Subject: Ethics, machine learning, explainability, finance, fintech, financial services
Year: 2022
Type: Journal Article
Title: Ethically Responsible Machine Learning in Fintech
Author: Rizinski, Maryan
Author: Peshov, Hristijan
Author: Mishev, Kostadin
Author: Chitkushev, Ljubomir
Author: Vodenska, Irena
Author: Trajanov, Dimitar
Abstract: Rapid technological developments in the last decade have contributed to using machine learning (ML) in various economic sectors. Financial institutions have embraced technology and have applied ML algorithms in trading, portfolio management, and investment advising. Large-scale automation capabilities and cost savings make the ML algorithms attractive for personal and corporate finance applications. Using ML applications in finance raises ethical issues that need to be carefully examined. We engage a group of experts in finance and ethics to evaluate the relationship between ethical principles of finance and ML. The paper compares the experts’ findings with the results obtained using natural language processing (NLP) transformer models, given their ability to capture the semantic text similarity. The results reveal that the finance principles of integrity and fairness have the most significant relationships with ML ethics. The study includes a use case with SHapley Additive exPlanations (SHAP) and Microsoft Responsible AI Widgets explainability tools for error analysis and visualization of ML models. It analyzes credit card approval data and demonstrates that the explainability tools can address ethical issues in fintech, and improve transparency, thereby increasing the overall trustworthiness of ML models. The results show that both humans and machines could err in approving credit card requests despite using their best judgment based on the available information. Hence, human-machine collaboration could contribute to improved decision-making in finance. We propose a conceptual framework for addressing ethical challenges in fintech such as bias, discrimination, differential pricing, conflict of interest, and data protection.
Publisher: IEEE
Relation: IEEE Access
Identifier: oai:repository.ukim.mk:20.500.12188/27905
Identifier: http://hdl.handle.net/20.500.12188/27905
