Cemil Ates

I specialize in transforming complex insurance data into actionable strategic insights. With a strong foundation in Actuarial Science and advanced Machine Learning, I help insurance companies and financial institutions navigate the shift toward data-driven decision-making and regulatory excellence.

My expertise lies at the intersection of traditional actuarial methods and modern computational techniques. Whether it’s implementing IFRS 17 reporting standards, optimizing health insurance provider networks through Linear Programming, or building predictive models for pricing and customer behavior, I deliver solutions that are both technically rigorous and business-aligned.

Key Areas of Expertise:

Actuarial Modeling: Pricing, reserving, and financial reporting (specializing in IFRS 17 frameworks).

Insurance Analytics: Developing ML models for offer acceptance, churn prediction, and risk assessment.

Optimization: Solving complex business problems using Linear Programming (e.g., network design and resource allocation).

Quantitative Finance: Stochastic calculus applications, financial market simulations, and risk minimization strategies.

Technical Stack: Python (Pandas, Scikit-learn, Pyomo), R, SQL, and Financial Modeling.

I am passionate about helping startups and established firms build scalable actuarial services and robust data pipelines. If you are looking for a consultant who understands both the "why" of insurance risk and the "how" of data science, let’s connect.