Difference between revisions of "Miller Janny Ariza Garzón"
(Created page with "Miller-Janny Ariza-Garzón [https://www.linkedin.com/in/millerjanny/?originalSubdomain=es], PhD student at Data Science [https://estudiosestadisticos.ucm.es/doctorado-analisi...") |
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Email: millerar@ucm.es | Email: millerar@ucm.es | ||
| − | == STSM grantee Period 1 ( | + | == STSM grantee Period 1 (September 2021). COST Action: CA19130 == |
Working group 2 - Transparent versus Black Box Decision-Support Models in the Financial Industry [https://fin-ai.eu/index.php/wg2-transparent-versus-black-box-decision-support-models-in-the-financial-industry/]. | Working group 2 - Transparent versus Black Box Decision-Support Models in the Financial Industry [https://fin-ai.eu/index.php/wg2-transparent-versus-black-box-decision-support-models-in-the-financial-industry/]. | ||
Revision as of 08:55, 9 March 2021
Miller-Janny Ariza-Garzón [1], PhD student at Data Science [2].Research member of Project FINTECH-EU Ho2020 [3]. Facultad de Informática [4]. Universidad Complutense de Madrid [5]. Spain.
Email: millerar@ucm.es
STSM grantee Period 1 (September 2021). COST Action: CA19130
Working group 2 - Transparent versus Black Box Decision-Support Models in the Financial Industry [6].
Host institution: ZHAW School of Engineering, Winterthur, Switzerland.
Home institution: Universidad Complutense de Madrid, Madrid, ES.
STSM title: Fintech and Artificial Intelligence in Finance - Towards a transparent financial industry (FinAI).
We will investigate the tradeoff between explainability and predictive performance of different black-box models as they apply to financial problem sets, primarily in risk management-credit risk. Specifically, we will study the elements that must be evaluated for a black-box model to be considered interpretable and explainable to take advantage of its predictive potential.