Human AI Collaboration in Financial Risk Management for Railway Resilience

Author

Bhukya Ganesh Naik, Dr. Ajay Kumar Varshney


Abstract

 The railway industry supports passenger movement, freight transport, employment and trade but its finances are vulnerable to demand shocks, rising costs and disruptions such as the COVID-19 pandemic. Artificial intelligence (AI) can improve forecasting, anomaly detection and risk monitoring, but full automation may neglect operational experience, context and human accountability. This study examines a human-AI financial risk management framework that links AI capability, explainability, trust, human oversight, human-AI collaboration, decision quality and financial resilience. A structured Likert questionnaire dataset of 120 respondents (railway finance, management, IT and operations roles) was analysed with descriptive statistics, Cronbach’s alpha, chi-square tests of independence, one-way ANOVA with post-hoc tests and simple and multiple linear regression. Chi-square tests revealed significant associations between respondent role and organisational AI use (χ²(6) = 26.60, p < .001, V = .33), AI familiarity and organisational AI use (χ²(6) = 51.05, p < .001, V = .46), sector and preferred AI application (χ²(6) = 78.78, p < .001, V = .57) and role and preferred responsibility model (χ²(4) = 21.15, p < .001, V = .30). ANOVA showed significant differences in trust by AI familiarity (F(3,116) = 20.91, η² = .35), AI-capability perception by role (η² = .24), resilience importance by sector (η² = .21) and perceived collaboration benefit by experience (η² = .18). All six proposed hypotheses were supported by simple regression, and human oversight and trust predicted decision quality in multiple regression (R² = .40) while decision quality and training predicted resilience importance (R² = .22). Notably 97 of 120 records in the dataset are labelled as synthetic, so the results are exploratory and illustrative and should not be presented as evidence from a real survey of railway professionals.


Keywords

Human-AI collaboration, financial risk management, railway resilience, artificial intelligence, chi-square test, ANOVA, regression, Indian Railways.



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References


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