Leverage the Power of Predictive Modelling to Automate Underwriting

Our artificial intelligence solution for life insurers reduces customer acquisition costs, expedites underwriting, boosts customer satisfaction, and increases sales without increasing risk exposure.

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Optimize Medical Testing

Use predictive models to determine which medical tests are likely to yield unusual results and should thus be prescribed

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Empower Underwriters

Equip your underwriters with state-of-the-art machine learning models to inform and support their decision-making

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Improve Customer Satisfaction

Allow low-risk applicants to go through a quick and simple process with no fluid testing

What We Do

At Prolego.ai, we allow life insurance companies to reduce their customer acquisition costs and to optimize their application process in real-time. By supplementing underwriters with predictive risk assessments, they can determine which applicants are low-risk and for whom certain medical tests would be unnecessary.

With predictive modelling, everyone wins. Insurers minimize invasive medical tests and consumers get access to an accelerated underwriting process.

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How It Works

Our state-of-the-art predictive models are powered by performing supervised learning on historical insurance application and medical test data.

Using this information, we can provide underwriters with accurate risk assessments of new applicants, optimizing the underwriters' workflow. Furthermore, every time an underwriter makes a decision about whether to prescribe a medical test, the AI learns about whether its recommendation was correct, further increasing its accuracy.

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What We Offer

We offer you full customizability and tailor our predictive model to work with your application form. We allow you to automate predictions by connecting our model directly to your application form. We also provide a simple and easy-to-use interface for underwriters, allowing them to quickly get the risk predictions they want. Now, if your underwriters decide that the applicant's risk of foregoing a medical test is sufficiently low, that test can be skipped, saving the insurance company hundreds of dollars per applicant and greatly accelerating the time to issue the policy.

Our model is trained on an insurance company's historical data, meaning that it reflects the real risk of the applicants as accurately as possible.

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Our Partners

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Leadership Team

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Mahan Salehi, CEO

As a former CEO and co-founder of a technology startup, Mahan leverages his expertise in business development, sales, and finance to lead the company's vision of reinventing underwriting. Throughout his engineering degree, he has developed a passion for tackling complex business problems using predictive modelling.

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Daniyar Akhmedjanov, CTO

From working on the development of a novel reinforcement learning trading algorithm, Daniyar has experience developing cutting edge AI solutions for large financial institutions. At Prolego.ai, Daniyar's practical expertise in the latest machine learning techniques allows him to make effective and elegant models for solving prediction problems.

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Kuba Wernerowski, CIO

Kuba's talent for automation and fullstack development has resulted in solutions that have generated massive savings for international institutions. He has extensive experience creating user friendly products that are simple and intuitive to use. At Prolego.ai, he is spearheading product development and design.

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Andrey Boris Khesin, Lead AI Scientist

Andrey brings a strong academic background in machine learning and has published research on algorithm development. He leverages his deep understanding of the mathematical theory behind AI technology to develop the underlying engine behind Prolego.ai's prediction machine.

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