Study of the prevention and detection of financial fraud through machine learning techniques

Abstract

Many organizations are currently affected by financial fraud, becoming a current concern for the financial area of ​​any entity, since when it materializes, it directly harms the assets of any public or private company. To do this, and in response to this problem, supervised and unsupervised techniques have been implemented that use artificial intelligence for the prevention and early detection of these frauds and, thus, minimize risks in financial operations. Due to the above, the study analyzes the use of supervised techniques, their referential status through scientometric and bibliometric analysis, determining their importance for the prevention and detection of financial fraud. At the methodological level, it is a documentary, exploratory and analytical study. The results of the study indicate that supervised machine learning techniques are the most accurate when applying experiments for detection and prevention, thus achieving effectiveness results greater than 90% using algorithms such as decision trees, neural networks, Naive Bayes, Support Vector Machine, Random Forest and logistic regression, being notable in the results that the financial frauds, mostly analyzed in these studies, were falsification of financial statements, credit card fraud, fraudulent financial reports and service financial fraud.  On the other hand, it is highlighted that the subject of research is growing thanks to the fact that fraud detection is becoming necessary for organizations and, with greater relevance, for financial institutions, as they are one of the most affected by this scourge of fraud.