VERGNE ADVISORY 

Data Analytics: The process of inspecting, cleansing, transforming, and modeling data, intended to discover useful information—to form data-driven conclusions, and support business decision-making. ​ Risk Management: Identification, evaluation, and prioritization of risks followed by coordinated application of resources to minimize, monitor, and control the probability or impact of unfortunate events or to maximize the realization of opportunities.

Growth Capital for Start Ups and Growth Stage Companies I am the Insurance Resource for a partner company that is a business development and capital advisory with a proven track record. We have firsthand experience collaborating with the principals who for the past 30 years have worked with start-ups and growth stage companies collaborating on sales, marketing, operational planning, and many times preparing them for funding, expanding, and achieving significant milestones along the way. Their business development ecosystem is a proven 3 step process taking start-ups and growth stage companies from where they are today to where they want to go. This uniform, process driven, 3 step approach for owners / founders achieves market entry, next level growth and the overall vision including an exit. 1. Preparation 2. Capitalization 3. Performance Their strategic and rational approaches have proven successful with over 500 clients in recent years and on projects with NASA, Samsung, General Electric, and hundreds of other large and small companies, founders, and idea exploitation around the world. Contact us to understand more.

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Claims analytics: (tuning in to the new frontiers) How to assess the scope for improvement and effectiveness of current analytics / AI ML effort – Fraud, Subrogation and other? How to target effective use of internal claims data and available external data to target Litigation spend analysis, Industry drivers and Large loss propensity? Underwriting analytics (analytical readiness and adoption): How to leverage analytics and AI for skill-building and risk-taking and blend the role of human and machine for improving underwriting outcomes? What are some of the analytically effective ways to perform risk segmentation and submission prioritization? Data (strategy and execution guidance) How do you draft external vs internal data strategy? What external data sources you focus on? How do you decide? How do you create a balance between the use of Enterprise Data Warehouse vs Enterprise Data Lake? How do you leverage Cloud resources for data lake purposes? How do you initiate Enterprise wide Data Governance? What are the initial steps? How do you decide ownerships of items between technology vs business? How do you leverage value from unstructured data? What are the prerequisites? Analytics (strategy and planning): How do you begin drawing enterprise analytics strategy? How do you communicate and draw support? How do you plan for use of Visual analytics for storytelling purposes? How do you mentor data science team for such skills? How do you differentiate between pure statistical methods vs machine learning methods? Which methods do you promote under what circumstances? How do you promote early progress in the use of machine learning? How do you proliferate its use for business purposes? How do you plan use of descriptive, predictive vs prescriptive methods for business outcome purposes? How do you emphasize their use for actionable insights? How do you plan for models that leverage different types data – Structured and Unstructured data together or External and Internal data together? Tools, methods, resources and technologies: (experience and industry outlook) How you compare open source technologies (Python, R) vs package solutions for developing analytics? How do you process your growing and diverse analytics needs How do you build off-shore talent base to augment in-house data science team? What difficulties you may incur? What are some of the best practices in growing analytics team by promoting talent internal to the organization? How do you negotiate contracts with data analytics technology and/or consulting vendors? How do you overcome the intellectual property negotiations?

Would you be interested in Global Venture Capital Broadcast? We can drop your pitch to 43+ accredited investors, 77 Billionaires to a new affiliate database. It drives on the low-end 100-200 + interested investors, last drop generated over 620+ in 2 hours. You must have the resources / system to follow up on the interested parties since it's a very responsive list and we are very careful about who gets access. It is the fastest, cheapest way for you to get the funds needed. We can help by placing your venture press release in front of 43,000 investors at once. Who Are the Investors? Our investors identify themselves as high-net-worth angel investors, family offices, private equity professionals, venture capital professionals, and institutional investors. Broken down in the following regions: North America (26,000+), Europe (6,800+), Asia (1,960+), Middle East (450+), Australia (400+), South America, (140+), and Africa (110+). In total, it reaches over 43,000 investment professionals worldwide.