Generative AI for Banks: Benchmarks and Algorithms for Synthetic Financial Transaction Data

The banking sector, as a data-driven industry, relies on the availability of high-quality data to create value and protect its customers. The synergy between recent deep learning (DL) advancements, and the sector's data needs presents a growth potential of USD$4.6 trillion by 2035 (Accenture, 2017). However, deploying DL models is challenging due to the need for large, high-quality training data (Ryll et al., 2020), a difficulty made worse by the intricacy of financial transaction data (with complex data patterns and time-related characteristics), and strict regulations that limit data sharing (EU Regulation 2016/679, PCI DSS v4.0). One possible solution is to use synthetic data which is artificially generated rather than drawn from real-world events to increase samples in the minority class (Jordon et al., 2022), and allow safe data sharing between financial institutions while protecting privacy (Karst et al., 2024). This approach is essential for improving models used in assessing risks and detecting fraud.