From Data to Decisions – Top 3 Application Areas for BI Tools in Insurance
Article Synopsis :
What are some exciting areas for AI-powered BI applications in insurance? This whitepaper from Swiss AI firm Veezoo takes an educated stab.
Digital Transformation is a term that gets thrown around a lot, but what does it really mean? To the authors, it means three things:
- Leaving legacy systems – Insurers are modernizing operations by automating internal business processes to achieve higher efficiency. A big aim of these efforts is to overhaul legacy IT systems, which act as stumbling blocks in the digital age, since they are not compatible with new interfaces, generate huge system complexity and are very costly to maintain.
- Building a smooth user experience for Millennials – Digitization influences demand. The new customer generation has very different – and in many respects higher- expectations for the steps along the customer journey due to vast experience with customer-facing technology in other industries. From buying shoes on Amazon to booking a holiday on Booking.com to reading the e-version of the New York Times on the way to work, everything is online, easily accessible and intuitive.
- Using AI and big data to reinvent insurance as we’ve come to know it – Insurance startups (as well as some pioneering incumbents) are employing smart sensors and IoT combined with AI to supply customers with innovative insurance products that are highly personalized for both features and price.
Focusing specifically on this third point of digital transformation, the report identifies three use cases for insurers to leverage AI and data for actionable insights.
- Simplified identification of cross-selling opportunities in the client portfolio, and the ability to quickly launch new targeted campaigns. Both of these have tangible business value.
- Improved sales team performance—not only monitoring agent performance but actually understanding agent behavior, toward higher overall sales force effectiveness.
- Accelerated reporting. Equipping employees with easy-to-use data analysis tools can save time and money by reducing reporting backlogs. The benefit is twofold: business users do not have to wait and can make quicker and more informed decisions, while controllers and reporting analysts can focus on more strategic work.
All three use cases leverage data that large insurance companies typically have on hand—even if they lack the right instruments to exploit it.
Intuitive and actionable access to information for major strategic and/or daily operational decisions is key to achieving sustainable competitive advantage.
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Digital Insurer's CommentsWith neural networks (AI), though the software isn’t trivial, it’s far less important than the quality of the data. Massive scale data sets are often required to generate meaningful findings.
Insurers typically have the data. The problem is it’s either locked in a mainframe or stored across thousands of disparate databases. New approaches and tools are improving the economics of large-scale data extraction and consolidation for the purposes of AI. Which is why we see AI as a horizontal enabling layer that, within three years, will make every enterprise that takes advantage better, smarter, and faster.
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