The Convergence of Core Systems, Data, and Analytics
I recently participated on a webinar panel hosted by Insurance Networking News. The webinar was about a new Strategy Meets Action (SMA) white paper, “Core Systems, Analytics, and the Data Explosion – Empowering Modern Core Systems with Data and Analytics”. Guidewire was a sponsor of this white paper and webinar.
With all the intense media coverage of the 2016 U.S. federal, state and local election – incidentally held on the same day – I realized that elections are not a spectator sport; everyone plays. Likewise, the participation for this webinar comprised of SMA, Guidewire, and all of our key competitors. The topic was how insurers can discover the greatest benefits when their core and data systems are fully integrated with analytics operationalized throughout the core systems. While you can listen to the webinar recording, here is Guidewire’s perspective.
The advantage of embedding analytics within core processes
There is currently a procedural, operational, and strategic information gap that insurers face. Claims managers wonder, “How do I obtain more efficiency in my claims handling?”; the CEO might ask “How will my business look in three years with these decisions?”; claims adjusters would like to know, “I’ve estimated four of these in the past; give me the average to start.”; the CMO might ask “Based on my current experience, which markets should we target?”; product managers wonder “What new coverages can I offer to help differentiate my products?”.
Embedding analytics within the core processes helps bridge this information gap. Different types of business users can take advantage of embedded analytics in different ways:
Role-based content kits (collection of metrics, KPI’s, dashboards, and scorecards) that show “what happened” help users such as executives, underwriters, marketers, agents, and front-line managers with their daily tasks;
Embedded diagnostic analytics (“Why did something happen?”) can provide seamless views of information for claims and underwriting management, agents, and even for the insured customers;
Embedding prediction and scoring enables the front-lines to quickly understand conglomerates of information for triaging, assignment, and pre-identification of high severity claims;
Embedding real-time risk assessments based on varying profiles and spatial rule analysis helps underwriters make effective and efficient underwriting decisions; and
Real time streaming analytics support a variety of use cases, such as quote stagnation and notification; real-time catastrophe triage and assignment; team management and performance; and sentiment analysis and quick resolutions.
Data in motion
Modern core systems are mature transactional systems and contain what can be characterized as “data at rest”. Emerging big data processing platforms store and analyze “data in motion” to deliver actionable intelligence. The “data in motion” is created by the connected world – data from IoT devices, wearables, social media, video, voice/audio, email/texts, telematics, etc. Not only is more “data in motion” being produced everyday, but access to this “data in motion” is also becoming simpler. The ease of data access, coupled with cheap computing power, better algorithms, and the rise of artificial intelligence (AI) technologies, is already disrupting our industry.
The right decision requires more than just the core system data. Insurers are looking to integrate and use curated third-party data sources (geospatial, weather, crime, financial, geopolitical, etc.), streaming data, as well as data residing in other ancillary systems. From a timing perspective, the smart (or next-generation digital insurer) is already taking advantage of “data in motion” to improve their top and bottom lines.
Data from IoT devices and social media is making the underwriter more efficient and effective with improved risk assessments. Machine learning algorithms and cognitive computing helps with actuarial analysis. Customer service is improving with sentiment and behavioral analysis coupled with deploying AI-based assistants. Finally, claims processing is benefiting from fraud detection models and blockchain-based payment processing.
Conjoinment of data and analytics today
There are two main areas within core processing that are most impacted by the conjoinment of data and analytics today:
Customer Analytics: Traditionally, insurers have used data such as demographics, vehicles, payments, claims, and some other third-party data for pricing decisions. However, insurers now have a lot more data that drives personalization and can help customers in unexpected ways. Data such as telematics to pinpoint risk, behavioral data, social media activity, online activity, and other unstructured customer data (call center data, images, etc.) help identify drivers of retention and improve customer satisfaction. This data can also help gain new customers, thereby improving profitability. Answering questions such as “what is the customer’s preferred channel?”, “how and when do we provide appropriate guidance?”, and “how can we provide value-added services with IoT?” are essential elements of customer analytics.
Product Analytics: This essentially consists of improving the underwriting process, selling more existing products, designing new products, and entering new markets. An underwriter is essentially looking to optimize the insurer’s portfolio of risks by spotting the profitable segments and customers. Traditionally, an underwriter has had to use multiple systems and even perform site visits prior to making a decision. Analytics with integrated and curated data sets; smarter risk assessment algorithms; and the use of drones and telematics data has reduced underwriting decision times from days to hours.
I concluded my talk with the following three key takeaways for insurers to leverage to benefit from the convergence of core systems and data and analytics:
It all starts with getting your data strategy and the timing of the data strategy right to set a strong foundation. The core transformation project itself spawns a parallel data project, and insurers are increasingly looking to handle these issues together.
In Guidewire’s experience:
The timing of these core and data projects is almost simultaneous;
The data project requires an understanding of the underlying core system data models, not just of an abstract ideal of an “industry model”; and
The data and core platforms must evolve in tandem.
Insurers should start small and then scale out, instead of taking a big bang approach. Start by tackling processes that will help improve customer service along with enhancing internal productivity; and in the near future we can expect insurers to leverage cognitive computing and AI-based technologies to not only improve the speed and accuracy of decisions, but also to reduce costs through automation.