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Omina Technologies > Insurance
proactive insurance

Delivering proactive and personalized insurance is challenging

The expectations of insurance customers have increased, necessitating:

  • Personalized, continuous and dynamic risk prevention
  • Instant, personalized and seamless damage handling

Consequently, insurance companies must enable proactive, personalized, seamless risk management. Proactive and seamless risk management requires a paradigm shift from ‘repair & replace’ to ‘predict and prevent’ and the ability to analyse continuous individual-level data.

But personalized risk management conflicts with:

  • the increased need to reduce costs due to increased competition
  • the increased expectations and obligations regarding data privacy and protection

Regular AI is not up to the challenge

To reduce operational costs, many insurance companies started digitizing workflows and using AI to automate tasks, for example: automated risk assessments for faster and more accurate underwriting, automated fraud detection, automated customer service via chatbots and automated claim management. However, automating insurance tasks must allow for human agency and explanations of automated decisions that are understandable to insurance employees and customers (cf. GDPR).

To deliver personalized products and services, insurance companies started partnerships with companies that provide tools for tracking a customer’s continuous risk (e.g. partnership with Fitbit). Using AI to analyze these continuous individual data raises significant data privacy and data protection challenges.

Lack of human agency, explainability, fairness, and respect for data privacy result in lack of trust and, most often, a failure to adopt the AI solution.

domain expert distrust
EIOPA AI Governance

Trustworthy AI to enable proactive and personalized insurance for all

Trustworthy AI supports hyper-personalized insurance decisions that are fair, transparent and explainable by design. Trustworthy AI is ethical (fair, non-discriminating), lawful, and robust (Ethics Guidelines for Trustworthy AI, 2019). It enables fair and consistent decisions that are explainable and justifiable to all stakeholders.

The European Insurance and Occupational Pensions Authority published a report on trustworthy AI for insurance companies (2021): ‘’Artificial intelligence governance principles: towards ethical and trustworthy artificial intelligence in the European insurance sector.’’

Use Cases

Omina Technologies trustworthy AI by design helps you to support insurance decision making in an ethical and explainable way.

Precise, yet inclusive underwriting and pricing: AI can reinforce inequalities and discrimination in insurance risk underwriting and pricing. Actual customer-level risk behavior allows for more accurate underwriting and pricing. However, this can result in certain high risk customers having no or limited  access to (affordable) insurance products.

Explainable and fair fraud detection: 
Black box AI is a problem in machine learning because even the designers of an algorithm cannot explain why and how it arrived at a specific decision. For example, if an AI solution dealing with insurance fraud identifies an anomaly, the insurance company should be able to explain the reason the solution arrived at that conclusion. Black-box AI models do not allow for explainability creating a problem with auditability.

Likewise, claim handling should be fair and explainable.

Omina Technologies AI insurance use cases

Why Omina Technologies?

Omina Technologies has been an expert in ethical and trustworthy AI since 2016. We enable you to become a leader in proactive and personalized insurance for all by supporting you in building trustworthy AI solutions by design: