Rapid proliferation of digital banking has led to faster, greater and more complex transactions. While digital channels have made banking easier and efficient, they have at the same time provided new avenues for financial crime like money laundering. Traditional AML systems usually follow pre-defined rules, threshold-based transaction analysis and manual reviews. These systems may result in too many alarms, false alarms and inability to detect new patterns of transactions. In such an environment, AI, machine learning, automation of customer due diligence, real-time alarm generation, customer risk scoring and integrated analytics platforms are becoming increasingly significant aspects of AML compliance. In this current study, the influence of AML technology on transaction-monitoring effectiveness is assessed for selected banking organizations in the state of Rajasthan. For this research, a descriptive, analytical, correlational and cross-sectional research design was adopted. The main data were collected through the structured questionnaire survey, where 298 respondents in total number were selected from public, private and cooperative banking institutions. The subjects participating in the survey were AML and compliance officers, auditors, risk managers, forensic accountants and other finance control personnel. The questionnaire survey was conducted on the basis of five-point Likert scale, and the quantitative data was analyzed through the SPSS. The reliability of the instrument was established based on internal consistency, where Cronbach’s Alpha was 0.910 for 40 items. From the regression analysis, a highly significant and positive correlation was found between AML technology adoption and transaction monitoring efficiency. R = 0.837, R² = 0.701, adjusted R² = 0.700, and F(1,296) = 694.165 were observed where p < 0.001. Thus, AML technology adoption accounted for 70.1 per cent of the variation in transaction monitoring efficiency. This study finds that Artificial Intelligence (AI) enabled monitoring, automated reporting, real-time surveillance, risk scoring, and data platforms are useful in making AML more efficient. However, the success of such tools requires good quality data, capability of employees, model validation, integration, and proper human control.