![]() That financial platform differentiates it from some of the other startups in insurance tech. With more than $60 billion in assets under management it also provides tremendous financial backing to TSIQ,” said Modesitt. Two Sigma was founded in 2001 as a quantitative investment manager so it has lots of experience with data. The most obvious example of this is vehicle driving data from telematics, but any IoT initiative or drone project is going to create lots and lots of new data that insurers aren’t good at handling.” “But some of it is really new types of data that are not easily processed in traditional ways. That might include data hidden in a broker’s email. The industry is also seeing a lot of new, less structured data emerge, he added. There are a lot of legacy systems that have trouble connecting to modern systems and insurers are using RPA to migrate fields from one system to another.” Meaning: can we get the fields from a PDF of a policy submission form converted into digital data in a structured database. “So you’ve got a ton of technology right now focused on ‘extract/ingest’ from forms. “Because the insurance industry is so form-driven, it’s largely built around structured data,” he added. ![]() Essentially a data-focused solution for insurers rather than a process-focused solution, he said.” ![]() The new evolution of underwriting workbenches focus on intelligent decision making, process automation, risk modeling, and smart data aggregation. “The past generation of underwriting systems have really been case management systems, focused on rules, to-do lists, communication, and note taking. Jeff Goldberg, head of insurance at Aite-Novarica Group, said it is part of a new generation of underwriting tools. Two Sigma’s SubmissionIQ “collects, organizes and enriches submissions, feeding the underwriting workflow with comprehensive risk and performance insights to prioritize and route submissions, review individual risks and proactively manage production.” Although data scientists are supposed to be the stars of financial analysis, often they spend much of their time in the distinctly unglamorous work of assembling data from disparate data stores and normalizing it.
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