The banking and stock exchange industries suffer from insider threat detection problems, which are being exacerbated by the rapid digitization of financial infrastructure. Traditional security measures would become increasingly ineffective when dealing with sophisticated internal actors that have legitimate access credentials. An advanced ML detection framework called Threat Sense is introduced here, which distinctively relies on and integrates behavioral analytics, temporal pattern recognition, and digital forensics capabilities for the protection of enterprise financial systems. The framework employs multivariate temporal sequence mining algorithms for detecting suspicious behavioral patterns with a very high accuracy rate in different transaction volumes and user activity patterns to make sure the firms are dealing with secure transactions. The trials for its application in three totally different financial institutions confirmed that the optimal operation is within adjusted data environments, and the detection power is directly proportional to the quality of data and accuracy of behavioral baseline measurements. The system provides continuous monitoring; it generates actionable security alerts while the forensic evidence’s chain-of-custody is meticulously preserved for later use in legal proceedings. In contrast to traditional detection systems, ThreatSense was able to detect the unauthorized access of financial data, covert transaction modifications, and the leakage of data at a high grade during the penetration testing exercises.