Machine Learning Platform for Insurance | Skymind

Machine Learning Platform for Insurance


Customers are switching to insurance providers who can provide the fastest claims processing, best pricing, and most responsive customer support. Executive, product, and engineering teams at insurance companies can achieve this using Machine Learning, but face several hurdles:

Lack of AI/ML Talent
There are not enough people with experience in developing and deploying ML solutions for insurance companies.
Strict internal and regulatory policies prohibit deployment of untested, unhardened solutions.
Point Solutions
Specialized solutions that utilize AI or ML to solve just one use case do not provide enough flexibility, control, and transparency.
Common ML solutions may not be compatible with the hybrid, Java-based, secured environments of insurance companies.


Insurance providers are leveraging Artificial Intelligence and Machine Learning (AI/ML) to automate painful and costly manual workflows. Skymind is a platform that helps product and engineering teams ship those ML capabilities into enterprise applications faster.

Ship ML-Powered Applications Faster

Empower engineering teams to use the language to build ML functionality for Java applications in enterprise environments, using the language and environments they already know.

Reduce Operational Overhead

Eliminate dependencies and interoperability issues between data science and engineering teams. Get enterprise-grade model reliability, performance, and scaling without maintenance, all on the JVM stack.

Automate Business Processes

Build ML-powered capabilities into existing insurance workflows using streaming or stored data from ERP and CRM systems, data lakes, Spark, Kafka, and elsewhere. Improve model performance over time with quality monitoring, A/B testing, and winner selection.

Make Data Science Work Useful

Engineers can integrate and deploy Python-based models developed by data science research teams into JVM environments and applications, or build their own models in a tracked and collaborative environment.

Talk to ML Expert Product Overview

Perspective: Machine Learning in the Insurance Industry

According to Forbes, claims processing is a notoriously laborious process. Along with the manual data entry of printed forms, the claims process tends to miss cases of fraud which result in over $40 billion dollars in losses per year. The sheer volume of claims makes it impossible for human analysts to properly vet each case in a timely manner.

On top of claims processing, insurers face competitive pressures to improve the customer experience. For example, underwriting is still largely done manually over the phone. Insurance advisory requires a limited supply of human experts to recommend plans that best fit a customer’s unique circumstances. Customer acquisition and support require call centers which can have long customer wait times. All of the above create negative customer experiences.

Machine Learning is a powerful tool that insurers can use to automate manual tasks, increase the efficiency of human analysts, and improve the customer experience.

Common applications of Machine Learning for insurance companies include:

  • Claims Fraud: Score claims by the likelihood that it is fraudulent. This enables a fraud analyst to focus effort on blatant cases of fraud.
  • Fraud rings: Neural networks are capable of uncovering sources of claims fraud (e.g. a specific individual, a specific organization, or clusters of people working together).
  • Document Digitization: A technique called optical character recognition (OCR) is commonly used to digitize handwritten documents. This enables a computer to extract data and automatically process documents without human intervention.
  • Lead Scoring: Rank visitors by the likelihood that they will purchase insurance using patterns in web activity.
  • Churn prediction: Understand and prevent churn by measuring the likelihood that a customer will switch to a different insurance provider.
  • Search: Improve the relevancy of search results to help customers better locate tools, tips, resources, and insurance information.
  • Relevant answers: Help customers resolve common inquiries by recommending answers to common questions.
  • Customer Support: Better engage customers using a chatbot to provide real-time insurance advice. Help customers resolve common inquiries using a virtual assistant.
  • Recommendation Engines: Recommend insurance plans based on a person’s unique history and situation.
  • Image Analysis: Evaluate if evidence (e.g. images or video) have been fraudulently tampered.
  • Risk Assessment: Improve risk assessment and predict reasonable premium rates for policyholders based on historical, demographic, and personal data.

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