AI-BASED ETHICS INDEX OF RUSSIAN BANKS

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Abstract

Measuring a company’s ethics is an important element in the mechanism of regulating the behavior of market participants, as it allows consumers and regulators to make better decisions, which has a disciplining effect on companies. We tested various methods of machine analysis of consumer feedback from Russian banks and developed an Ethics Index that allows us to calculate a quantitative assessment of the ethics of three hundred Russian banks based on consumer feedback for different time periods from 2005 to 2022. We used a bag-of-words method based on the Moral Foundations Dictionary (MFD) and BERT model training based on a 3,000- and 10,000-sentence sample marked up by experts. The resulting index was validated based on the number of arbitration cases from 2005 to 2022 (more ethical companies are involved in fewer arbitration cases as a defendant), with only the BERT model validated and the MFD-based model not. The ethicality index will be useful as an alternative metric to the popular ESG ratings both for theoretical research on company behavior and for practical tasks of managing company reputation and forming policies to regulate the behavior of market participants.

About the authors

M. A. Storchevoy

St. Petersburg School of Economics and Management, National Research University Higher School of Economics in St. Petersburg

Санкт-Петербург, Россия

P. A. Parshakov

International Laboratory of Intangible Asset Economics, National Research University Higher School of Economics in Perm; Moscow School of Management SKOLKOVO

Perm, Russia; Moscow, Russia

S. N. Paklina

International Laboratory of Intangible Asset Economics, National Research University Higher School of Economics in Perm

Perm, Russia

A. V. Buzmakov

International Laboratory of Intangible Asset Economics, National Research University Higher School of Economics in Perm

Perm, Russia

V. V. Krakovich

St. Petersburg School of Economics and Management, National Research University Higher School of Economics in St. Petersburg; International Laboratory of Intangible Asset Economics, National Research University Higher School of Economics in Perm

Email: mstorchevoy@hse.ru
St. Petersburg, Russia; Perm, Russia

References

  1. Гришанкова C. Д. Рейтинги ESG. ESGтрансформация как вектор устойчивого развития: В трех томах. Том 2. Под общ. ред. К. Е. Турбиной и И. Ю. Юргенса. М.: Издательство “Аспект Пресс”, 2022.
  2. La Torre M., Cardi M., Leo S., & Schettini Gherardini J. ESG Ratings, Scores, and Opinions: The State of the Art in Literature. Contemporary Issues in Sustainable Finance, 2023. С. 61–102.
  3. Игнатова О. В. ESG-рейтинги российского бизнеса. РИСК: Ресурсы, Информация, Снабжение, Конкуренция. 2022. № 1.
  4. Петров В. О., Стариков И. В., Фурщик М. А. Особенности отечественных ESG-рейтингов // Журнал Бюджет. 2022. № 4.
  5. Казаков А., Денисова С., Барсола И., Калугина Е., Молчанова И., Егоров И., Костерина А. et al. ESGify: автоматизированная классификация экологических, социальных и управленческих рисков // Доклады Российской академии наук. 2023. Т. 514. № 2.
  6. Brown T. J., & Dacin P. A. The company and the product: Corporate associations and consumer product responses. Journal of marketing, 61(1), 1997.
  7. Folkes V. S., & Kamins M. A. Effects of information about firms’ ethical and unethical actions on consumers’ attitudes. Journal of consumer psychology, 8(3), 1999.
  8. Sen S., & Bhattacharya C. B. Does doing good always lead to doing better? Consumer reactions to corporate social responsibility. Journal of marketing Research, 38(2), 2001.
  9. Brunk K.H. Exploring origins of ethical company/brand perceptions—A consumer perspective of corporate ethics. Journal of business research, 63(3), 2010.
  10. Khan I., & Fatma M. Understanding the Influence of CPE on Brand Image and Brand Commitment: The Mediating Role of Brand Identification. Sustainability, 15(3), 2023.
  11. Fombrun C. J., Gardberg N. A., & Sever J. M. The Reputation Quotient SM: A multi-stakeholder measure of corporate reputation. Journal of brand management, 7, 2000.
  12. Yang C. C., Tang X., Wong Y. C., & Wei C. P. Understanding online consumer review opinions with sentiment analysis using machine learning. Pacific Asia Journal of the Association for Information Systems, 2(3), 2010.
  13. Sokolov A., Mostovoy J., Ding J., & Seco L. ESG Index from Tweets and News Articles. Proceedings of the 2020 Workshop on NLP Business Applications. 2020.
  14. Briscoe-Tran H. Do employees have useful information about firms’ ESG practices? Fisher College of Business Working Paper, 2023.
  15. Jain P. K., Pamula R., & Srivastava G. A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews. Computer science review, 41(1), 2021.
  16. Wankhade M., Rao A. C. S., & Kulkarni C. A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 2022.
  17. Rantanen A., Salminen J., Ginter F., & Jansen B. J. Classifying online corporate reputation with machine learning: a study in the banking domain. Internet Research, 30(1), 2020.
  18. Agrawal S. R., & Mittal D. Optimizing customer engagement content strategy in retail and E-tail: Available on online product review videos. Journal of Retailing and Consumer Services, 67, 2022.
  19. de Kok S., Punt L., van den Puttelaar R., Ranta K., Schouten K., & Frasincar F. Review-Aggregated Aspect-Based Sentiment Analysis with Ontology Features. Progress in Artificial Intelligence, 7(4), 2018.
  20. https://doi.org/10.1007/s13748-018-0163-7
  21. Sanei A., Сheng J., Adams B. The Impacts of Sentiments and Tones in Community-Generated Issue Discussions. IEEE/ACM 13th International Workshop on Cooperative and Human Aspects of Software Engineering, CHASE, 2021. https://doi.org/10.1109/CHASE52884.2021. 00009
  22. Mirtalaie M. A., Hussain О. K. Sentiment Aggregation of Targeted Features by Capturing Their Dependencies: Making Sense from Customer Reviews. International Journal of Information Management, 53, 2020. https://doi.org/10.1016/j.ijinfomgt.2020.102097. 2020
  23. Basiri M. E., Kabiri A., Abdar M., Mashwani W. K., Yen N. Y., Hung J. C. The Effect of Aggregation Methods on Sentiment Classification in Persian Reviews. Enterprise Information Systems, 14(9–10), 2020. https://doi.org/10.1080/17517575.2019.1669829
  24. Graham J., Haidt J., & Nosek B. A. Liberals and conservatives rely on different sets of moral foundations. Journal of personality and social psychology, 96(5), 2009.
  25. Halamka R., & Teplý P. The effect of ethics on banks’ financial performance. Prague Economic Papers, 26(3), 2017.
  26. Alotaibi K. O., Mubarak I. A. S., & Alhammadi S. Perceptions of Concerned Parties about Governance and Business Ethics in Kuwaiti Banks, June, 2020.

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