This study aims to quantify the scale/incidence of cyber-financial fraud and examine critical evolutionary patterns that shape its trajectory across Indian states.
This study uses regression analysis on state-level panel data (2003–2004 to 2022–2023) to examine how the financial magnitude across major fraud categories influences cyber-financial fraud incidence. A similar approach is applied to four structured sub-periods to analyse these relationships and identify significant temporal shifts in the predictors. The use of Panel-Corrected Standard Errors (PCSE) for the full period, and a methodological shift from Pooled Ordinary Least Squares-robust standard errors to PCSE in the sub-period analyses, ensures methodological rigour and robustness.
The results reveal a significant, positive and differentiated financial impact of all fraud categories on overall cyber-financial fraud incidence. The amount involved in credit card-related frauds exerts the strongest influence, followed by internet banking and ATM/debit card frauds. Sub-period analyses confirm temporal variations in both the relative influence and statistical significance of these categories, underscoring evolving patterns in the predictors of cyber-financial fraud incidence.
The findings may inform policymakers in enhancing institutional responses, directing state-level interventions and formulating targeted public awareness campaigns to mitigate both the incidence and financial impact of such frauds.
The study provides novel perspectives into key drivers and their evolving dynamics in shaping cyber-financial fraud incidence, using a systematic methodology through full-period and sub-period analyses.
