CLASSIFYING FRAUDULENT COMPANIES USING ML ALGORITHM IN PYTHON

Abstract

This paper is a case study of visiting an external audit company to explore the usefulness of machine learning algorithms for improving the quality of an audit work. Annual data of 777 firms from 14 different sectors are collected. With the appearance of tremendous growth of financial fraud cases, machine learning will play a big part in improving the quality of an audit field work in the future Purpose: The goal of the research is to help the auditors by building a classification model that can predict the fraudulent firm on the basis of the present risk factors 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). Methodology/Approach: The machine learning algorithms like Random Forest Classifier and Logistic regression are used in this project to classify the fraudulent firms.The exploratory data analysis is done using libraries of Python like matplotlib and plotly. Research Limitations: The dataset is one year non-confidential data in the year 2015 to 2016 of firms is collected from the Auditor Office of India to build a predictor for classifying suspicious firms. Value: To help the auditors by building a classification model that can predict the fraudulent firm on the basis the present and historical risk factors.