Fraud

Understanding Real Time Fraud Detection in Banking Sector Using Data Analytics

April 20, 2024
Understanding Real Time Fraud Detection in Banking Sector Using Data Analytics

Bank fraud has existed for centuries, but it has reached a whole new level of sophistication in the digital age. According to the Federal Trade Commission (FTC), fraud losses exceeded a staggering $10 billion in 2023 alone. 

Scammers are now using more advanced tactics, such as identity theft, phishing, account takeovers, and check fraud, to exploit the cyber vulnerabilities of financial institutions. 

But don’t worry—there’s a silver lining to this dark cloud. Thanks to real-time fraud detection technologies, banks are now better equipped than ever to fight against these fraudsters. 

One of the most important tools in their arsenal is data analytics, which enables banks to track customer behavior, prevent fraud, and minimize its impact. So, while fraud may never be completely eradicated, we can take comfort in knowing that updated solutions are here to help. 

The evolution of fraud detection in banking

Historically, banks relied on rules-based systems and manual assessments of activities to spot suspicious transactions. This provided banks and their customers security in the pre-digital era. 

Today, however, the velocity, volume, and complexity of the digital banking space underscore the flaws in these traditional banking fraud detection methods. Namely, that they’re:

  • Reactive rather than proactive, in that transactions are evaluated after they’ve gone through 
  • Prone to human error
  • Susceptible to false positives (or the inability to decipher the difference between legitimate and fraudulent transactions) 

Over time, automated algorithms were employed to recognize unusual spending and trigger alerts—but this process can inadvertently flag legitimate transactions and may result in lost revenues and customer dissatisfaction. What’s more, traditional anti-fraud methods fail to anticipate ever-evolving financial fraud trends, which is key to staying a step ahead of attackers.

AI in banking security has radically changed the predominant approach to combating digital bank fraud by perpetually collecting new data and matching it against pre-established behaviors. By capitalizing on the power of real-time analyses, the banking industry has the opportunity to detect fraud before and as it happens. 

Data analytics in fraud detection

Data analytics in fraud detection technologies involves gathering and investigating data to catch and obstruct fraudulent behavior. Rather than poring over transactions that have occurred after the fact, real-time financial data analysis works at the moment, just as financial transactions take place—and, at times, before.

Artificial intelligence and machine learning (a subset of AI) play a vital role in this: by learning from the data they’re given—both historic and new—they can identify abnormalities that may be indicative of digital banking fraud, such as:

  • Large transactions
  • Transactions outside of the customer’s standard geographic location(s)
  • Unusual time intervals between transactions

Key components of real-time fraud detection systems

Real-time fraud detection in banking sector data analytics key in on:

  • Pattern recognition, which recognizes standard behaviors 
  • Anomaly detection, which pinpoints actions that fall out of this pattern
  • Predictive analytics in fraud prevention, which identifies trends and determines the likelihood of fraudulent behavior

However, it’s important to note that the efficacy of advanced cybersecurity in finance technologies such as these hinges upon the relevance and quality of the data provided to machine learning fraud detection platforms. Not to mention, the AI is given data without compromising consumer privacy and compliance regulations.

This calls for a well-rounded, robust, continually updated approach to reflect the perpetually changing fraud (and digital banking) landscape. 

Techniques and algorithms used in real-time fraud detection

AI-driven security measures create this very robustness through the three primary types of machine learning models: supervised, unsupervised, and reinforcement. The data collected and adapted by these machine learning fraud detection models continually shapes algorithms that conduct:

  • Risk scoring: Risk scoring “grades” transactions on their threat level according to location, amount, frequency, and established patterns and allows banks to take action.
  • Identity verification: The growing importance of digital age verification and identity proofing underscores the crucial need to ensure customers are who they claim to be.
  • Network analyses: Network analyses uncover collaborations between fraudsters by assessing the connection between entities—such as users and devices—and flagging alarming relationships. 

Fraud detection data analytics is performed in mere milliseconds, thus allowing banks to move swiftly and appropriately when suspicious activity is detected.

The impact of real-time fraud detection 

Fraud analytics has gone from a smart choice to a business imperative: estimates from a recent survey suggest that more than one bank customer out of 10 will cancel their account if they believe fraud wasn’t handled properly. 

Because AI-powered data analytics not only works in real-time but can also discern subtle complexities that may be unseen to the human eye, it’s a vastly superior fraud detection method that may result in:

  • Enhanced security
  • Minimized potential losses
  • Improved customer trust and satisfaction 
  • Increased customer retention
  • Enriched reputation

Data underlines this: according to the Association of Certified Fraud Examiners, institutions that utilize proactive data tracking decreased their fraud losses by 54%.

The future of fraud detection

Fraud is expected to become increasingly cunning as innovations in technology are made. However, the same could be said about real-time fraud detection in banking sector solutions. Generative AI will continue to shape how fraud is fought—and Microblink is at the helm of it. 

With real-time banking solutions ranging from face photo tampering detection to barcode authentication, Microblink integrates seamlessly into existing workflows to enhance real-time fraud detection

Try our demo today to learn more.

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