The Use of AI for Financial Fraud Prevention

The Use of AI for Financial Fraud Prevention
The Use of AI for Financial Fraud Prevention

 A vital component of modern economies, the banking system is an intricate network of organizations, tools, and procedures that make it easier to transfer and store money. Due to the rapid growth of technology and shifting consumer preferences, the definition of a bank has evolved from physical facilities to include digital and online platforms [1],[2].

The contemporary financial system has many advantages, but it is not without issues. The increasing prevalence of digital transactions raises serious cybersecurity risks. Avoiding fraud and risk management are critical to preserving the credibility and integrity of financial institutions in the complex world of banking. The term « banking fraud » refers to a broad spectrum of illicit actions, including identity theft and complex cyberattacks. As the banking industry develops, particularly with the emergence of digital banking, fraudsters’ techniques are becoming more complex [3].

Typical Fraud Types AI Can Identify
 Ø Card Fraud:

Card scammers don’t manually break into cards. They delegate the grunt work to their bots, who frequently employ brute force attacks that put a significant load on payment gateways. Card fraud is one of the most prevalent forms of fraud; according to analysts, the total amount of fraudulent transactions will rise from $32.04 billion in 2021 to $38.5 billion in 2027.

Because AI doesn’t rely only on IPs and IP reputation to thwart incoming threats, it is capable of detecting this kind of fraud. Artificial Intelligence keeps an eye on user activity to identify and prevent dangerous bots. If there is ever any doubt, a CAPTCHA is shown to the user [4].

Ø Account Takeover (ATO):

The criminals can breach the accounts of actual users through automated threats similar to fake account creation. ATO prevalence is rising: 55% of online retailers said they had more ATO attacks in 2021 than the previous year. Because ATOs specifically target consumers and their sensitive data, they harm your organization’s credibility.

The challenge with ATOs is that their visibility isn’t always instantaneous. These attacks are risky since they are typically covert. ATOs can be defeated by multi-factor authentication, although many users refuse to enable it. AI is a discrete method of stopping ATOs since it tracks every signal that bots leave behind before taking over an account.

Ø Filling in Credentials:

The automated danger known as « credential stuffing » occurs when a bot attempts to enter frequently used usernames and passwords into your login page. Occasionally, a portion of these passwords and identities come from earlier data breaches. There’s a considerable likelihood that when combined with easy or frequently used passwords, fraudsters will be able to access an astonishing amount of user accounts.

This can result in carding and ATOs in addition to crashing your login page. To detect whether you’re the target of a credential-stuffing assault, AI monitors variations in website traffic, a higher-than-usual login failure rate, and other factors.

 

 

AI Applications for Financial Fraud Prevention:

The use of artificial intelligence (AI) is transforming how businesses identify and stop financial fraud. Artificial Intelligence (AI) can swiftly and precisely analyze vast amounts of data to spot unusual transactions and anomalies that can point to fraudulent conduct by utilizing machine learning algorithms.

 

 

This article will examine the advantages of artificial intelligence (AI) over conventional techniques for detecting fraud, as well as how it is being applied to stop financial fraud. The following are some significant applications of AI in fraud detection [5]:

 Ø Automatic anomalies identification:

 Transactional fraud monitoring systems can train AI algorithms for automated fraud detection to identify trends in data that point to possible fraudulent behavior. Abnormal transaction quantities, numerous transactions done from the same gadget, or transactions made from several places quickly might all be examples of these patterns. When the AI notices a discrepancy, it might mark the transaction for additional examination.

Ø Behavior assessment: 

Artificial intelligence (AI) can analyze consumer behavior trends over time to spot anomalous activities. For instance, the AI system may identify transactions as suspicious if a consumer starts making huge purchases at a time when they don’t usually spend that much money.

Ø Natural language processing (NLP):

AI systems can analyze email correspondence and chat transcripts to find signs of fraud by using NLP to analyze client contacts. For instance, the AI system can recognize a possible fraud attempt if a customer modifies their account details abruptly and then emails to ask for a password reset.

Ø Constant learning:

Over time, AI systems can be educated with fresh data to increase their precision and potency. Through this ongoing learning, fraud detection systems are kept abreast of the most recent techniques and developments regarding fraud.

In general, artificial intelligence (AI) plays a crucial role in fraud detection by quickly spotting suspicious activity and fraudulent transactions, lowering the possibility of financial loss for companies, and safeguarding client information.

 Conclusion:

With the introduction of cutting-edge technologies, risk management and banking scam protection are fast changing. Artificial Intelligence is one of the main technological forces in these fields (AI). The amount of data is increasing exponentially, making it hard for humans to manually sort through and find anomalies. This is where artificial intelligence (AI) comes in, allowing the identification and avoidance of fraud to move from reactive to proactive methods. AI allows for immediate response in questionable situations and provides predicted insights by utilizing the massive amounts of data produced by banks.

 References:

[1] B. Mytnyk, O. Tkachyk, N. Shakhovska, S. Fedushko, and Y. Syerov, “Application of Artificial Intelligence for Fraudulent Banking Operations Recognition,” Big Data and Cognitive Computing, vol. 7, no. 2, p. 93, May 2023.

 [2] E. Muthu Kumaran, K. Velmurugan, P. Venkumar, D. Amutha Guka, and V. Divya, “Artificial Intelligence-Enabled IoT-Based Smart Blood Banking System,” in  Proceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications, 2022, pp. 119–130.

 [3] S. Jahandari, A. Kalhor, and B. N. Araabi, “Order determination and transfer function estimation of linear mimo systems: application to environmental modeling,” Environmental Modeling and Software, 2016.

 [4] Data Dome, “How AI is Used in Fraud Detection – Benefits & Risks”

[5] Fraud.com, “Artificial Intelligence – How it’s used to detect financial fraud”