[Case Study] Top Commercial Insurer Identifies Emerging Client Reputational Risk to Predict Losses


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The Data Science Lab and AI Innovation Center of one of the top commercial insurers contacted Bitvore about leveraging Bitvore Cellenus to surveil clients for emerging reputational risk to enhance predictive modeling of potential losses.

The Problem

Identify emerging risks for clients of Commercial Insurance, Workers Comp and Employee Benefits solutions.


The Solution

  • Identify emerging client reputational risks
  • Model predictive likelihood of a legal risk and predict losses
  • Identify leadership change metrics – Executive Change signal support
  • Identify detailed Legal and Business Risk material events (e.g., Sanctions, Regulatory Issues, Corruption/Fraud, Legal Investigations, Lawsuits, etc.) •
  • Identify emerging ESG-related issues to support reputational risk assessment

Know First, Act First

Get leading indicators of material changes affecting companies, industries, markets and emerging themes. With Bitvore Cellenus you’ll have the competitive advantage of leading indicators, so you’ll act first.


Bitvore Provides

  • Comprehensive, Clean and Normalized Data
  • Over 60K High Quality Sources
  • Over 20K Licensed Sources
  • Over 100 Material Business Events
  • Comprehensive ESG Signal Set
  • Delivery via API and File Download
  • Company Sentiment Scoring
  • Company Growth and Risk Scoring
  • Industry and Market Insights
  • 5 years of Historical Content

Download the PDF version of this case study below!



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