Increase policyholder satisfaction and loyalty while effectively managing risk and fraud with external data

machine learning insurance

Insurance is a data-driven industry, where it is common to make use of multiple data sources in decision-making processes.

External data sources are becoming more important when it comes to detecting potentially fraudulent insurance claims. External data offers key signals not available in internal data sources: weather, credit scores, technographics, firmographics, regulatory, and governmental. 

Explorium's automated data science platform


Consolidating external data for analytics is resource and time consuming
Explorium’s External Data Platform centralizes access to public, premium, and proprietary external data for business analysts, data scientists, and business leaders

Transforming, matching, and integrating external data is tedious work
Explorium’s External Data Platform ML capabilities automatically match and integrate external data with your internal data accelerating time to value

Assessing the value of external data is inexact
Explorium’s External Data Platform identifies the relevant external data signals and calculates the uplift in your ML models before deployment

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The right external data is a competitive advantage. 

Understanding the broader trends in the market and including them in predictive models improves their performance. Enrich your internal data with external data signals to gain better insights and improve business outcomes.
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Refine customer lifetime value and churn analysis

Consumers have more options than ever when deciding on insurance providers. Identifying profitable customers is critical - before they jump to a competitor. Redefine LTV and churn models by enriching your internal data with external data signals such as payment history compared to socioeconomic and income status, social activity, reviews of similar products, and income and claims history
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Improve customer conversions

Increased competition and choice has put pressure on insurance companies to improve conversion rates. The challenge is understanding which factors could lead to conversion at the start of the cycle, as soon as  users submit an initial form. External data allows insurance companies to make smarter marketing decisions by understanding which actions would lead to user conversion. As a result insurers can  design a better user journey.

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Reduce claims fraud and costs

Insurance claim fraud costs the industry billions every year. Efforts to reduce fraud without impacting the customer experience is a careful balancing act. Insurance companies need a way  to detect fraudulent claims earlier in the process without adding more steps that could create bottlenecks and impact their user experience. Enriching fraud models with external data such as economic information, social media interactions, and alternative risk boosts their accuracy. This enables you to offer a much better service to your customers, boost  revenue, and reduce churn. 

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external data acquisition

If you’d like to know how you compare with other organizations in the pursuit of the most relevant 3rd-party data, then don’t miss our latest research.


Unlock new business value with external data.

Explorium’s External Data Platform improves analytics and machine learning. Explorium enables organizations to automatically discover and use thousands of relevant data signals to improve predictions and ML model performance. Explorium’s  External Data Platform empowers data scientists and analysts to acquire and integrate third-party data efficiently, cost-effectively and in compliance with regulations.

machine learning insurance
Example data signals for Insurance

Review based information:

  • Business rating and reviews
  • Health score
  • Noise level
  • Average pricing of businesses in area

Economic information:

  • Annual revenue
  • Number of sales
  • Payroll
  • Growth and stability indicators
  • Loan/lending information

Business information:

  • Number of businesses nearby by category
  • Foot traffic information: number of visits in the area, places people visited on the same day, etc.
  • Tourist attributes: hotels and attraction characteristics (number of hotel rooms in an area, etc.)

View our Data Gallery to see more examples and learn more.

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