artificial intelligence bank fraud

AI is used by 75% of acquiring banks to prevent fraudulent card transactions

January 21, 2022

Can artificial intelligence be the solution to bank-related fraud? The increasing interest in AI technology in the banking sector continues growing. It is estimated that around 75 percent of acquiring banks believe that revolutionary AI solutions can help detect card-related fraud easily.

The significant advantages of AI technology are built over time. This is because algorithms keep collecting data while learning more about using the same. Acquiring banks remain under immense pressure to ensure efficient processing of transactions during the detection and prevention of fraud-related attempts. There is an increasing challenge due to the current rise in fraud attacks and payment volumes. To manage the ongoing trends, most banking institutions seek help from innovative AI or Artificial Intelligence technology.

AI-based Fraud Detection for Card Transactions

Card-specific transactional fraud is continually on the rise. However, most financial organizations believe that AI can be a strong preventative measure.

Cybercrime is one of the costliest threats to the entire banking industry and its consumers. It is estimated that such crimes cost around $600 billion every year across the United States of America. Online card transaction fraud has the biggest share slice, costing over $200 billion. Therefore, financial organizations such as banks and credit unions are highly vulnerable to scams and fraud. As the concept of online banking and banking apps continues growing, efforts are expected to detect and prevent fraud.

Machine learning and artificial intelligence are being used as relevant solutions for detecting transaction fraud before it happens. This helps in protecting customers from fraud-related effects and helps in eliminating or reducing friction for customers whose card transactions might be wrongly flagged.

AI-based Fraud Detection for Banking Applications

Simple banking applications, including credit cards, payday loans, and opening a deposit account, only require some basic information. However, this fact alone makes it easier to perpetrate fraudulent activities. If a hacker can obtain information like a social security number, they can easily submit fraudulent applications.

Research reveals that the banking industry is the worst hit as far as occupational fraud is concerned—occupational fraud represents around 17 percent of overall fraud cases. At the same time, credit card fraud and identity theft are becoming increasingly common with the continuing growth of online banking.

AI can help combat and defeat application fraud by detecting illicit activities early in the process. For example, algorithms continue looking for connections between loan applications and credit cards. Algorithms also monitor newly opened accounts to prevent financial damage before its occurrence.

Acquiring Banks Using AI to Prevent Fraud

A new report emphasizing AI was conducted as a collaborative survey of 104 executives at acquiring banks. The report results found that around 37 percent of acquirers reveal that payment volumes increased significantly in 2021. In addition, 93 percent of acquirers also revealed that the total ratio of fraudulent transactions remains a significant share of the total number of transactions in the past year.

Acquirers aim to widely adopt AI technology and other related technologies to manage their respective businesses safely. For example, around 75 percent of acquirers use artificial intelligence to detect card-related transactional fraud. In addition, some reports suggest that AI can help solve the challenge of increasing payment volumes while simultaneously reducing illicit transactions. For example, acquirers using cutting-edge technology are 2.6 times more likely to say that their payment volumes have increased than those who do not use the technology.

Banking organizations view artificial intelligence as a vital tool for fighting fraud and improving revenues. It is a significant key because around 88 percent of AI acquirers reveal reduced fraudulent transactions. It turns out to be a significant factor for ensuring profitability as well. When surveyed, 53 percent of acquirers shared that reduced fraudulent cases are another critical factor for profitability.

Acquirers are also increasingly becoming aware that with the suitable investment in technology, including investments in AI for boosting fraud detection, they can easily differentiate merchant clients in a highly competitive market. A significant share of acquirers using AI systems, 83 percent, revealed that the process of acquiring new customers or merchants is another determining factor for increasing profitability in the coming year.

AI and Successful Fraud Detection

The utilization of AI for detecting fraud has helped many financial businesses boost internal security and simplify high-end corporate operations. Therefore, artificial intelligence has emerged as a crucial tool for preventing financial crimes due to its efficiency.

AI is used for analyzing higher volumes of transactions for uncovering fraud-related trends. The process can eventually be utilized for detecting fraud in real-time. Upon detecting fraudulent activity, AI models are deployed for rejecting transactions altogether or flagging them for further investigation. This allows investigators to concentrate the overall efforts on the most probably instances of fraud.

The AI model can also help offer automated response and decline codes for the flagged transactions. The respective reason codes will direct the investigator where they should seek to find the faults and speed up the investigation process. AI might also learn from the investigators when they will evaluate questionable transactions to reinforce the AI model’s knowledge. This helps in avoiding false positives and customer inconveniences while maintaining security.

Digital organizations can easily and precisely search for complex and automated fraud attempts by combining unsupervised and supervised machine learning as part of the significant artificial intelligence fraud detection process.

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