From client profiling, acquisition, and service to risk assessment and costing, fraud detection and right-touch claims management, capital deployment, and exceptional event planning, data, and analytic solutions are enhancing the insurance industry’s ecosystem. Although some organizations use data and analytics more extensively than others, practically every company in the industry, from life insurers to personal lines writers to commercial P&C companies, uses data and analytics to better their insurance business performance.
The four cornerstones of risk transformation are strategy, governance, and culture, business and operating models, and data, analytics, and technology. The latter lays the groundwork for a new approach to risk management. This revolutionary approach necessitates the leadership of senior management delegates, particularly the chief information officer (CIO) and chief risk officer (CRO), and has major financial ramifications.
Enhancing the quality, accessibility, and aggregation of data
For an organization to meet regulatory and risk-management standards, it must consider the issue of data ownership. Some insurance institutions have appointed a chief data officer (CDO) to oversee data managerial functions and collaborate with the chief risk officer (CRO) to satisfy risk needs, the chief compliance officer to meet regulator needs, and the chief technology officer to connect with. Identifying where data should be stored and how to apply risk data standards are two other important concerns.
Putting Analytics to Work
Although data from independent bodies are frequently used in risk identification, measurement, monitoring, evaluation, and reporting, selecting and using this data as well as verifying its quality and accuracy can be difficult. Statistics about growing strategic and reputational risks, for instance, can be found in both highly unstructured and structured formats, such as blog postings, social media, and online news stories, as well as in organized formats, such as economic reports, financials, and public disclosures. Fortunately, the cost of tools for evaluating and integrating enormous data has decreased, and advances such as risk-sensing capabilities focused on parsing large data are still being developed.
Institutions’ risk transformation needs are quite obvious, and technology providers have been attempting to address them, with mixed results. Databases have traditionally been designed for storage and, later, integration. Techniques that aggregate data and views to support particular analytics and reporting, such as stress testing, are displacing the storage-and-integration method. This eliminates networks and allows recurrent operations; instead, operations are brought to a central destination, and reporting is done rather than as calculation and reporting on the back-end.
Nevertheless, forward-thinking senior executives are considering risk conversion as a strategy to limit expenses, the lower total cost of ownership of technology, and realize operational efficiencies, all while enhancing risk assessment and capital allocation. In the long, such initiatives may aid in improving competitiveness and increasing shareholder value.