Insurance companies often face challenges with data silos and inconsistencies among their legacy systems. To address these issues, they need a centralized and integrated data platform that serves as a single source of truth, preferably with strong data governance capabilities.
To keep up with the rapidly growing Insurance industry and its increasing data and compute needs, it’s important to centralize data from multiple sources while maintaining high performance and concurrency.Â
With the growth of terabytes of data each month and the development of complex AI and ML use cases, the need for efficient data management is becoming more pressing.
This blog will examine how Insurance companies can effectively utilize the Snowflake Data Cloud to better manage data and take advantage of Snowflake’s innovative capabilities including data sharing, governance, and scalability.
What is the Significance of Snowflake in Insurance?
If your organization’s prevailing solution is struggling to keep up with the data needs of users, it may be time to take a look at Snowflake.
Snowflake is a cloud-based data warehousing platform with the ability to handle scale, compute, and storage independently. Its built-in security and governance features help insurance companies to govern and efficiently handle large varieties of data in massive volumes.Â
Snowflake empowers the insurance industry to efficiently store and analyze customer information, including claims, policy, and financial data. This enables insurers to gain insights and better understanding in the following areas:
- Customer behavior
- Fraud detection
- Risk assessment
- Underwriting and pricing improvements
What Are the Top Use Cases of Snowflake for Insurance Companies?
1. Data Governance for Insurance Companies in Snowflake
Dynamic data masking and object tagging are two significant features in Snowflake that complement a company’s data governance practice.Â
The PII of insurance clients is a prominent use case for data masking, protecting data at query time by hiding sensitive values without changing the underlying data. A common use case for data masking is obscuring PII exposed to unauthorized users, who would not require evaluating customers’ personal information to make business choices.
Another important use case is testing, where applications need data to evaluate the effectiveness of different systems and detect failures at a very early stage. Using sensitive plaintext data or original values is dangerous and greatly expands the scope of compliance (i.e., cost).Â
Because Snowflake’s masking policies are applied to cloned objects, data can be cloned from the production environment with the least risk, increasing the speed and agility of development and testing.Â
Masked data provides a cost-effective way to help test if a system or design will perform as expected in real-life scenarios.
As the insurance industry continues to generate a wider range and volume of data, it becomes more challenging to manage data classification. Snowflake’s object tagging feature addresses this by helping classify and define data governance policies based on the tags associated with the object and tracking the usage of sensitive information.
For example, you could use tags to identify objects that contain sensitive data and apply different access controls or data masking policies to those objects. You could also use tags to group objects by department, project, or application, making tracking usage and managing resources easier.
2. Reducing Risk with Snowflake
A typical insurance company requires analyzing data like customer demographic data, credit score, social network info, and behavioral data to determine the likelihood of a customer filing a claim. A traditional approach requires massive efforts and a long lead time in sourcing from various data providers, data pipelining, and integrating into data marts. Also today’s volume, variety, and velocity of data, only intensify the data-sharing issues.
With Snowflake’s data marketplace, this data can be sourced in just a few clicks from various data providers without any data-wrangling efforts. Snowflake’s data-sharing capabilities also enable organizations to share data internally across various departments and externally with a variety of vendors in a much more secure way, without creating any data silos or building new technology capabilities.
Such unique Snowflake capabilities enable an insurance company to gain access to a wide variety of data in a much faster time—all while unlocking deeper insights into their customers’ profiles, which gives them a clear advantage over the competition and reduces the underwriting risk significantly.
3. Fraud Detection in Snowflake
To proactively identify high-risk claimants, insurers rely on predictive analytics platforms, which involve complex and time-consuming number-crunching. By training algorithms on prior fraudulent claim data, insurers can detect fraud more effectively. However, legacy systems have scalability and performance limits, which can delay desired outcomes.
Snowflake offers a unique solution for predictive modeling with its architecture that can quickly and easily scale to meet on-demand computing needs. With the ability to process unstructured and semi-structured data, Snowflake can facilitate predictive analytics at speed, making it an ideal unified platform for machine learning and cross-functional teams to build scalable data solutions.
Organizations can instantly scale Snowflake’s capabilities, enabling them to explore massive data sets from various sources at the speed of thought and transform the data into actionable business insights. This quick and cost-effective process can result in making decisions up to 10 times faster. Users can also easily improve model performance by accessing shared data sets from their business ecosystem and third-party data through the Snowflake Data Marketplace.
4. Snowflake for Customer Acquisition
Insurance companies typically struggles to form solid client connections resulting in higher customer acquisition cost (CAC) and lower customer lifetime value (CLV). Also, consumers easily find the best deal at the lowest price. So, how can insurers persuade consumers to look beyond the price tag?Â
This challenge is compounded by rigid and inelastic legacy architecture, making it challenging for marketing teams to take advantage of time-sensitive opportunities.
Snowflakes Data Marketplace provides hundreds of free and subscription-based datasets from a wide range of partners, who provide customer demographic data. Vast varieties of datasets can be discovered and integrated effortlessly and securely while reducing data engineers’ operational efforts.Â
Also, Snowflake is cloud-native, which frees up organizations from ongoing maintenance obligations. The pay-per-use model and elastic near-infinite resources allow an organization to understand its customers better, without worrying about the IT infrastructure.Â
This enables marketers to deploy compute-intensive artificial intelligence (AI) and machine learning (ML) tools, which can swiftly examine enormous datasets for customer segmentation based on key variables, that would otherwise be hard to detect.
These unique features of Snowflake enable marketing teams to better understand their target audience better, devise effective strategies, create and distribute highly tailored messaging and extend unique offers to clients in a cost and time-effective way. Gaining better insights also creates new cross-selling and upsell opportunities.
Closing
As the insurance industry becomes increasingly data-driven, the demand for futuristic, cloud-based solutions will continue to grow. If your organization’s prevailing solution is unable to keep pace, perhaps it’s time to restrategize.Â
We hope this blog helps set your organization in the right direction. At phData, we understand that this effort is critical to your business, and we are here to assist you. We’ve built a set of tools, processes, reference architectures, and a team to help you better utilize your data and analytics with Snowflake.Â
Contact phData today for any questions, advice, and best practices today!
Frequently Asked Questions
The Snowflake Secure Data Sharing feature of Snowflake allows for data collaboration across environments, whether those be internal disparate data products or separate businesses. Best of all, it’s so easy to use. Simply put, data sharing allows you to share selected objects with another Snowflake account or with a reader account (more on that later). One of the primary benefits of data sharing is that data isn’t copied or transferred between accounts.
 Snowflake helps data scientists build churn models and provides them with a platform for deploying those models. The data used to train a model may come from a variety of different sources of customer information, such as Hubspot, Salesforce, etc. Snowflake can be used to centralize that data in a common location for model development.
