Strategically use both structured data and unstructured data to help provide greater accuracy in all types of analytics from customer, underwriting and risk assessment.
Big Data and advanced analytics are foundational for artificial intelligence for learning-based systems to help insurers achieve maximum value from their data initiatives and help them build more precise models that match market trends, adjusting through real-life learning methodologies.
Analytics strength and rigor must move beyond the actuarial department into other business units and functions such as claims, fraud and marketing using new sources of data and algorithms which can be operationalized in real-time and sourced from core systems. This will help optimize data throughout the enterprise, driving greater ROI where data has been underutilized in the past.
The immediacy of business decisions requires regular monitoring of data a sophisticated technical infrastructure to collect and tabulate information. The importance and complexity of these decisions means insurers insist on very high standards for data-analytics tools. The sensitive nature of insurance decisions and data furthermore creates major concerns about privacy.
Several data conventions in insurance hinder the widespread use of data analytics because data is split among different platforms and have different formats. Even well-structured data are often not available to insurers who could use them in useful ways.
The resistance to adoption of analytics is the requirement of learning new tools and the workflow disruption that results. For data analytics to truly transform insurance, data must be presented in tools that that are already in use.
Insurers want to manage their data analytics investments so that extra costs are not passed on to consumers. Working with data in an efficient, timely manner helps makes insurance more affordable in a competitive environment.
Get a single view of all of your data through intelligent data virtualization, providing dependable information and insights regardless of data location or format.
Autonomous engineering uses machine learning to optimize data queries to reduce time from hours to seconds and to make costs efficient and predictable.
Provide data access through a single common interface and allow your data users to use the BI tools of their choice to look at data across the organization
Optimize underwriting, pricing, and claims management and deploy analytics in a seconds and calculate what you need to be more operationally efficient and effective in the marketplace.
Drive growth, revenue, streamline business operations and serve your customers and partners in a more informed, personalized and faster way leveraging self-service analytics.
Apply autonomous systems to increase data reliability, speed, and security while reducing delays from manual data engineering.
Modernize data architecture, policies and procedures for improved securing for agents and insureds to protect your company from liability.
Simplify the process of bringing data together and providing high-speed insights in a complex, hybrid cloud environment.
A large insurance provider with 10s of millions of members and processing 100s of millions of claims a year assesses the efficacy of the healthcare services reimbursed, the insurance industry uses a key metric called “PMPM” or “Per Member Per Month” (it refers to the cost of service divided by the number of members within that month). This metric, albeit seemingly appearing fairly basic, is fairly hard to get right when insurance members fluctuate, when services cost vary and when the tools used to compute and analyze these numbers range from excel spreadsheets, to Tableau reports to custom-build applications.
AtScale provided the ability to define key metrics in one place, secure them centrally yet make them accessible everywhere to transcend beyond the limitations of their outdated infrastructure, reduce costs, and empower their analysts and data consumers.
Fortune 50 DIY retailer optimizes a cloud data platform to increase ROI per analysis.
Toyota leverages AtScale, accelerating time-to-insight from weeks to minutes.
Fortune 100 industrial conglomerate embraces the cloud without business disruption.
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