How is AI Used in Fraud Detection?
Artificial intelligence refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect.
Using AI to detect fraud has aided businesses in improving internal security and simplifying corporate operations. Artificial Intelligence has therefore emerged as a significant tool for avoiding financial crimes due to its increased efficiency.
AI programs to Detect Fraud
AI can be used to analyze huge numbers of transactions in order to uncover fraud trends, which can subsequently be used to detect fraud in real-time. Some examples in which AI programs can be used to detect fraud are:
Email Phishing
Phishing detection is tackled as a supervised Machine Learning problem that involves collecting a number of falsified emails with fake URLs and an equal number of legit emails and websites from the original sources in order to train the model.
Credit Card Fraud
Fraud detection software for credit cards employs anomaly detection by tracking someone’s spending habits and patterns and establishing a range of value and instance in which someone typically spends. If a statement falls out of that range, this software will identify it as possible fraud and request verification from the cardholder to dispute the suspicion.
Bank Fraud
Fraud detection software for banks consists of developing risk profiles for bank customers and rating them on granular data to decide whether or not a client is trustworthy enough to loan money to.
Identity theft
Identity theft detection is considered an anomaly detection challenge so various unsupervised Machine Learning algorithms are used to help find abnormal patterns of a user’s behavior in order to detect unauthorized actions.
ID document forgery
ID document forgery detection deals with image processing where certain techniques are used to make sense of the visual information that an image carries. These programs analyze and break down certain aspects of the document whilst comparing it to an extensive amount of both legitimate and illegitimate documents.
Fake account identification
Account identification programs start by selecting the profile that needs to be classified as fake where they identify features of the account and establish those as parameters of legitimacy. They look at the rate of engagement, activity, number of followers compared to the number of people the account is following, and the relevancy of comments to help identify if an account is a real person or not.
The threat to the banking and retail sectors will remain as long as the world is overrun with digital payments. Email phishing, money fraud, identity theft, document forgery, and phony accounts all play a part in the increasing volume of criminal attacks that expose the data of unprotected users. New cutting-edge techniques based on Machine Learning algorithms for fraud detection and prevention are offering a greater value to businesses with their real-time work, speed, and efficiency as outdated rule-based algorithms for fraud detection become obsolete.
