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AI Series: The introduction of AI in fraud: How Artificial Intelligence is Revolutionizing the Monitoring and Prevention of Fraud in Payment Transactions


In our series of articles on the impact of artificial intelligence, we had not yet touched on the subject of fraud. Although, if we have mentioned it briefly, in this article we proceed to analyze in detail the impact of AI on fraud.

The rapid advancement of technology is transforming the way we carry out financial transactions, thanks to the ease and availability of making payments from any device, mobile, watch, tablet or computer today. With the increase in digital payments and online commerce, guaranteeing the security of these transactions has become essential, and critical, since millions of transactions are processed every second that were previously carried out on manually. Digital fraudsters are constantly evolving their tactics and technologies, making traditional fraud prevention methods less and less effective. With Artificial Intelligence (AI), there has been a complete change in the field of monitoring and preventing financial fraud. In this article, we will explore the significant impact of AI on fraud, facilitating the prevention and detection of fraudulent activity across different payment methods, including account-to-account payments, online payments, card payments, and business payments. The irruption of AI in technological fraud will cause a before and after in the process of fraud prevention and monitoring.

Account to Account Payments

Account-to-account payments, commonly known as direct bank account-to-bank account transactions, whether person-to-person (P2P) or business-to-business (B2B), have gained enormous popularity due to their convenience and speed. However, they are not immune to fraud attempts. This technology has emerged as a powerful tool to combat such fraudulent activities in this payment arena and introduce AI into fraud as well.


  1. Behavioral Analysis: AI fraud systems can analyze individual transaction patterns, including frequency, time, amount, and geographic location. By learning and subsequently understanding normal and typical user behavior, fraud AI can detect any anomalies that may indicate fraudulent activity and thus prevent any fraudulent attack and take appropriate action by alerting or blocking transactions.
  2. Real-Time Transaction Monitoring: Connected and cloud-based AI-powered fraud monitoring systems can process transactions in real-time, enabling immediate action against suspicious activity. This reduces the window of opportunity for fraudsters and helps prevent fraudulent transactions. The advantages of AI in fraud are innumerable, and the monitoring of hundreds of millions of transactions in real time is becoming possible thanks to this technology.
  3. Adaptive Learning: AI algorithms can continuously learn from historical data, making them capable of adapting to new fraud patterns. As fraud tactics evolve, AI can stay one step ahead, ensuring better protection for account-to-account payments.

Online payments

In recent years, the rise of e-commerce has revolutionized the way consumers shop and interact with brands and service companies. Online payments have become a fundamental part of modern transactions, but they are also susceptible to various fraud schemes. The impact of AI in monitoring and preventing fraud in online payments is profound.


  1. Machine Learning for Fraud Detection: AI-powered machine learning models can analyze vast amounts of transactional data and identify patterns associated with fraudulent transactions. This allows payment processors to block suspicious transactions, protecting consumer funds.
  2. Biometric Authentication: AI is enabling the implementation of biometric authentication methods, such as fingerprint and facial recognition. These technologies add an additional layer of security to online transactions, reducing the likelihood of fraudulent activity.
  3. Contextual analysis: AI can analyze data from text, social networks and other platforms to assess the objective of a customer when making transactions and in stores. This information helps identify potential scammers or fraudulent websites, improving the overall security of online payments.

Card Payments

Card payments, whether credit or debit, have been a popular method of transaction for decades. However, they are still a prime target for tech scammers or hackers. AI in fraud is making significant strides in the card payment fraud monitoring and prevention revolution.


  1. Fraud Pattern Recognition: AI algorithms can detect fraud patterns across multiple transactions, cards, and merchants. This makes it possible to identify coordinated attacks and take preventive measures in time.
  2. Continuous Authentication: AI enables continuous authentication during card transactions, analyzing various parameters such as device information and user behavior to ensure the legitimacy of the transaction.
  3. Natural Language Processing (NLP): AI-powered systems in fraud can analyze text-based data, such as emails and chat communications, to identify potential indicators of fraud or phishing attempts targeting cardholders.

Business Payments

Business payments involve higher transaction volumes and larger amounts of money, compared to consumer or P2P payments, making them an attractive target for more sophisticated fraud attempts. The introduction of AI in fraud is transforming the way companies protect themselves against financial loss due to fraudulent activity affecting transactions.


  1. Anomaly Detection: The integration of AI into fraud can detect unusual financial patterns in business transactions, such as sudden increases in payments or changes in supplier behavior, which could indicate fraudulent activity.
  2. Supplier Risk Assessment: AI can assess the risk associated with different suppliers by analyzing data related to their financial health, reputation and historical transactions. This information helps companies make informed decisions and reduce fraud risks.
  3. Invoice Fraud Prevention: AI-powered systems can examine invoices for potential indications of fraud, such as altered bank information or false invoices, reducing the likelihood of businesses falling victim to invoice scams.


The introduction of Artificial Intelligence or AI in fraud is significantly revolutionizing the field of fraud monitoring and prevention in banking and payment transactions. Its ability to process endless streams of data in real time, detect links, trends and patterns, and adapt to new fraud techniques makes AI an invaluable tool in the fight against fraud activity. Whether in account-to-account payments, online payments, card payments or business payments, AI-powered solutions are strengthening security measures, protecting consumers, businesses and financial institutions from the ever-evolving threat of fraud.

Snab: Efficiency & control in decision making

In today’s world of business management, where efficiency and data-driven decision-making are critical to success, having tools that simplify and streamline financial processes is essential. In this sense, Snab offers a comprehensive platform that can be a strategic ally to optimize and monitor treasury management in real time and thus improve decisions in the financial area. Soon, artificial intelligence will have a big impact on such services and it is possible that platforms like Snab will allow or integrate AI to offer more personalized services and personalized predictions.

Currently with Snab, companies can centralize their data, banking and treasury in a single digital platform. The automation to receive, approve and pay invoices reduces errors and time, improving efficiency and control in liquidity management. Thus, more agile and well-founded financial decisions are made, essential when evaluating financial leverage.

In addition, Snab offers real-time visibility of cash flows and their forecasts and synchronization with the ERP to access up-to-date information. This allows, once again, to make more informed and strategic decisions.

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