Peter Sarlin, PhD

Biography

Peter is the Executive Chairman and Chief Scientist of Silo.AI, leading the company’s AI platform initiative. He is also a Professor of Practice specializing in machine learning and artificial intelligence at Hanken School of Economics (Helsinki, Finland), and Director of RiskLab Finland at Arcada.

Peter is a co-organizer of the Annual RiskLab/BoF/ESRB Systemic Risk Analytics Conference and Europe’s 1st Fintech Master’s program. He is also a research associate with the Systemic Risk Center at London School of Economics, IWH Halle Institute for Economic Research and the Financial Innovation Lab at University of Cape Town, as well as a board member of the IEEE Analytics and Risk Technical Committee and the IEEE Computational Finance and Economics Technical Committee. Moreover, he is an Associate Editor of Journal of Network Theory in Finance and Intelligent Systems in Accounting, Finance & Management. Peter completed his PhD in 2013 at Turku Centre for Computer Science, and has also studied at London School of Economics, Stockholm School of Economics and Stockholm University.

Peter has built solutions as an external consultant with the European Central Bank, International Monetary Fund, Bank of Finland, Deutsche Bundesbank, De Nederlandsche Bank, Bank of Indonesia and Banco de la República of Colombia, as well as several other private organizations. He is also a founder of Infolytika Ventures and Almax Analytics.  Peter’s book Mapping Financial Stability was published by Springer in May 2014 and his current research interests include machine learning, natural-language processing, complex systems and visual analytics.

Research in the news

Work in progress

  • Using Artificial Intelligence to Create Value in Insurance, with Riikkinen M, Saarijärvi H, Lähteenmäki I. Submitted.
  • A framework for early-warning modeling. Submitted.
  • RiskRank to predict systemic banking crises with common exposures, with Giudici P, Spelta A, Björk K-M. Submitted.
  • Weighted crisis signals: A country versus a global observer.
  • State of the art in crisis prediction: A literature review and a tool for modeling, with Holopainen M.

Working papers

  • Cerchiello P, Nicola G, Rönnqvist S, Sarlin P, 2016. Deep Learning Bank Distress from News and Numerical Financial Data. [UNIPV] Submitted.
  • Forss T, Sarlin P, 2017. News-sentiment networks as a company risk indicator. [arXiv] Submitted.
  • Sarlin P, von Schweinitz G, 2015. Optimizing policymakers’ loss functions in crisis prediction: before, within or after? [ECB] [IWH] Submitted
  • Rancan M, Sarlin P, Peltonen T, 2015. Interconnectedness of the banking sector as a vulnerability to crises. [ECB] [SSRN] Submitted.

Selected publications