Accéder directement au contenu Accéder directement à la navigation
Pré-publication, Document de travail

Accuracies of Model Risks in Finance using Machine Learning

Abstract : There is increasing interest in using Artificial Intelligence (AI) and machine learning techniques to enhance risk management from credit risk to operational risk. Moreover, recent applications of machine learning models in risk management have proved efficient. That notwithstanding, while using machine learning techniques can have considerable benefits, they also can introduce risk of their own, when the models are wrong. Therefore, machine learning models must be tested and validated before they can be used. The aim of this work is to explore some existing machine learning models for operational risk, by comparing their accuracies. Because a model should add value and reduce risk, particular attention is paid on how to evaluate it’s performance, robustness and limitations. After using the existing machine learning and deep learning methods for operational risk, particularly on risk of fraud, we compared accuracies of these models based on the following metrics: accuracy, F1-Score, AUROC curve and precision. We equally used quantitative validation such as Back-testing and Stress-testing for performance analysis of the model on historical data, and the sensibility of the model for extreme but plausible scenarios like the Covid-19 period. Our results show that, Logistic regression out performs all deep learning models considered for fraud detection
Type de document :
Pré-publication, Document de travail
Liste complète des métadonnées

https://hal.umontpellier.fr/hal-03191437
Contributeur : Mélanie KARLI Connectez-vous pour contacter le contributeur
Soumis le : mercredi 7 avril 2021 - 10:45:03
Dernière modification le : jeudi 19 mai 2022 - 16:04:07
Archivage à long terme le : : jeudi 8 juillet 2021 - 18:59:47

Fichier

Accuracies_of_Model_Risks_in_F...
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-03191437, version 1

Collections

Citation

Berthine Nyunga Mpinda, Jules Sadefo-Kamdem, Salomey Osei, Jeremiah Fadugba. Accuracies of Model Risks in Finance using Machine Learning. 2021. ⟨hal-03191437⟩

Partager

Métriques

Consultations de la notice

201

Téléchargements de fichiers

484