The insurance CIO’s role is undoubtedly evolving. While CIOs have been laser-focused on cost and efficiency, the new decade has ushered in fresh priorities.
Incumbent carriers have been slow to the digital transformation party, but that’s changing fast. Today, over 85% of insurance technology leaders expect an increase in customer demand for digitally delivered products & services.[1] And for insurance, this is a welcome development.
Traditionally, insurance carriers haven’t had many opportunities for customer engagement outside of policy purchase and claims processing touchpoints. The result is that customer ownership often falls into the lap of insurance agents and brokers, who can access a number of products from different carriers and fill the role of advisors to potential customers. Unfortunately, this situation has resulted in insurance carriers being relegated to record-keepers instead of playing an active role in the customer journey.
At the same time, 30% of CIOs identified increasing data complexity and competitive pressure from Insurtech as their primary technology challenges.[2] Insurtech’s ability to capture market share primarily lies in their use of technology as a CX value-multiplier, and their product innovation agility. In fact, 2020 saw Insurtech startups attract more than $7 billion in financing.[3]
For a CIO, the situation is worrying. How do they leverage technology as a tool to drive customer-centricity while carrying the baggage of aging mainframes, managing costs, and walking the tightrope of margin compression?
Based on our extensive experience with insurance carrier modernization, we’ve identified several key technology challenges that insurance CIOs will be focused on in the coming year.
Data Analytics Will Drive Dynamic Pricing Models
Customer data’s ubiquitous nature is changing how insurers price their premiums. Instead of using a cost-oriented pricing model that is based on statistical sampling and historical data, tech-savvy insurers are leveraging dynamic, real-time data points. For example, auto insurers are using telematics to estimate the quality of a given consumer’s driving and using that data to determine damage risk and the cost of premiums. In the US, for instance, All State has released a pay-as-you-go model that rewards car owners with lower premiums for safer driving behaviour like sticking to speed limits and avoiding hard braking maneuvers.[4]
The same concept is being extended to health insurance, with medical records and lifestyle-based data (from social media and health/fitness ecosystems) being tapped in real-time to personalize premiums to a specific customer.[5] The benefits for insurance CIOs are unquestionable – they gain access to the necessary data to optimize their customer experience further while lowering their risk exposure across the board.
The API Ecosystem Is Here to Stay
Using APIs is one of the fastest ways to accelerate internal systems integration and overall digitization, allowing the different business units within a carrier to share data seamlessly. And when combined with the ability to integrate third-party application data into your core, APIs offer insurers a real-time, 7200 view of the customer – critical to agile product personalization capabilities. CX has also been revolutionized by API development, and insurance products can now be embedded into critical touchpoints on consumer-facing third-party platforms like real estate, healthcare, and finance apps.[6]
These are particularly important developments for CIOs since they open up the ecosystem play, multiplying the number of digital channels available and unifying the customer experience across each one.
Mainframe Migration & Core Modernization Cannot Be Delayed
In the digital age, moving to the cloud and having ‘anytime, anywhere’ data accessibility is essential to the high-performance enterprise. Given that digital transformation has been a buzzword for over a decade now, it may surprise you to learn that 90% of the world’s biggest insurance companies still use legacy mainframes at their core.[7] While mainframes are great at accurately processing large volumes of data with low failure rates, they aren’t the most agile solutions and have limited and time-consuming integration protocols with newer technologies.
Additionally, the talent base for mainframe maintenance and code creation is shrinking by the year, driving up the cost of keeping these legacy systems in place. When we helped a Fortune 100 insurer migrate 1.5 million lines of code to the cloud, they realized a 35% cost savings while improving speed-to-market by 30%. This illustrates just how important legacy migration will be for CIOs under pressure to deliver tangible results in short timeframes.
Competitive Advantage Via Quality Assurance & Engineering
If insurers want the capabilities to release new products and services at the pace of digital, their CIOs will have to relook at their QA/QE processes. Automation is a core component of any modern QE program, allowing testing, defect triage, and product cycles to operate faster and with massively reduced resource consumption.
For insurers looking to build a digital ecosystem, a sound QE program can mean the difference between never-ending technical debt and a flawless, first-time-right feature release.
Another benefit of a modern QE program is skyrocketing efficiency. Many insurers still work with manual underwriting processes that are repetitive and time intensive. Automation, deployed in tandem with AI and ML algorithms, can accelerate underwriting, claims processing, and turnaround time for new products. For example, when working with a major insurance carrier to automate over 90% of their mission-critical processes, we observed that an automated QE program led to a 2x improvement in testing speed. As a result, the carrier acquired a significant competitive advantage via an accelerated go-to-market cycle.
AI & ML Technology Will Be Table-Stakes
While all of the above are essential to redefining the insurance value chain, perhaps none are more critical than AI and ML technologies. Today, AI has a massive impact on every facet of insurance, from underwriting and pricing to litigation propensity prediction and claim processing. At SLK, we’ve personally seen AI-driven automation slash claims handling and turnaround time by as much as 10x while delivering 70% cost savings on straight-through processing requests.
But that’s not the end of the AI story.
By 2025, experts predict that there will be over 1 trillion connected devices.[8] And as open-source, industry-agnostic data sharing protocols become mainstream, the levels of unstructured and structured information available to carriers will explode. In the process, deep learning AI technologies will become table-stakes in leveraging and deriving insights from these massive data volumes. The ability to predict which customers are litigious and which need a competitively priced premium will be relegated to AI instead of manually processed by actuaries.
The Next Big Leap for Insurance
Many CIOs within the industry are aware of tech trends that they must address. But in a world where disruption is happening at an ever-increasing pace, it can be hard to get a handle on which technologies to adopt. What is clear, however, is that data challenges are a top priority. Cloud migration and process automation can help stem the tide to a certain degree. Still, over the next decade, CIOs who expect their carriers to stay competitive will have to embrace the many facets of AI while adopting an ecosystem-based outlook on customer acquisition.
Leading insurers who manage to hit the right notes between CX, AI, data management, and process optimization will likely retain their market position. However, for smaller and regional carriers, emerging and existing technologies offer a quick route to increased market share and improved customer relationships.
Authored by Jagadish Kundu
