How to Detect Banking Fraud in a Constantly Evolving Cyberspace?

Banking fraud has been around as long as banks have been in existence, but it’s acquired a whole new dimension with the rise of online banking. The banking industry’s ability to detect cybercrime or identify anomalies has to adapt with changing technology. Financial institutions continually need to keep up with the detection of banking fraud.

Banking Fraud Example

Losses due to banking fraud can add up, making it imperative to reduce them. However, banks and Fintech services are beginning to implement preventative measures to reduce these losses. Sophisticated data science models can speed up the process and predict anomalies, be more accurate and predictive, and reduce spending amounts for banks.

Fraudsters can potentially strike in any bank, in any country, across the globe thanks to their expertise in hijacking online banking sessions. The most common behaviors of fraudsters include:

  • Stealing the client credentials
  • Deploying malware
  • Stealing funds from client accounts infected with malware

Author Marc Goodman, in his book Future Crimes, points out that criminals rank among the first exploiters of emerging technologies; they quickly gain the expertise needed to turn even complex technologies against unsuspecting users.

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The Pitfalls of False Positive Results

False positives occur when fraud detection systems misread genuine transactions and flag them as fraud, resulting in the transaction being declined. This could lead to damaging customer relations between the account holder and the bank. This can also result in merchants losing sales as a result of declined transactions, leading to false positives as sales killers.

If the system is not calibrated to minimize false positives, the bank risks losing its clients when it wrongfully classifies legitimate transactions as fraud. If the bank cancels credit cards in such a scenario, it has to pay out of pocket for operational costs such as printing new cards and mailing them to frustrated customers. This could lead to a loss of trust and increased customer churn. Therefore, banks must be as accurate as possible in distinguishing between genuine transactions and fraudulent ones.

This is where the following tools and technologies become an essential part of fraud detection:

Tools for Banks to Beat Cybercrime and Reduce False Positives

Data Analysis Software

Data analysis software comes with a spectrum of tactics to detect fraudulent banking transactions. These include modes of analyzing various aspects of everyday business data such as entry dates validation, flagging duplicate transactions, numeric values summation, and statistical calculations to detect outliers that signal fraud. Internal checks and balances in the software allow the analysis of contextual situations for a standalone fraud investigation as well as a repeatable analysis of banking processes that render it susceptible to cyberattacks and estimating risk levels of one occurring.

Even traditional banking sectors have increased their management requirements for information, moving audit adjustment from cyclic, conventional approaches to a longstanding and risk-based model to keep up with fintechs. Constant surveillance with locally developed software can be helpful if preventative controls are not able to make the cut.

Artificial Intelligence and Machine Learning in Banking

Anomaly detection is a classic fraud detection technique driven by artificial intelligence. This technique picks out any deviations from set norms to measure against remote banking fraud and money-laundering processes. Anomaly detection-based anti-fraud solutions are more common than solutions that use predictive and prescriptive data analytics.

Anomaly detection’s inherent machine learning model is trained based on the continuous flow of incoming data that it constantly compares against pre-established baselines for normalcy with regards to banking transactions, new account generation, loan applications, and other banking transactions. The system flags any deviations from the norm for a human monitor. Upon review of the data, the human monitor can either accept or reject the flag as a bonafide alert. The human monitor’s decision is the basis for the machine learning model to understand whether its detection of fraudulent activity was correct or not, and if not, whether it was a hitherto unseen but acceptable deviation.

Machine learning-based solutions for fraud detection can be trained to detect fraud across more than one data channel and with more than one type of transaction and application, often in parallel.

Banks that employ artificial intelligence-based anti-fraud systems often see a reduction in their daily false positives count, and detection rates of actual fraud increases. This can empower banks to rearrange resource allocation toward stamping out real cases of fraud and detecting emerging fraudulent practices. It can also detect inconsistencies between known data, such as a difference between the registered geographical location of an account holder and a transaction location or when highly irregular types of purchases are made.

Data Analytics for Banking Fraud Prevention

High-tech data visualization has advanced analytics greatly in the last few years. Data science can discover hidden patterns and deliver valuable insights from enormous amounts of both structured and unstructured data. Data analysis requires a combination of data mining, machine learning, and advanced analytics to deliver useful insights.

High-tech analytical capabilities are classified into the following four general categories:

  1. Descriptive analytics: describes what has occurred. This could be something such as a recent weather report.
  2. Diagnostic analytics: delves into a phenomenon and explains why something occurred. In other words, they examine the factors which contributed to an event or occurrence. For example, what weather conditions caused a hurricane.
  3. Predictive analytics: takes the diagnostic information and predicts what might come next. This is weather forecasting—knowing weather patterns for the area, seeing what has occurred, and predicting what may happen in future.
  4. Prescriptive analytics: recommends solutions, preventative or contingency measures, or damage control.

Predictive and prescriptive analytics software can both work the same data and get similar training. Banks employ data scientists or banking data experts to first establish a baseline by labeling a very high volume of transactions as legitimate, acceptable, or fraudulent. Running them through the machine learning model then enables the software to recognize and flag banking fraud.

Fraud Orchestration

Fraud orchestration is a powerful new tool in a bank’s cybercrime arsenal. It functions in the form of a centralized platform where fraudulent activity can be monitored from a single location. Real-time data analytics are run in conjunction with fraud prevention systems, leading to the rapid identification of fraud and agility to respond to it. Another advantage of fraud orchestration is the ability to develop customer profiles for banks, based on customer spending patterns and trends.

This in turn helps eCommerce retailers supervise their customers’ purchases in real-time to stop a false positive reading. Thus, fraud orchestration provides banks with a more holistic picture of their customers, combining behavioral data with transactional data, and thereby fine-tuning fraud-detection systems.

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The Future of Banking Needs Accurate Fraud Detection

As more and more financial institutions increasingly adopt automated, holistic, and integrated modes of banking fraud detection, they have the potential to use their experiences to make detection systems even sharper over the long term.