This study aimed to explore the relationship between banking system health and money laundering in Iran’s economy. Key performance indicators such as Return on Assets (ROA) and liquidity were analyzed to assess their impact on money laundering risk.

Findings revealed a negative and asymmetric relationship between ROA and money laundering. All correlation coefficients—Kendall’s τ, Spearman’s ρ, and Pearson’s r—were negative and statistically significant (τ = –0.1333, ρ = –0.2529, r = –0.1013; p < 0.05). This suggests that banks with higher ROA are less likely to be involved in illicit financial activities due to stronger internal controls and transparency.

In contrast, liquidity showed no significant relationship with money laundering. Although the correlation coefficients were negative, they were statistically insignificant (p > 0.5), indicating that liquidity alone is not a reliable deterrent against financial crime.

Among the 25 copula models tested, the Raftery model provided the best fit for the ROA–money laundering relationship (RMSE = 0.0924, NSE = 0.9874). For the liquidity–money laundering relationship, the BB1 model performed best (RMSE = 0.0846, NSE = 0.9882), although the relationship itself was not statistically meaningful.

Overall, the study highlights that improving banking performance—especially through increased ROA and stronger internal oversight—can serve as an effective shield against money laundering. These insights offer practical guidance for policymakers to enhance financial integrity and reduce economic crime.