Data Monetization Insurance

Monetizing Your Data in the Insurance Industry

“One way to future-proof a business in the insurance sector is to lean on data monetization software.”

As the insurance industry changes alongside a number of social and technological trends, many companies are looking for ways to improve their bottom lines using data analytics tools. One way to future-proof a business in the insurance sector is to lean on data monetization software. You may be wondering, though, what exactly data monetization is and how you can put it to work.

The What of Monetizing Data

Data analysis can be performed using a number of resources that most insurance providers already have access to. The industry is demanding in terms of the amount of data that is taken in from customers themselves and from incident reports. This offers a lot of opportunities to go into a data lake and derive insights that may help a firm operate more efficiently, reduce risks and properly priced products. You may be interested in conducting:

  • Fraud detection
  • Loss prevention
  • Predictive modeling of macro-scale risks
  • Analysis of customer relationships

One major advantage that most insurers have over companies in other sectors is they tend to own huge repositories of historical data. When working with analytics, it’s impossible to over-emphasize just how much value comes from feeding more information into any data monetization software system.

The Why

If your company is curious about the potential impact of new safety features on automobiles, for example, you can make comparisons to historical precedents. This can include looking at moments like the advent of seat belts and airbags, to get a sense of what the risk profile of your average customer will look like in 5, 10 or 20 years. While this sort of modeling isn’t considered purely predictive, it provides a starting point for understanding changes that are hard to plan for.

Fraud detection is also a major opportunity for monetizing data. In the modern business environment, many people engaged in fraud are working together either directly or sharing information across the internet. This means that new kinds of fraud can appear seemingly out of nowhere. Likewise, individuals engaged in fraud may move around. By looking for patterns in how they purchase insurance and file claims, it’s possible to identify both buying and filing behaviors as they’re just appearing.

The How

Acquiring the staff and building out the infrastructure required to perform meaningful data analysis requires a significant shift in a company’s attitude toward computing. Data science places an emphasis on testing various hypotheses, and that means you’ll need team members who have strong backgrounds in statistics in order to assess the relevance of output from your data analytics tools. This goes beyond the basic actuarial work that’s done in the insurance world and extends into other disciplines, including computer programming, economics, pattern recognition, social sciences and even psychology.

All this work is underpinned by significant amounts of computing resources. In particular, companies need a lot of data storage capacity to provide robust enough databases for analysis work. This entails installing servers, setting up redundancies and providing reliable networks for both machines and users to communicate across. In some cases, a high-speed network may call for completely re-cabling buildings to ensure the infrastructure is robust enough.

Culture Change

Establishing a culture that values data and analysis is also critical, and it demands more than just bringing in stats geeks, IT people and computer programmers. From the bottom to the very top of your organization, stakeholders need to be on-boarded with the culture change. This includes training sessions where decision-makers are taught about data dashboards and what their contents actually allow them to do.

Likewise, training needs to include education about the power and limitations of data. The insurance industry has many privacy issues that have to be broached. There also needs to be an understanding that excessive reliance on computer-driven answers can create its own set of problems.

One downside to this approach is that some people are going to resist change. New assignments and severance packages need to be available to ensure that folks who can’t follow the company into this new era aren’t left in positions where they can impede progress. Hiring processes should also be altered to ensure that new employees show up ready to be part of a data-centric business culture.

The culture change toward data analysis is a long one that calls for commitment. It takes time to bring in skilled professionals to set up systems and make choices about what processes need to be used. Similarly, stakeholders need to be patient in order to allow the benefits of monetization of data to begin to flow into the company. As the culture shifts and processes are refined, though, you’ll begin to see a discernible uptick in profits. 

Artificial Intelligence Data Analytics Insurance

How to Accelerate AI in Insurance Data Analytics

Mastering techniques around Insurance data analytics, knowing what data to get, and how to analyze it, greatly streamlines many of the most expensive insurance business processes.

“The United States is the world’s largest single-country insurance market. It writes more than $1 trillion in net insurance premiums every year. In emerging markets, China continues to be the growth engine.

All together, the global insurance market writes over $5 trillion in net insurance premiums per year.”  

Insurance Journal

Despite its size and global reach, the insurance business model has always been about two things.

  1. Maximizing the premiums received
  2. Minimizing the risk of your portfolio

Beneath these two top goals are a myriad of activities every insurance company has to master, including:

  • Reducing risk
  • Reducing fraud
  • Keeping customers happy with great service
  • Finding new customers with favorable risk profiles

Insurance fraud alone costs the insurance industry more than $80 billion per year. In an effort to overcome fraud, waste, and abuse, many companies are turning to insurance data analytics.

The staggering level of criminality costs us all, adding $400 to $700 a year to premiums we pay for our homes, cars, and healthcare, the feds say. There are simply not enough investigators to put a significant dent in the criminality, so the industry is turning to the machines.

Reducing Risk & Improving Customer Service

The insurance industry definitely has plenty of data. A single claim could have dozens of demographic or firmographic data points to analyze and interpret. A single policy could have dozens of individual attributes depending on what is being insured. Data enrichment, which has become more and more popular, can increase these data points into the thousands.


However, as insurance companies succeed and grow, datasets become increasingly large and complex. Often these are locked inside massive policy and claims management systems which do a great job of storing and maintaining the data. These do a great job for looking up individual policy records and claims, and of course, handle billing and renewals quite well.

When multiplied across an organization’s entire book of business, data sets become so large that legacy, on-premises systems are unable to keep pace with data volume, variety and velocity.

But what else could insurance companies be doing with All. That. Data?

