Expense reimbursement internal control is a high-stakes corporate finance task that combines multimodal, visually rich evidence (e.g., receipts and approval forms), firm-specific policy text, and multi-step compliance reasoning. No existing benchmark evaluates large multimodal models (LMMs) in this domain. We introduce ReimburseBench, the first benchmark targeting full-workflow multimodal audit reasoning in corporate internal control. It comprises 151 expert-constructed reimbursement workflows, each packaging an application, receipts and supporting documents, and a firm-specific policy, evaluated by a process-level rubric grounded in twelve auditing logic points. Benchmarking nine frontier and locally deployable systems, we find the strongest attains only 72.5%, and no model exceeds 47.7% on hard workflows. Beyond this aggregate score, risk-aware, process-level evaluation surfaces three structural findings: (i) a persistent gap between binary admissibility judgment and precise quantitative computation; (ii) a systematic lenient bias in monetary predictions that poses deployment risk; and (iii) a performance pattern consistent with compositional-generalization difficulties under realistic workflow load. We provide ReimburseBench as a testbed for advancing LMMs toward trustworthy financial systems. Data and code: https://github.com/ikerzhan/ReimburseBench.

Extracting structured information from visual documents (Visual Information Extraction, VIE) is a cornerstone of business automation. While recent Multimodal Large Language Models (MLLMs) have shown promising capabilities, existing benchmarks suffer from critical limitations in scale and realism, lack semantic granularity, and fail to cover diverse document types. To bridge this gap, we introduce ReceiptBench, a large-scale, human-annotated benchmark consisting of 10k diverse receipts, organizing information extraction into four hierarchical sub-tasks: (1) Basic Perception for raw text spotting, (2) Format Normalization for strictly following standardization instructions, (3) Semantic Reasoning for inferring implicit attributes from context, and (4) Structure Parsing for handling nested line items. Furthermore, we propose a two-stage training framework incorporating Metric-Aware Group Relative Policy Optimization (GRPO), which translates rigorous evaluation constraints into reinforcement learning signals to enhance structural consistency. Extensive experiments demonstrate that our method yields state-of-the-art performance, surpassing leading proprietary models on complex reasoning tasks. We release our datasets and code at https://github.com/wwwT0ri/ReceiptBench.