DIGITAL ECONOMY, PROXIMITY AND REGIONAL INNOVATION CAPABILITY——EMPIRICAL RESEARCH BASED ON THE PERSPECTIVE OF COLLABORATIVE INNOVATION

Abstract

Background and Purpose: With fraud losses expected to reach USD 41 billion by 2027, the swift growth of digital finance has increased the dangers associated with international payment networks. According to reports, 72% of financial institutions have seen an increase in attempts at fraud. This study combines financial analytics, cybersecurity, and behavioral economics to create a real-time, adaptive fraud prevention system since it acknowledges that no single technology can eliminate these risks. By addressing both systemic and human vulnerabilities, the goal is to increase the resilience of the financial system. Methods: Behavioral nudges, adaptive AI algorithms, and real-time transaction analysis were all combined to create a hybrid system. Key cognitive biases that make people vulnerable to fraud were uncovered by the study, including overconfidence, loss aversion, hyperbolic discounting, and the familiarity heuristic. More than 10,000 anonymous bank transactions from the US, Japan, and India were used to test a prototype. To fortify defenses, the architecture integrated situational threat intelligence and Zero Trust security model concepts. Results: Compared to previous models, the method increased the accuracy of fraud detection by 27% and decreased false alarms by 18%. Adaptive security techniques combined with behavioral insights greatly decreased algorithmic and human error. Early risk identification and user engagement were improved by its human-centered design, which included individualized learning prompts, decision aids, and real-time notifications. Conclusion: The results highlight how behavioral economics and advanced analytics can be combined to improve cybersecurity and digital banking. Financial institutions are better equipped to manage changing digital risks thanks to this cross-disciplinary, data-driven approach. With useful ramifications for legislators, cybersecurity professionals, and financial institutions alike, the study emphasizes the significance of combining human behavioral aspects, adaptive machine learning, and Zero Trust security principles to combat payment fraud.