We know when data is looked at in aggregate, surprising and valuable insights begin to show themselves.

By contrast, cloud data warehouses working in concert with Data Analytics Software make it possible to ingest, integrate, and analyze limitless amounts of data, freeing up resources to automate these important business processes:

Mastering these techniques around insurance data analytics, knowing what data to get, and how to analyze it, greatly streamlines many of the most expensive insurance business processes.

Customer Quoting, Risk and Pricing Analysis: Life insurance companies harness analytics to provide customers an expedited application and quoting workflow.

Writing Life insurance used to require multi-step risk scoring and an in-person health screening for the customer with a physician. Now it’s done almost instantaneously through the secure analysis of an applicant’s digital health records.

Fraud Detection: Property insurers use data analytics to detect and mitigate fraudulent claims. Predict fraud events from available data before it happens with a predictive analytics platform. Using Machine learning powered historical fraudulent claim data to model your risk in real-time. Look for highly predictive factors that correlate to. In this scenario, past performance is indicative of future results.

Detecting High-Risk claimants: Other algorithms can proactively monitor your portfolio and identify high risk claimants on a recurring basis, over time. After all, most claimant risk is only assessed once – when the policy is first written. However we know, financial circumstances change, properties age, vehicles require repair. Pulling together all obtainable data – policyholder financial and employment status, vehicle repair log, etc. tells companies what’s happening right now and what is likely to will happen next. This reduces manual effort and increases the effectiveness of fraud detection processes.

In about one third of cases, claims can be approved and paid out essentially instantly on approval by the company’s algorithms, he says. Even if a human is involved, it’s radically quicker. It becomes just a quick check to confirm the algorithm’s recommendation, instead of a deep analysis.

Source: Fast Company 

Provider Abuse Prevention:  Medicare and Medicaid make up approximately 37 percent of all healthcare spending in the United States. (according to the Centers for Medicare & Medicaid Services.) This adds up to over $1 trillion of government-subsidized hospital, physician and clinical care, drugs, and lab tests.

At these levels, the potential for waste and sometimes abusive billing by providers and health systems is always present. Program administrators and companies contracted by Medicare and Medicaid increasingly rely on insurance data analytics to combat this. This lets them identify patterns and outliers to thwart unethical billing.

Real-time Lead Scoring: New customers are the lifeblood of insurance growth. And never before have consumers and business customers had so many options and choices for insurance.

Predictive lead scoring sifts through inbound channels and optimizes leads by value and priority. Insurance Lead Scoring tools help select the best prospects with the most favorable risk profiles. Predictive lead scoring also tells insurers and brokers the best ways and times to contact prospects.

Behavioral analysis can predict whether a prospect is just shopping around or truly ready to buy. It also identifies the best method of contact for those prospects based on demographic profiling. Some prospects will appreciate a prompt phone call. Some prefer to come to a branch office. A fast growing group prefers typing over talking and responds better to a digital exchange (text messages, web and mobile apps.) Meeting the needs of these diverse audiences is the key to acquiring the best new prospects. This type of advanced profiling lets insurers predict the best methods and timing for prospect communications, and increases close and policy writing rates.


How Big Data Analytics Software in a Cloud Data Warehouse Accelerates Insurance Analytics

Unlike on-premises systems that don’t easily scale, a complete analytics platform featuring a cloud data warehouse, such as Inzata data analytics software, enables organizations to keep pace with the growing demand for insurance data by delivering:

Rapid time-to-value: Realize the power of real-time analytics to supercharge your business agility and responsiveness. Answer complex questions in seconds; ingest and enrich diverse data sources at cloud-speed. Turn virtually any raw, unrefined data into actionable information and beautiful data visualization faster than ever before, all on a single platform.

Rapid ingest of new data sources with AI:

  • Got a hot new leads file?
  • Just found out a new way to tell which vehicles will have the lowest claims this year?

Instantly add and integrate new sources to your dataset with Inzata’s powerful AI data integration. Integrating new data sources and synthesizing new columns and values on the fly can enhance an organization’s decision-making but doing so also increases the company’s data storage requirements.

The Power of Real-Time Performance: Your insights and queries are more most valuable if they get to you in time. In a competitive market where leads convert or abandon in seconds, having the speediest insights makes a huge difference. Inzata’s real-time capabilities and support for connecting to streaming data sources for analysis means you always have the most up-to-the minute information.

Make data even more valuable with Data Enrichment (One-Click-EnrichmentsTM):  Enrich and improve the value and accuracy of your data with dozens of free data enrichment datasets – all within a single, secure platform.

Inzata offers more than 40 enrichments include: Geo-spatial, Advanced Consumer and Place Demographics, Political Data overlays, Weather data, and Healthcare Diagnosis Codes. Plus more than 200 API connectors to bring in data from web and cloud sources.

Security and Compliance: Cloud data warehouses can provide greater security and compliance than on-premises systems. Inzata is available with HIPAA compliance and PCI DSS certification and maintains security compliance and attestations including SOC 2, Type 1 and 2.

Real-time Data Sharing:Secure and governed, account-to-account data sharing in real time reduces unnecessary data exports while delivering data for analysis and risk scoring.

Harness the Power of Insurance Data Analytics

As insurance evolves into an even more data-driven industry, business processes that used to take hours and days are going to be compressed down to seconds. Companies who properly anticipate these changes will reap the benefits in the form of more customers, higher profits and greater market share.

Inzata is an ideal platform for insurers to take the step toward real-time, AI powere analytics that will shape the industry for decades to come.

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