This study examines whether payment system data can be useful for tracking economic activity in Azerbaijan. We utilise the transactional payment system data at the sectoral level and employ a Dynamic Factor Model (DFM) and Machine Learning (ML) techniques to nowcast quarterover- quarter and year-over-year nominal gross domestic product. We compared the nowcasting performance of these models against the benchmark model in terms of the out-of-sample root mean square error at three different horizons during the quarter. The results suggest that ML and DFM models have higher predictability than the benchmark model and can significantly lower nowcast errors. Although our payment time series is still too short to obtain statistically robust results, the findings indicate that variables at a higher frequency in such data can be helpful in assessing the current state of the economy and have the potential to provide a faster estimate of the economic activity.