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How AI Empowers the Insurance Industry

Data science in insurance (including machine learning and AI) allows providers to reduce risk and streamline workflows, increasing value and improving the customer experience.

Every day, thousands of claims, diverse data, and customer queries are produced in the insurance industry, which makes it a perfect environment for AI-driven systems (including both data science and machine learning). Indeed, the application of data science in insurance is a must for providers to stay ahead of fraudsters, reduce losses, and provide the best risk-adjusted solutions to their customers.

Premiums, risk projections, claim adjustments, customized financial advisory, and underwriting are all derived from the provider’s ability to make accurate data-driven predictions. And it’s all connected; inaccurate forecasting leads to misaligned premiums, inaccurate risk projections, and ineffective balance sheet management — all of which can cripple an insurance provider’s bottom line and, ultimately, increase costs for the consumer. Plus the price of mistakes is high, so minimizing risk is critical.

High-Value Use Cases

Just some of the applications for data science in the insurance industry include:

Fraud Detection: Fraud detection is one of the most pressing use cases in the insurance industry, and AI can generate incredible efficiency and value gains; fraudulent activity costs the insurance industry billions annually. Whether it’s hard fraud (e.g., staged accidents) or soft fraud (e.g., embezzlement), there are always indicators that can suggest a high-risk claimant — while these are often subtle, if they are uncovered they can provide a significantly positive impact on an insurer’s bottom line.

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AI in Insurance: Use Cases, Challenges, & Trends


Pricing Optimization: With AI, insurance providers can dynamically monitor the marketplace, boost their understanding of risks to cover, and offer the best risk-adjusted prices. This enables them to stay competitive and retain the trust and accounts of their existing customers.

Customized and dynamic investment profiling: Thanks to AI, insurance companies can leverage the depth of understanding they have of their clients and evolving financial needs to offer tailored “robo-advised” financial solutions adjusted to current and evolving needs.

Marketing, Including Churn Prediction: Effectively identifying customers at risk of churning and then automating a system to take action on those at-risk customers is a perfect space for AI. With data from CRMs, claims-related data, website data, and more, machine learning-powered systems can automatically address likely-to-respond churners with targeted marketing messages.

Claims Triage and Forecasting: For claims triage, not being able to accurately isolate claims that warrant fast settlement—or deeper investigation— can be expensive and result in some significantly over- or under-paid claims. AI-powered systems are more sophisticated and nuanced than a rules-based system, and can make increased  granularity a reality. 

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Automating workflows, such as underwriting: Machine learning can leverage fuzzy matching to encode baseline underwriting logic in addition to an evolving algorithm that can optimize the engine’s performance over time. Additionally, natural language processing (NLP) can limit the amount of material that requires analyst review, streamlining all but the trickiest applications.

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Regulatory agility and efficiency in controls: Flexibility in production of analytics and integration of Machine Learning in regulatory-related controls are significant regulatory transformation enablers. Furthermore, integration of Machine Learning in the compliance landscape is being a must-have to significantly streamline volumes of false positives and reinforce focus of risk and compliance teams on material issues.

Dataiku for Insurance Companies

Dataiku is the platform democratizing access to data and enabling enterprises to build their own path to AI. Hundreds of companies use Dataiku daily to build, deploy, and monitor predictive data flows, solving problems like fraud, churn, claims triage, forecasting, and much more.

Specifically, Dataiku can help organizations in the insurance industry by:

  • Enabling regulatory compliance and auditability. Dataiku offers robust user permission tracking, ensuring that only authorized users can access sensitive information. Models are developed in a robust environment allowing for documented collaboration. Version history and usage justification information ensure auditability in the event of a compliance audit for an overall more responsible AI strategy.
  • Creating machine learning models, with or without coding. Dataiku has robust features for analysts (business, risk, actuaries, etc.) and data scientists alike, allowing everyone to derive insights from data for an inclusive AI strategy.

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