The field of financial security and interpersonal dynamics is rapidly evolving, with a focus on developing innovative methods for detecting anomalies and predicting behavior. Researchers are exploring the use of machine learning and data-driven approaches to identify potential security threats and improve teamwork dynamics. A key trend in this area is the integration of contextual information and external distractions to enhance attention awareness and predict user behavior. Additionally, there is a growing interest in using temporal network effects to analyze and predict employee turnover in financial markets. Noteworthy papers in this area include: Detecting Rug Pulls in Decentralized Exchanges, which presents a machine learning framework for early detection of rug pull scams on decentralized exchanges. HyPV-LEAD, which introduces a data-driven early-warning framework for detecting cryptocurrency anomalies. AttenTrack, which proposes a lightweight attention awareness model that relies solely on non-privacy-sensitive objective data available on mobile devices. Network Contagion in Financial Labor Markets, which analyzes the role of professional networks in shaping career moves and predicting employee departures. Teamwork as Linear Interpersonal Dynamics, which proposes a psychologically meaningful representation of interpersonal dynamics using a context matrix.