3 Questions: Nilesh Dambe, VP, Bold Penguin Insurance Intelligence Product and Data Operations

Bold Penguin's "Data Detective" Explains How His Team Is Working To Make Underwriting Less Risky


1. Your team is taking big steps toward making small commercial insurance underwriting more efficient. How do data tools like SubmissionLink help these users make better risk decisions?  

There is more data than ever available to insurance underwriters and risk managers. Nevertheless, many of these insurance professionals still rely on legacy data sources, including information pulled from customers’ insurance policy applications. Managing all this data can make small commercial insurance underwriting challenging compared to other insurance lines. 

SubmissionLink is one example of how we address the complexity of small commercial underwriting by building the only tool that combines data enrichment and data extraction functions to create the most efficient, highest trust commercial enablement network. 

SubmissionLink performs data validation during the quoting process by filling in information that is not provided and defining more underwriting-oriented questions. This product helps to solve a major challenge for carriers by arbitrating between enrichment and extraction used for underwriting automation without compromising effective risk selection.

2. Explain how tools like AI and machine learning are essential to streamlining underwriting. How does output from these sources help to ensure accuracy of data input?

 The insurance industry, which has long been resistant to change, is undergoing a technology revolution. One of the benefits of this revolution is that it allows underwriters to explore technology that brings in more information to better assess risk and offer competitive pricing.

Commercial insurance is a complex business. Small businesses that apply for insurance coverage sometimes commit errors– honest mistakes and deliberately withholding important information that might negatively impact risk assessment.  

SubmissionLink uses machine learning, especially Natural Language Understanding and AI built on top of enriched and extracted data. Doing this reduces processing time and improves accuracy, while also digitally applying underwriting wisdom for successful risk assessment.

3. What are some of the ways your team is improving the quality of analytical data to create better underwriting outcomes?

One way is by making data more transparent for users. We designed SubmissionLink to provide complete traceability to users, which cuts the time needed to conduct underwriting audits, claim adjudication, and other important functions.

Where SubmissionLink shines, in my opinion, is in how it collects data points and information from third-party sources about a business’s operations–its voice-of-customer/voice-of-employee comments and ratings, public motor vehicle databases, past insurance claim data, and other records. 

SubmissionLink acts as a “data detective” to find hidden facts about companies’ employment practices and work conditions (e.g., whether workers need hazardous materials handling certification, or are working on high-rise building construction). This gives underwriters the big picture view even if a business does not disclose all relevant information on coverage application documents.

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