Trade finance fraud, particularly duplicate trade financing, is a growing concern for financial institutions, exacerbated by the post-COVID-19 supply chain demands and retreat of major banks from trade funding due to fraud risks. High-profile cases, like the $9 billion scandal involving Hin Leong Trading, highlight the vulnerabilities in trade finance, where perpetrators secure multiple financings for the same transaction due to antiquated processes, lack of standardization, and limited technology use for fraud prevention. Privacy regulations and competitive concerns pose challenges for cross-border data sharing needed to prevent such fraud, but advancements in modern IT, data science, and cryptography offer promising solutions. Technologies like Privacy Enhancing Technologies (PETs), federated learning, and homomorphic encryption can help financial institutions share data while preserving privacy and compliance, ultimately enabling them to adopt a strategic, collaborative approach to fraud prevention. By leveraging AI and data modeling, financial institutions can enhance their predictive capabilities and support decision-making to mitigate risks while contributing to the evolution of financial standards for privacy-preserving data collaboration.