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Antoine Ly, PRSLF – R&D

Consulting Actuary, Data Scientist - Paris
Paris, FR

Tel: +33 1 42 99 1560

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Professional Designations

  • Actuary, French Institute of Actuaries. Datascientist.


  • Statistician and Economist engineer from ENSAE ParisTech
  • Master degree in Big Data from Université Pierre et Marie Curie (Paris 6)
  • Associate Actuary (France)
  • Antoine is currently a PhD candidate and researching the application of machine learning algorithms in rare event detections.

Current Responsibility

Antoine is a data scientist and actuary. He joined Milliman’s Paris office in 2015.


Antoine has two years of experience as an actuary, including:

R&D - Predictive Analytics

  • SOLYS development
  • Researches and developments linked to machine learning

Antoine is a member of the Research and Development team at Milliman’s Paris office. He teaches DSA training at the French Institute of Actuaries and is a teaching assistant at ENSAE ParisTech school for "Python for a Data Scientist" and "The big data analytics tools" classes.

Antoine has worked on several predictive analytics projects such as behaviors prediction with neural networks, solvency capital determination with machine learning algorithms or traffic modeling.

With the Predictive Analytics team, Antoine is developing a big data solution (Solys) with Python, R and Spark. Antoine is also the president of the Data Science Game, an international competition for students in big data.

Life - Solvency II

  • Replicating Portfolio
  • Works on the Wu and Zhang Stochastic Volatility Model (used to price and forecast Swaption)
  • Least Square Monte Carlo methods to compute SCR

Predictive modeling

  • Switches modeling with neural networks
  • Yield management—traffic and income predictions
  • Developments on the Milliman Predictive Analytics Tool
  • Machine Learning algorithms—CART, Random Forest, Neural Networks, SVMs, Knn etc.
  • Work at Milliman

    • “I was offered a chance to come in, shake things up, create something."
    Work at Milliman