions, and identify your highest-value customers for cross-selling or upselling opportunities.
Thanks to cloud-based technology, you can conveniently access data insights through an intuitive web-based user interface. When you leverage the power of data analytics, you can focus on your core business, enhance your organizational efficiency, and improve day-to-day decision-making. Doing so ultimately impacts the bottom line.
Finally, with predictive analytics, you are better equipped to compete in the marketplace as a direct result of increased customer satisfaction.
What Is an SQL Query Engine? SQL query engine architecture was designed to allow users to query a variety of data sources within a single query. While early SQL-based query engines such as Apache Hive allowed analysts to cut through the clutter of analytical data, they found running SQL analytics on multi-petabyte data warehouses to be a time-intensive process that was difficult to visualize and hard to scale.Explore
A Winning Base for Successful Digital Transformations When it comes to developing a successful digital strategy, it is not just corporations planning to maximize the benefits of data assets and technology-focused initiatives. The Government of Western Australia recently unveiled four key priorities for digital reform in its new Digital Strategy for 2021-2025.Explore
Engage Your Workforce with a Modern Employee Intranet Solution The employee intranet has changed significantly since it was first introduced in the early 1990s. What started as HTML-based static portals have now evolved into intuitive communication tools complete with search engines, user profiles, blogs, event planners, and more. Today, many organizations are taking a second look at employee intranets to bridge gaps between teams, build company culture, centralize information, increase productivity, and improve workflow.Explore
Adopting emerging cloud technologies, consolidating resources, and improving processes is the key. “IT no longer just supports corporate operations as it traditionally has but is fully participating in business value delivery. Not only does this shift IT from a back-office role to the front of business, but it also changes the source of funding from an overhead expense that is maintained, monitored, and sometimes cut, to the thing that drives revenue,” said John-David Lovelock, research vice president at Gartner.Explore
Deliver Powerful Insights Instantaneously with Federated Queries - No Matter Where Your Data Resides The concept of federated queries isn’t new. Facebook PrestoDB popularized the idea of distributed structured query language (SQL) query engines in 2013. Over the years, AWS, Google, Microsoft, and many others in the industry have accelerated the adoption of a distributed query engine model within their products. For example, AWS developed Amazon Athena on top of the Presto code base, while Google’s BigQuery is based on Cloud SQL.Explore
What is Unstructured Data? Almost 80% of the data that enterprises and organizations collect is unstructured - data without a set record format or structure. Unstructured data includes data such as emails, web pages, PDFs, documents, customer feedback, in-app reviews, social media, video files, audio files, and images.Explore