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@article{204080,
        author = {Sumit Kumar},
        title = {Algorithmic Explainability, Consumer Cognitive Bias, and Trust Calibration in AI-Driven Financial Technology Platforms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {1162-1170},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204080},
        abstract = {AI in FinTech platforms is growing rapidly which is completely changing how financial services are offered in India. Algorithms work in a black box and this leads to consumer trust and cognitive bias issues.  The objective of this research is to look into the algorithmic explainability, cognitive bias of consumers and trust calibration of AI driven FinTech companies in Delhi NCR. A structured questionnaire was used to conduct the quantitative methodology for 412 respondents’ primary data collection. In this research framework, we have adapted the Technology Acceptance Model (TAM), Trust and Cognitive Bias Theory. The researchers tested their hypotheses by using SEM technique The results indicate that algorithmic explainability significantly affects consumer trust (beta = 0.423, p < 0.001).  It was found that consumer cognitive bias awareness partially mediates the above relation. Digital literacy is the adapter. The model has acceptable fit indices (CFI = 0.968, TLI = 0.961, RMSEA = 0.054).
This report provides useful information for fintechs and policymakers about AI governance, which is helpful in taking actions.},
        keywords = {Algorithmic Explainability, Consumer Trust, Cognitive Bias, FinTech, Artificial Intelligence, Trust Calibration, Digital Literacy, SEM, India},
        month = {June},
        }