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Jan Thiemen Postema

MSC
Consultant
1101 BE, NL

Jan Thiemen Postema works as a consultant in the Amsterdam office of Milliman.

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Experience

Jan Thiemen joined Milliman in 2020, and has five years of experience working in the realm of data science. Before joining Milliman, he worked in a similar role at Ortec Finance. At Milliman, he focusses mainly on the application of data science and machine learning techniques in the insurance sector, as well as data engineering and visualization projects.

Publications

Read their latest work

Article

Flood risk modelling in Europe

02 May 2024 - by Jan Thiemen Postema, Daniël van Dam, Menno van Wijk, Niels Van der Laan, Antoine Rainaud, Eve Titon
We present a framework and tools for projecting insured flood losses in the Netherlands and France, which can be used for insurance portfolios across Europe.
Article

欧州における洪水リスクのモデリング

02 May 2024 - by Jan Thiemen Postema, Daniël van Dam, Menno van Wijk, Niels Van der Laan, Antoine Rainaud, Eve Titon
オランダおよびフランスにおける洪水保険の損害を予測するためのフレームワークとツールについて紹介します。
Article

Impact of COVID-19 on best estimate mortality assumptions

08 September 2023 - by Flora Auter, Amal Elfassihi, Salima el Khababi, Jan Thiemen Postema, Eve Titon, Raymond van Es
Using mortality data adjustment methods for COVID-19 helps avoid double counting and derives long-term trends without instabilities from a one-off experience.
Article

COVID-19の最良推計死亡率前提条件への影響

08 September 2023 - by Flora Auter, Amal Elfassihi, Salima el Khababi, Jan Thiemen Postema, Eve Titon, Raymond van Es
COVID-19の死亡率データ補正法を用いることで、重複計上を避け、単発的経験による不安定性なしに長期的なトレンドを導き出すことができます。
Article

FICO® Score 10 and FICO® Score 10 T model assessment

25 July 2023 - by Kenneth A. Bjurstrom, Raymond van Es, Ryan Huff, Jan Thiemen Postema
FICO 10 and FICO 10 T mark a meaningful advancement in assessing credit in mortgage lending, powered by trended data and cutting-edge analytical techniques.
Article

Anomaly detection techniques in fraud detection, performance optimization, and data quality

08 March 2023 - by Bjorn Blom, Jan Thiemen Postema, Rens IJsendijk, Judith Houtepen, Job Prince
Methods to detect anomalies can be used to find fraudulent claims in insurance, especially in products with a large frequency of payments, such as in healthcare.
Article

不正検出、パフォーマンスの最適化、そしてデータ品質における異常検出手法

08 March 2023 - by Bjorn Blom, Jan Thiemen Postema, Rens IJsendijk, Judith Houtepen, Job Prince
異常を検出する様々な手法は、特に医療保険など支払頻度が多い商品など、保険の不正請求を検出するためにも使用できます。
Article

不正検出における説明可能なAI

22 December 2022 - by Inès Zitouni, Jan Thiemen Postema, Raymond van Es
説明可能な人工知能における技術は、なぜモデルが真に不正な保険金請求を特定可能であり、不正調査員の手間暇を削減できるのかについての理由を示唆します。
Article

Explainable AI in fraud detection

22 December 2022 - by Inès Zitouni, Jan Thiemen Postema, Raymond van Es
Techniques in explainable artificial intelligence can indicate the reasons why a model expects a claim to be truly fraudulent, saving time for investigators.
Article

Improved mortality rate forecasting using machine learning and open data

23 November 2022 - by Jan Thiemen Postema, Raymond van Es
Compared with traditional techniques, state-of-the-art machine learning algorithms can substantially improve the forecasts of mortality rates.