Just as you wouldn’t build a mansion on quicksand, you shouldn’t build your insurance strategy on questionable data. Whether it’s AI, machine learning or underwriting and pricing decisions, the success or failure of every aspect of your company’s digital transformation may hinge on the reliability of your company’s data.
Every claim you receive provides valuable information about your customers and their loss events. If you can tap into this data, you can obtain valuable insights into your loss drivers, which puts you in the perfect position to control your risks and lower your costs.
The problem is traditional FNOL processes make this data hard to access. To run analytics and obtain valuable insights, you need clean, structured data – and digital FNOL processes can provide it.
When we’ve talked about digital FNOL processes before, we’ve focused on how they can improve operating efficiency and boost customer satisfaction. These are certainly huge advantages. A truly digital FNOL process eliminates manual data entry through reflexive forms and automation. This saves time and reduces the chance of human error, resulting in a better, more streamlined experience for your policyholders.
Structured data is yet another bonus. The digital FNOL process puts accurate, structured data at your fingertips.
If you’ve read anything about the insurance industry in recent years, you’ve no doubt seen numerous references to big data. Insurance companies are scrambling to secure data – and for good reason. McKinsey & Company says that “data and analytics are changing the basis of competition,” with leading companies leveraging data to improve their core operations and launch new business models.
As insurers grapple with natural disasters and rising losses, the importance of data-driven insights grows. Financial Technology Today discusses how insurers need accurate, real-time data to price natural disaster risks appropriately. Without quality data, insurers can’t adjust their models for rising risks, which could result in major losses.
Indeed, major losses are already happening. According to Business Insurance, property reinsurance rates surged in January as reinsurers adjusted pricing on catastrophe coverage and tightened terms. In addition, Insurance Journal says California insurers are unable to use sophisticated computer models to price growing wildfire risks. Both State Farm and Allstate have stopped writing new coverage in the state. Insurers have always depended on data, but the importance of accurate data is growing.
Data can power AI and machine learning. It can also give insurers the information they need to tackle rising risks. However, if you want quality insights, you need to start with quality data.
As insurers build up sources of data, they need to remember that not all data is equal. According to LexisNexis, inaccuracies in data can stem from many sources, including human error and departmental silos that block data integration.
Once poor data is in your processes, it can wreak havoc. According to a report from Information Builders, poor data quality is the main reason 40% of business initiatives fail to achieve their targeted benefits. It also reduces overall labor productivity by up to 20%.
This means insurers can’t simply focus on gathering data – they need to prioritize high-quality data. Once they have this data, they can build a data lineage that can serve as the foundation for business growth. However, if you start with poor-quality data, you’ll be building everything on a faulty foundation, which can lead to bigger and costlier problems down the road.
Insurance industry transformation is happening fast. To keep up with competitors and manage evolving risks and skyrocketing losses, insurers need to implement tech strategies that produce results. This starts with high-quality data – and high-quality data starts with your own digital FNOL process. Once you have structured data from your own digital FNOL process, you can layer it with data from third-party sources, such as property and licensing records.
Whether you’re looking for better insurance data, superior customer experiences, or increased operating efficiency, Liberate’s future-ready digital FNOL process can give you a competitive advantage. Learn more.
Get the latest product and management insights.
Your insurance customers want to engage with you using the most convenient channel at the moment – messaging/chat, SMS/text, email or voice. On any given day, a policyholder or broker could choose different communication channels at different times, depending on whether they’re in their car, at their desk or out for dinner. If your AI solution only works on one communication channel, you’ve got a problem. Your AI agent should communicate in every channel and move seamlessly between them – just like humans!
Technology shouldn’t be difficult to access. With Liberate’s Operator AI system, insurance professionals can use natural language to interact with highly-sophisticated AI programs. Operator AI is the streamlined, user-friendly way to leverage AI for underwriting and other insurance tasks.
Everyone’s talking about generative AI – but what comes next? Although generative AI is impressive, it’s just the tip of the iceberg of what AI will be able do. As technology continues to progress, AI is moving from generation to synthesis. This has major implications for insurance companies.