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Opening a New Era with Machine Learning in Financial Services? Forecasting Corporate Credit Ratings Based on Annual Financial Statements

International journal of financial studies, 2023-09, Vol.11 (3), p.96 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2227-7072 ;EISSN: 2227-7072 ;DOI: 10.3390/ijfs11030096

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  • Title:
    Opening a New Era with Machine Learning in Financial Services? Forecasting Corporate Credit Ratings Based on Annual Financial Statements
  • Author: Pamuk, Mustafa ; Schumann, Matthias
  • Subjects: annual financial statements ; Credit ratings ; Credit scoring ; Data mining ; Financial services ; Financial statements ; Forecasting ; forecasting corporate credit ratings ; Government regulation of business ; Machine learning ; multi-class classification ; Ratings & rankings
  • Is Part Of: International journal of financial studies, 2023-09, Vol.11 (3), p.96
  • Description: Corporate credit ratings provide multiple strategic, financial, and managerial benefits for decision-makers. Therefore, it is essential to have accurate and up-to-date ratings to continuously monitor companies’ financial situations when making financial credit decisions. Machine learning (ML)-based internal models can be used for the assessment of companies’ financial situations using annual statements. Particularly, it is necessary to check whether these ML models achieve better results compared to statistical methods. Due to the multi-class classification problem when forecasting corporate credit ratings, the development, monitoring, and maintenance of ML-based systems are more challenging compared to simple classifications. This problem becomes even more complex due to the required coordination with financial regulators (e.g., OECD, EBA, BaFin, etc.). Furthermore, the ML models must be updated regularly due to the periodic nature of annual statements as a dataset. To address the problem of the limited dataset, multiple sampling strategies and machine learning algorithms can be combined for accurate and up-to-date forecasting of credit ratings. This paper provides various implications for ML-based forecasting of credit ratings and presents an approach for combining sampling strategies and ML techniques. It also provides design recommendations for ML-based services in the finance industry on how to fulfill the existing regulations.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2227-7072
    EISSN: 2227-7072
    DOI: 10.3390/ijfs11030096
  • Source: Coronavirus Research Database
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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