Posted on : August 10th 2022
The financial services industry needs data-driven tools to convert a goldmine of data into usable insights. Technology is proceeding rapidly to connect the dots in understanding customers and risks, and data-driven strategies are at the center of these operations.
From underwriting to risk assessment, claims settlement, and customer interactions, insurance enterprises must produce and process vast data volumes, particularly unstructured data. In these processes, artificial intelligence (AI)–led technologies are helping reimage claims processing, insurance underwriting, fraud detection, and customer experience (CX).
Traditionally, conventional insurance institutions competed with similar entities. But the rise of agile technology-led companies ("Insurtechs") has brought new options to the market, and legacy players must factor in fresh entrants leveraging modern technologies to cement their competitive advantage.
The Insurtech Surge Thus Far
In December 2016, the Insurtech firm Lemonade set a world record. In 3 seconds, it reviewed a claim, cross-referenced it against the policy, performed 18 antifraud algorithms on the application, and approved it, resulting in an almost real-time payment. Early Insurtech players like Lemonade have shown how fintech can provide digital-first customer services with minimal legacy systems at lower costs.
The global Insurtech market volume touched US$10.6 billion in 2021 and is forecast to grow to US$158.9 billion by 2030. In practice, the fortunes of early Insurtechs may ebb and flow, but using technology for efficiency and customer service will grow. That's why long-standing carriers and brokers will be optimistic despite the first six months of 2022 being soft regarding Insurtech valuations.
Exhibit 1: Global venture capital investment in Insurtech start-ups
In the Insurtech paradigm, digital devices are encouraged as mediums of closing deals, and their comfort and efficiency have also changed customer expectations. Even insurance firms that cannot retool to become similar to tech-first Insurtechs could—and should—use newer technologies to boost efficiency and speed in data processing and CX.
In summary, all insurance companies must focus on seamless customer journeys, allowing for quick comparisons of features and prices and short buying journeys.
Benefitting from Data Solutions
An overwhelming quantity of enterprise data in the insurance sector is unstructured. Other hidden and alternative data varieties emanate from sensors, satellite images, traffic cameras, and others. Robotic process automation (RPA) does a great job with structured and even semistructured data sets; however, with unstructured data, they perform less well.
Exhibit 2: Understanding data types
Source: Tutorialspoint, Straive
A more advanced approach is needed to deal with the complexity and size of unstructured data pools, including technologies such as applied AI and machine learning (ML), computer vision, AI-driven optical character recognition (OCR), and natural language processing. In the insurance industry, effectively unlocking unstructured data and using these technologies is crucial for supporting everyday practical operations at speed, scale, and quality.
Exhibit 3: The journey from unstructured insurance data into structured formats
Examples of unstructured data in handling insurance claims include
Reliably extracting and interpreting unstructured data are keys to accurate, speedy, and high-quality claims processing.
The Nature of Unstructured Data
For insurers, unstructured data solutions focus on automating the capture and consolidation of data from various sources in diverse formats. Because they do not have a definite form, unstructured data cannot reside logically in a tabular row-and-column format. Unstructured data, generated by machines and humans in various forms, can also be "subjective." However, it typically does not have a pre-defined model, and analyzing unstructured data is challenging, time-consuming, error-prone, and manual processes also limit scalability.
Although machines can efficiently process structured data, building automation to analyze unstructured data can be more challenging. Often AI/ML technologies like computer vision and natural language processing are required, which is how intelligent automation solutions that leverage AI/ML help.
Intelligent unstructured data solutions are moving the insurance sector from purely financial key performance indicators to a broader mindset focusing on customer engagement, with AI/ML underpinning both customer service and selling agent experience cycles. Using insights from unstructured data to create more intelligent AI/ML models can drive a better understanding of the customer.
Exhibit 4: From data to insights with SDP
Today, insurance companies compete through technology, including AI/ML, transforming their distribution, CX, claims processing, and other areas. Investing in improving unstructured data gathering and ingestion continues apace.
Unstructured data provide new insights that fill the gaps in the big-picture understanding of the customer, and combining unstructured data with structured data improves business decisions, unleashing intelligent automation at scale.
The importance of automation is steadily growing for insurance companies. Platforms for end-to-end workflow automation (like RPA) are popular, but now the demand is to move to intelligent automation using unstructured data, and experimenting with a simple proof of concept and scaling is often the best bet. Ultimately, AI-driven automation and advanced analytics help companies better understand and serve their customers.
Daniel Schrieber, “Lemonade Sets a New World Record: How A.I. Jim Broke a World Record Without Breaking a Sweat,” Lemonade101, https://www.lemonade.com/blog/lemonade-sets-new-world-record
Julian Ostertag, Christophe Morvan, Michael Metzger, and Sam Levy, “Drake Star Insurtech Industry Report,” July 2022, https://www.drakestar.com/news/global-insurtech-industry-report-2022.
The process of data extraction involves identifying and recovering alternative and semi-structured data from various data sources such as files, XMLs, JSON, etc.
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