FinanSE 2025

Software development has an integral role in every financial organisation; indeed, almost every service provided by a bank utilizes some form of software solution. While AI for SE research has led to solutions and innovations for many popular SE problems, there remain unresolved challenges, particularly, those challenges faced in software development in financial firms. An example of such a challenge is defect prediction, where defects are not equal as some may lead to larger reputational and financial damage than others. Consequently, testing and verification are burdened with further restraints for finance-based SE teams. Financial firms began automating processes as early as the 1960s, and as such, must maintain large legacy systems which may host critical operations. This problem is further exacerbated by the numerous mergers and acquisitions common in the financial sector, which leaves firms with a set of heterogeneous legacy systems that need to communicate with one another effectively and efficiently. Therefore, maintaining these systems while modernizing them leads to intriguing challenges, spanning from model extraction and process optimisation to code translation. Moreover, highly regulated institutions like financial firms require a high degree of transparency and accountability. These requirements facilitate the need for model fairness and explainability for any SE solution, in particular those that rely on AI.

FinanSE Workshop provides the SE community in both industry and academia with a unique opportunity to discuss such challenges and work in collaboration towards effective solutions.

Call for Papers

FinanSE’25 welcomes research papers presenting:

• Original and innovative ideas to resolve software engineering challenges in financial firms leveraging AI power
• Open research problems to pursue
• Experience reports, case studies, and tool demonstrations

We invite submissions at the intersection of financial services and software engineering. Topics of interest include but are not limited to:

• AI/ML for Software system monitoring and analytics
• Software reliability, automated testing, and high-value defects prediction
• Software modernisation and cloud migration
• Code translation from ``low resource'' programming languages
• Programming without coding (natural language to software)
• Search-based software engineering and optimisation
• Model extraction for legacy software systems in low data and highly regulated environments
• Model explainability and fairness
• Human aspects of AI for Software Engineering in a financial market