Researchers have developed a benchmark called FinRCA-Bench to evaluate the performance of artificial intelligence systems in financial evidence retrieval and reasoning. The benchmark consists of 2,250 synthetic cases of accounts-payable-to-bank reconciliation, including 1,500 injected failures and 750 legitimate or hard-negative cases. The evaluation focuses on the ability of different AI models to retrieve relevant evidence independently of their answer correctness. The results show that the performance of these models is heavily influenced by their retrieval architecture, with some models performing significantly better than others in retrieving correct evidence. --- Why it matters: This matters because it highlights the importance of retrieval architecture in financial AI systems. Engineers and researchers need to consider how their models retrieve evidence in order to improve their overall accuracy and reliability. Source: https://arxiv.org/abs/2608.18534