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Advances in digital tools—particularly data analytics and related technologies, including mobile and social, sensors, and automation tools—enable insurance companies to create operational efficiencies, customize products, and enhance customer experience. This allows them to increase profitability and add potential new revenue streams from the sale of data to outside parties. But they face an uphill battle in leveraging technology to survive competition.
For established insurers, successfully competing with digitally born firms requires moving to an agile operating model, embracing new tools with end-point integration in mind. They must also embed data-driven decisions into each business unit. In doing so, companies must ask themselves: Have we enhanced our digital capabilities end to end, adding to existing architecture in a smart and business-driven manner? Are we driving a good process, or are we just adding complexity without tangible benefit?
A new company can hit the ground running with a flexible IT system that incorporates all data points and combine it with artificial intelligence-driven decision-making. Conversely, established companies struggle with a patchwork of legacy systems, data quality issues, and fragile integrations.
Sensors and wearables—ranging from facial recognition for medical testing to car-use apps (e.g., Progressive Snapshot, Allstate Drivewise) to evaluate driver risk—aid insurers in multiple areas, such as risk assessment, pricing, and customer service. However, sensors and wearables add another layer of complexity for established companies who already face multiple issues integrating technology and data.
Legacy insurers, then, face the challenge of ensuring that underwriting systems “speak with” these new data collection systems. Front-line customer data gathering is the easy part. The challenge is effectively flowing the data across multiple systems to make it usable, and embedding the right processes and skills to take full advantage of the digitization.
So, how can established companies achieve the flexibility of a startup while maintaining the benefits of a market leader? To overcome the obstacles outlined above, the most successful companies:
Start with the greatest areas of value: Identify your biggest opportunity areas, develop pilot or Skunk Works projects around them, and then scale and cascade them throughout the organization. Agile companies “fail fast” and embrace concurrent pilot initiatives. Once these prove successful, companies must develop a long-term strategy to manage the implementation and enhance long-term capability to sustain results.
Build agile operating models: For successful implementation, companies must reshape their operating model to continuously maximize advances in data and technology. To increase agility, insurers should rethink organizational structures to make them more product-focused, redefine roles, and improve links between business and technology.
Product teams should include roles across the entire value chain (e.g., product owners, developers, testers, etc.). This holistic structure breaks functional barriers/silos, improves interactions, and accelerates time to market.
Apply new tools to connect systems and automate information flow: Companies need not discard old data and technology. Using new robotic process automation and data transformation tools, they can link old and new systems in the near term. The key is avoiding patchwork add-ons that may compromise enterprise architecture flexibility and cement suboptimal processes as standard and replicated. Wherever possible, processes should be first optimized and engineered to make use of new digital data, analytics tools, and automation technology.
Over the longer-term, companies should design system architecture such that legacy core applications have defined interaction layers that integrate with any platform, including the cloud. This includes maintaining an agile integration team (with “interaction layer” experts) that supports each product but does not delay product releases or technology advancements with core application changes or needs.
Embed the use of data analytics in the organization: Business leaders must understand how to turn data into useful insights. Success does not require hiring an army of data scientists. Many insurers have created better and faster leverage by upskilling business analysts who run day-to-day operations.
Placing data analysts as close to business leaders as possible also provides significant advantage, particularly in a product-centric organization. This empowers product teams to mine data, analyze results, and develop meaningful insights for their own product without requiring involvement of another part of the organization.
New tools require new behaviors, and change is hard for companies, regardless of size and tenure. Firms need comprehensive engagement and communication plans to align teams and drive adoptions. When employees understand how new data and automation tools can empower them to solve problems and create new capacity, they become more apt to embrace them. Companies can successfully get executives in the habit of using real-time data to drive decisions by embedding data scientists into leadership meetings.