How is artificial intelligence used in fraud detection?

How can artificial intelligence help detect fraud?

Artificial Intelligence can play a crucial role in fraud management by detecting and preventing fraudulent activities.

The global average rate of losses caused by fraud for the last two decades represents 6.05% of the gross domestic product. Additionally, companies have reported that cyber breaches have caused financial damages equaling 3% to 10% of their revenue. Moreover, global digital fraud losses are projected to exceed $343 billion between 2023 and 2027.

Given the estimated amounts, it is a crucial question for any organization to build up an efficient fraud management system. Fraud management is identifying, preventing, detecting and responding to fraudulent activities within an organization.

Artificial intelligence (AI) has a significant role in fraud management. AI technologies, such as machine learning (ML) algorithms, can analyze large amounts of data and detect patterns and anomalies that may indicate fraudulent activities. AI-powered fraud management systems can identify and prevent various types of fraud, such as payment fraud, identity theft or phishing attacks. They can also adapt and learn from new fraud patterns and trends, improving their detection over time.

AI-based solutions can also integrate with other security systems, such as identity verification and biometric authentication, to provide a more comprehensive approach to fraud prevention.

How can machine learning algorithms help in fraud detection and prevention?

Machine learning algorithms are designed to recognize patterns based on a large amount of data, which can be leveraged to identify fraudulent activities.

AI refers to technologies that can perform tasks requiring human intelligence, such as analyzing data or understanding and responding to human language. They are designed to recognize patterns and make predictions in real time. AI algorithms are often a combination of different ML models.

ML is a subset of AI; it uses algorithms to analyze large amounts of data to enable systems to learn autonomously. The more data ML algorithms are exposed to, the better they perform over time. The two main approaches of ML are supervised machine learning…