Ethically Responsible Machine Learning in Fintech
Journal
IEEE Access
Date Issued
2022-08-29
Author(s)
Rizinski, Maryan
Peshov, Hristijan
Chitkushev, Ljubomir
Vodenska, Irena
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.
(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.
Subjects
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