Awareness of Machine Learning for FinTech App Engagement: A Study of User Trust and Perception
Diwakar H G
Background: The rapid embedding of machine learning (ML) within FinTech applications — powering investment recommendations, copy-trading signals, and automated savings nudges — has outpaced users’ recognition of when and how these algorithms operate. Objective: This study measures the level of ML awareness, understanding, and trust among FinTech app users and identifies the demographic and usage-based factors that shape user engagement with ML-powered financial features. Methodology: A descriptive, quantitative research design was adopted. Primary data were collected from 101 FinTech app users through a structured Google Form questionnaire comprising demographic items and nine Likert-scale statements. Data were analysed using Python (pandas, SciPy, scikit-learn) through descriptive statistics, independent-samples t-tests, one-way ANOVA, Pearson correlation, Markov Chain modelling of trust-state transitions, and linear-programming-based prescriptive optimisation.