Context Audit Risk Dataset for classifying Fraudulent Firms Content The goal of the dataset is to help the auditors by building a classification model that can predict the fraudulent firm on the basis the present and historical risk factors. The information about the sectors and the counts of firms are listed respectively as Irrigation (114), Public Health (77), Buildings and Roads (82), Forest (70), Corporate (47), Animal Husbandry (95), Communication (1), Electrical (4), Land (5), Science and Technology (3), Tourism (1), Fisheries (41), Industries (37), Agriculture (200). This research work is a case study of an external government audit company which is also the external auditor of government firms of India. During audit-planning, auditors examine the business of different government offices but the target to visit the offices with very-high likelihood and significance of misstatements. This is calculated by assessing the risk relevant to the financial reporting goals (Houston, Peters, and Pratt 1999). The three main objective of the study are as follow: 1. To understand the audit risk analysis work-flow of the company by in-depth interview with the audit employees, and to propose a decision-making framework for risk assessment of firms during audit planning. 2. To examine the present and historical risk factors for determining the Risk Audit Score for 777 target firms, to implement the Particle Swarm Optimization (PSO) algorithm to rank examined risk factors, and evaluating the Risk Audit Class (Fraud and No-Fraud) of nominated firms. 3. To examine the present and historical risk factors for determining the Risk Audit Score for 777 target firms, to implement the Particle Swarm Optimization (PSO) algorithm to rank examined risk factors, and evaluating the Risk Audit Class (Fraud and No-Fraud) of nominated firms.