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:
Figure 2: Holistic 4S Framework for Data. Source: Wipro Insights Analysis
The data 4S framework can create value and deliver efficiencies across the insurance value chain.
Figure 3: Value propositions of the Data: 4S Framework. Source: Wipro Insights Analysis
Data use cases: Depicting how data can create value and impart a competitive edge
Underwriting automation: Data (4S framework), through real-time risk assessment and straight-through engines, enables underwriting automation. Insurers can cost-efficiently underwrite simple risks through automated systems and manually underwrite complex high-value risk. This will not only enhance customer experience, but also result in operational cost efficiencies. For instance, one of the largest commercial insurers in the US leveraged data across multiple sources and applied advance analytics to process over 15–18% of its new business through automated systems, thus strengthening its position in the small commercial segment. Another US-based life insurer reduced underwriting expense up to 20% by adopting data-driven automated underwriting.
Loss prevention and fraud detection: Real-time data from IoT devices enables insurers to continuously monitor its customers’ risk exposure and adopt preventive modelling to notify them before occurrence of loss. Moreover, in case of loss occurrence, IoT data plays a significant role in conducting accurate loss assessment and quick claims settlements.
For instance, a midsized US-based insurer leveraged IoT data, which enabled it to avoid property losses due to frozen pipe leaks, reducing its loss ratio by over 4%. Similarly, another midsized insurer – offering policies to restaurants – used IoT sensor data to respond to electrical outages that could lead to refrigerated goods being spoiled, triggering preventive measures. A large personal lines insurer is leveraging data from social media to identify fraud. Meanwhile, several commercial insurers have already deployed drones and satellite imagery to estimate catastrophe losses and crop damage.
Profitability – Lowering risk exposure through positive behavioral nudge: One of the top 10 life insurers in the US is extensively using health-related data, collected from smartwatch wearables, to track customers’ health risks and then reward them for leading a healthy lifestyle. Several motor insurers have begun linking driving behavior with premiums, thus nudging customers to follow safe driving practices to earn discounts and rewards. These data-driven practices lower insurers’ claims risk and allows them to generate underwriting profits.
Conclusion: Transforming to a dynamic data-driven insurance organization is critical to be competitive
In 2022 and beyond, the global economy is anticipated to grow at an accelerated pace. Some of the change in market dynamics and consumer behavior that were triggered by the pandemic are expected to be permanent. Considering this backdrop, it is crucial for insurers to integrate a comprehensive data framework and strategy into their business models, making themselves future-ready to deal with new and changing risks.
Only those insurers harnessing the full potential of data will be able to tackle the emerging risks and gain a competitive edge over insurers that lag in data adoption. These laggards are certain to face sustainability challenges and gradually lose market share, not only to efficient data-driven incumbents but also to agile new entrants.
Supported by
Pradeep Agarwal (Senior Manager - Wipro Insights)
Chinmay Waikar (Assistant Manager – Wipro Insights)
Industry :
Suzanne J. Dann
Senior Vice President, North America Capital Markets and Insurance, Wipro Limited
Suzanne is responsible for managing growth and P&L throughout the insurance sector. Previously, she worked as General Manager of the Northeast region at Avanade, and held various leadership roles at IBM (VP of Sales for the Morgan Stanley Integrated Account Team, and Business Development Executive for IBM Research). She also served as Director of Technology for Fusient Media Ventures and spent the early part of her career at Ernst & Young Center for Technology Enablement, first as a consultant and later as Manager of Financial Services. Suzanne has a degree in engineering from Cornell University and is a certified information systems security professional (CISSP).
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