International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 08 | Aug 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1492 A Comparative Study to Detect Fraud Financial Statement using Data Mining and Machine Learning Algorithms Ankita Sharma 1 , Mayank Patel 2 , Manish Tiwari 3 1 M.Tech. Student(IV sem) , Dept. of Computer Science, GITS, Udaipur, Rajasthan ,India 2 Professor, Dept. of Computer Science, GITS, Udaipur, Rajasthan, India 3 Professor, Dept. of Computer Science, GITS, Udaipur, Rajasthan, India ----------------------------------------------------------------------***--------------------------------------------------------------------- Abstract — Financial frauds are increasing day by day within the world. The enterprise acknowledges the problem and is just now beginning to act. Many data mining algorithms are developed that permit one to extract relevant data from a large quantity of information like false financial statements. In recent years, machine learning has developed and received major attention in the predictive analytics in audit analysis A careful reading of the financial statements will indicate whether or not the corporate is running smoothly or is in crisis. Financial statements are records of economic flows of a business. Generally, they include balance sheets, income statements, cash flow statements, statements of retained earnings, and a few different statements. during a shell, the financial statements are the mirrors of a company‘s financial status. This paper contains the analysis of various classification techniques to detect frauds, can sometimes find ways in which to avoid such measures and compares classification techniques and produce the result based on accuracy level. Index Terms — Data mining, Fraud Detection, Financial Fraud, Financial Statements- Means clustering, Machine Learning. I. INTRODUCTION Financial statement frauds (FSF) have received wide attention from the general public, the financial community and restrictive bodies due to many high profile frauds reportable at massive companies like Enron, Lucent, and WorldCom and Satyam computers over the previous few years. Finding financial statements primarily contains elements manipulating by overstating assets, profit, or understating liabilities. Detecting management fraud using traditional audit procedures could be a tough task. First, there's a shortage of knowledge regarding the characteristics of management fraud. Second, most auditors lack the expertise necessary to find it. Finally, financial managers and accountants are deliberately trying to deceive the auditors for such managers, who perceive the restrictions of an audit, standard auditing procedures could also be limited. These limitations recommend the requirement for extra analytical procedures for the effective detection of false financial statements. Statistics and data mining strategies are applied with success to find activities like money laundering, e-commerce master card fraud, telecommunications fraud, insurance fraud, and laptop intrusion etc. However, FSF is difficult and detecting them is tough. Individuals tend to question regarding the way to do it and the way effective they're. The main objective this paper is to provide a comprehensive review on financial fraud detection (FFD) method. Selected data-mining- based strategies that are utilized in FFD were examined. II. RELATED WORK Data mining has been applied in several aspects of financial analysis. Few areas wherever data processing techniques have already getting used include: bankruptcy prediction, master card approval, loan decision, money-laundering detection, stock analysis, etc. However, analysis associated with the utilization of data mining for detection of financial statement fraud is restricted. The most objective of this analysis is to predict the occurrence of financial statement fraud in financial as accurately as possible using intelligent techniques. There has been a restricted use of data mining techniques for detection of financial statement fraud. [1] Ravisankar applied six data mining techniques specifically Multilayer Feed Forward Neural Network (MLFF) Sensitivity (50.6%), Specificity (65.6%), Support Vector Machines (SVM) Sensitivity (45.6%), Specificity (65.6%), Genetic Programming (GP), group methodology of data Handling (GMDH), logistic Regression (LR), and Probabilistic Neural Network (PNN) to spot financial that resort to financial statement fraud on an information set obtained from 202 Chinese financial. They found Probabilistic neural network because the best techniques without feature choice. Multilayer Feed Forward Neural Network and PNN outperformed others with feature choice and with marginally equal accuracies Sensitivity (65.6%), Specificity (74.6%).