A Study on Trust and Acceptance in AI-Based Financial Fraud Detection

Author

Y.Kanaka Durga, Swarna Lakshmi Pratheeka

Abstract

 Payment, bank and financial institutions are increasingly adopting AI to detect fraudulent transactions, while non-transparent decision processes can prove to be a hindrance to their acceptance. This exploratory research investigates respondents' perceptions regarding four constructs namely, the significance of the trustworthy-AI principles (TRU), the relation of explainability to trust (EXP) and effectiveness of AI based detection (EFF), and utility of and acceptance of explanations (ACC). A structured questionnaire based on the five-point Likert scale was filled in by 21 respondents from a sample of 132 individuals out of a target population of 197. The quantitative data was statistically analysed in Excel with descriptive statistics, Cranach's alpha, chi-square, one-way ANOVA and linear regression analysis. Respondents perceived all the four constructs with an average (on a 5 point scale) of 3.87-4.13 indicating agreement. Further, their levels of agreement did not significantly differ according to the gender and age groups, in chi-square analysis (all Fisher's exact p >.25), and in one-way ANOVA analysis (all p >.41). In multiple regression analysis, the three predictors explained 72.3% of the variance in the acceptance of explanations (F(3, 17) = 14.76, p <.001), and the trustworthy-AI principles was the only significant unique predictor (b = 0.48, p =.021). The explainability-trust was the significant predictor, without adjusting the other predictor variables, (b = 0.81, R =.587). Due to the small sample size and purposive sampling, these results should be regarded as preliminary, indicating that this may be a pilot for a much larger study.

Keywords

Trustworthy AI; Explainable AI; Financial Fraud Detection; User Trust; Technology Acceptance; Likert Survey.

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