Automated fraud detection (AFD) refers to the use of artificial intelligence and machine learning techniques to identify suspicious patterns and behaviour in financial transactions and other types of commercial activities in order to detect and prevent fraud.
Automatic fraud detection systems use machine learning algorithms to analyse large amounts of data and look for patterns that may indicate the presence of fraud. These patterns may include unusual or suspicious transactions, atypical spending patterns or other types of fraudulent activity.
Some common techniques used in automated fraud detection include anomaly detection, network analysis, predictive modelling and data mining. Automated fraud detection systems may also include the ability to take preventive measures, such as cancelling suspicious transactions or issuing automatic alerts to users.
Automated fraud detection is an important technique in the fight against fraud and identity theft in the financial industry and other sectors that handle large amounts of financial transactions and personal data. Automatic fraud detection systems can help prevent financial losses and protect customers against fraudulent activities.
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