More than $1 trillion in net insurance premiums are written in the United States annually¹, making the U.S. the world’s largest single-country insurance market. China, the third largest insurance market², continues to expand while ongoing economic growth in emerging economies solidifies the insurance industry’s position as a vital component of the global economy.

Despite the industry’s seemingly positive outlook, insurance has its own share of challenges. Insurance fraud costs the insurance industry more than $80 billion annually³. In an effort to overcome fraud, waste, and abuse, many companies are turning to data analytics.

Reducing risk and improving customer service with data

The insurance industry doesn’t lack data. A single claim, for example, could have dozens of demographic or firmographic data points to analyze and interpret. 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.

By contrast, cloud-built data warehouses make it possible to ingest, integrate, and analyze limitless amounts of data, freeing up resources to automate these important business processes:

Risk and Pricing Analysis: Life insurance companies leverage data analytics to provide customers an expedited application and quoting workflow. What formerly required a multi-step risk scoring process for the company and a health screening for the customer, is now done almost instantaneously through the secure analysis of an applicant’s health records.

Fraud Detection: Property insurers use data analytics to detect and mitigate fraudulent claims. Anticipating fraud without a predictive analytics platform is a rigorous, time-intensive process that depends on complex spreadsheets and number crunching. With machine learning powered by historical fraudulent claim data, insurers can “train” their algorithms to proactively monitor and identify high risk claimants, which reduces manual effort and increases the effectiveness of fraud detection processes.

Provider Abuse Prevention: Medicare and Medicaid account for approximately 37 percent of all healthcare spending in the United States, according to the Centers for Medicare & Medicaid Services. This equates to more than one trillion dollars of government subsidized hospital care, physician and clinical services, prescription drugs, and other professional services, leaving the door wide open for wasteful and abusive billing by providers and health systems. To combat this type of abuse and fraud, program administrators, private companies contracted by Medicare and Medicaid to administer such programs, are increasingly reliant on data analytics to identify outliers and thwart unethical billing.

Reimbursement Analysis: Pharmacy chains rely on data analytics to ensure they’re being adequately reimbursed by health insurers. Dispensing specialty drugs for rare conditions has a sizable upfront cost for the pharmacy, making proper reimbursement all the more important. Manually tracking reimbursement for each disbursement, however, is essentially impossible. Data analytics platforms allow pharmacies to efficiently analyze millions of transactions and records to identify delinquencies, thereby holding insurers accountable.

Litigation Propensity Scoring: Insurers of all types understand the true cost of defending disputed claims. Legal fees can quickly exceed the original cost of a disputed claim. Data analytics is transforming how litigation propensity scores are developed, making it easier for insurers to identify claimants who are likely to settle outside of court.

How a cloud-built data warehouse accelerates insurance DATA analytics

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

Instant Elasticity: Integrating new data sources can enhance an organization’s decision-making but doing so also increases the company’s data storage requirements. Scaling storage in the cloud happens quickly by simply clicking a button, which expedites time to value for stakeholders.

Concurrency: Separating storage from compute enables contention-free querying and makes data democratization a reality. Storage and compute layers can scale and change independently, delivering optimal performance for a fraction of the cost and effort.

Advanced Analytics Connectability: Just-in-time connectivity to machine learning technologies (i.e. Hadoop, DataBricks, Cloudera, etc.) with Snowflake Spark Connectors for fast, automatable, round-trip linkage to advanced AI and fraud detection capabilities.

Security and Compliance: Cloud-built data warehouses can provide greater security and compliance than on-premises systems. Snowflake, for example, is HIPAA compliant, PCI DSS certified, FedRamp Ready, and maintains security compliance and attestations including SOC 2, Type II.

Per-second Pricing: Pay-as-you-go pricing eliminates upfront infrastructure costs and aligns expenditures with actual data use. Coupled with up-and-down, instant elasticity, you get as much power as you want but can dial down compute resources automatically or on the fly.

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 Analytics

As insurance evolves into a more data-driven industry, the demand for modern, cloud-built solutions will continue to expand. If your organization’s on-premises solution is unable to keep pace, perhaps it’s time to take a tour of Snowflake. Snowflake’s zero-management infrastructure and multi-cluster shared architecture simplifies data management, freeing up additional capacity for analytics.

Start your 30-day free trial and receive $400 of credits to try all of Snowflake’s features.


  1. “Financial Services Spotlight”. Retrieved December 6, 2018. 2. “Annual Report on the Insurance Industry” (PDF, September 2017). Retrieved December 6, 2018. 3. Coalition Against Fraud. “Insurance Fraud, Why Care?” (PDF). Retrieved December 6, 2018. 4. National Health Expenditures 2016 Highlights” (PDF). Retrieved December 6, 2018.

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