<![CDATA[This study critically reviews the relationship between consumer behaviour and credit supply decisions, focusing on evidence from an Australian FinTech lender. FinTech lenders increasingly use alternative data, such as bank transaction patterns, to assess creditworthiness beyond traditional metrics. The reviewed literature indicates that specific behaviours, notably gambling and intensive cash usage, negatively impact the offered loan amounts. While lenders generally favour mature demographics, these preferences are moderated by risky consumption signals. However, this critical review identifies several limitations in current research, including observational designs that prevent causal claims and potential data selection bias. Furthermore, the modest economic impact of these behavioural variables raises questions about their incremental predictive value. Crucially, penalizing cash usage and repeated borrowing may inadvertently foster algorithmic bias, disproportionately disadvantage vulnerable populations, and conflict with broader financial inclusion objectives. Future research must prioritize causal identification, default analysis, and algorithmic fairness. Ultimately, the integration of alternative data requires robust regulatory frameworks to balance technological innovation with consumer protection.]]>
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