Figure 1: New and changing risks faced by insurers. Source: Wipro Insights Analysis
To tackle these rapidly evolving risks, insurers require a comprehensive and dynamic analysis of the economy, market, and several other risk-generating parameters. This dynamic analysis substitutes static assumptions with data-driven real-time knowledge, and generates actionable insights for insurers to be leveraged in mitigating risk.
Data is the cornerstone strategic asset in conducting this dynamic analysis. It is imperative that insurers harness their data to remain customer-centric, drive new products, and achieve competitive advantage. However, it can be difficult for established insurers to transform into data-centric data-driven organizations. The primary challenge is collecting relevant data in real time, and storing it in an enriched, structured format. Legacy systems, aging infrastructure, and in-house talent constraints further exacerbate the challenges faced by insurers. Furthermore, securing the investment requirements (long-term ROI perspective needed) and managing a rising regulatory stringency are factors that the organization needs to do well.
Adopting a holistic data framework: To address data-related challenges
A holistic approach is required to implement end-to-end data transformation and gain agility, efficiency, and automation across the insurance value chain. This can be achieved by deploying/modernizing the 4 stages of the data framework: Sourcing, Structuring, Storage, and Synthesizing (4S – Framework).
Sourcing: Insurers will drive greater value and insight if they can leverage data from both internal and new external sources. For instance, they can source data generated in real time by connected IoT devices – such as smartwatches, vehicle telematics, and industrial IoT sensors – and drones and satellites. Moreover, insurers also have large volumes of internal data, generated over time but buried in departmental silos, which could be further leveraged through operational integration across verticals.
Combining internal data with that from new sources is a strategy that can act as a force multiplier in delivering a remarkable competitive edge.
Structuring: The large volumes of data collected across internal and new sources come in different formats and require structuring to be rendered usable. It is vital for insurers to have effective structuring tools and platforms in place, as this will determine the quality of data, thus directly impacting the quality of insights and outcomes.
Storing: Storing data on cloud servers and integrating it with other insurance operations is key for effective data transformation. While the cloud had become widely leveraged by insurers to store and retrieve data, only a limited number of insurers have integrated data from across functions. Moreover, many have not fully integrated the cloud with their operations and have not fully leveraged the full potential of cloud for data transformation.
Synthesizing: The synthesizing of the data is where the value is derived and data processing technologies, such as advance analytics, AI/ML, cloud computing, and blockchain, to effectively analyze data to derive actionable insights.
A holistic 4S – framework approach toward data can drive end-to-end transformation of insurers: