Process mining is an advanced technological field offering transformative solutions for data-driven process analysis, and in this context, digital forensics for databases emerges as an increasingly important domain benefiting from process mining techniques in modern digital forensic investigations. However, detecting suspicious transactions remains a challenging task due to the complexity and scale of financial processes, as well as the adaptive nature of fraudulent behaviors. Traditionally, database examinations have relied on manual reviews and specific queries, which are time-consuming and may overlook hidden patterns. Existing methods in the literature often struggle with scalability, lack of automation, and limited ability to capture process deviations effectively. To the best of our knowledge, no systematic method of advanced data analytics combining process mining principles exists in this field. This work presents a novel approach that integrates multiple process mining techniques, including process discovery and conformance checking, to model business processes and systematically identify deviations associated with suspicious activities. The experimental results depict the ability of the proposed approach to detect the suspicious transactions and fraudulent activities.