How Advanced ML is Combating Fraud in Financial Institutions

Fraud has always been a big problem in the financial sector and with the growth of technology, it has gotten worse. This has made it quite easy for hackers to use advanced strategies to exploit gaps in these networks. As a result, there has been an increase in the number of failing traditional methods of detecting fraud such as using rule-based systems. 

This prompted the move towards advanced machine learning (ML) techniques that several companies, like TAZI, offer for fighting fraud. It also assists in real-time identification and prevention of shady activities by financial institutions. In this article, we’ll discuss some ways ML can and is used to fight against fraud in financial institutions.

What is Financial Fraud?

Financial fraud indeed encompasses a range of types, including credit card fraud, identity theft, account takeover, money laundering, and insider trading, among others. The proliferation of digital banking and online transactions has exacerbated this issue. Cybercriminals take advantage of remote operations and conceal their identities using robust technologies designed for anonymity.

According to the Association of Certified Fraud Examiners (ACFE), organizations globally lose about 5% of their annual revenue due to fraudulent actions. This roughly equals to a sum of $4.5 trillion.

Traditional Fraud Detection Methods

Financial institutions previously relied on rules-based systems for detecting illegal transactions. These methods depend on certain rules and thresholds to identify suspicious activities. For instance, a rule might trigger an alert if a transaction is above a certain amount. It may also do so if multiple transactions from different geographical locations are made within a short time.

While rule-based systems can be effective for detecting known fraud patterns, there exist several limitations you should know of:

• Static Nature: These systems are static and detect fraud only based on pre-established rules. They do not work against new and changing fraud tactics.
• Multiple False Positives: Rule-based systems usually have many false positives that overwhelm the supervisor and result in legitimate transactions being flagged as shady.
• Manual Updates: Rules updating and maintenance involve significant manual efforts as well as expert knowledge. This makes it hard to keep up with the recent updates.

How Machine Learning (ML) Has Impacted Fraud Detection

Machine learning, which is part of artificial intelligence (AI), simply refers to the use of algorithms that learn from data to make automatic predictions or decisions. In relation to fraud detection, there are ML models that can analyze large amounts of data to discover irregularities, which could indicate the potential of shady activities. That said, here are more importance on ML for fraud detection:

• Adaptability: ML models are able to adapt to new and emerging fraud patterns by continuously learning from new data.
• Accuracy: These algorithms can achieve higher accuracy in detecting fraud, reducing the number of false positives.
• Scalability: ML models can handle large volumes of data and transactions, which makes them suitable for financial institutions with extensive and complex operations.

Types of Machine Learning Techniques for Fraud Detection

Fraud detection is done using several ML techniques, each with its strengths and applications. Here are a few of the commonly used ones:

Supervised Learning

For this technique, a ML model is trained on a labeled dataset meaning that it knows if the transaction was fraudulent or not. Once the model has learned about common fraud patterns, it can then apply this knowledge to newly received files. The most common algorithms used in supervised learning for fraud detection include:

• Logistic Regression: a statistical method that models the probability of binary outcomes such as fraud or no-fraud.
• Decision Trees: A tree-like model making decisions based on values of input features.

Unsupervised Learning

Financial institutions often use unsupervised learning when the dataset has no labeled outcomes. It tries to identify patterns and irregularities based on the structure of the data. This method is really effective in identifying new and unrecognized types of fraud. 

Common methods of unsupervised learning are:

• Clustering: grouping similar transactions and finding outliers that separate them from the norm (e.g., k-means clustering and hierarchical clustering)
• Anomaly Detection: Identifying unique transactions different from the others (such as autoencoders, isolation forests and one class SVMs).

Semi-Supervised Learning

Semi-supervised learning combines features of both supervised and unsupervised learning when most of a dataset remains unlabelled while only a limited portion is known. In this case, the model first learns initial patterns with the help of labeled data and then uses the information to detect potential fraud in unlabeled data. This technique can be particularly effective in cases where obtaining labeled data is difficult or expensive.

Reinforcement Learning

This technique involves training an algorithm to make decisions using a trial and error approach. The application of reinforcement learning in fraud detection optimizes the decision-making process that should be used in flagging any suspicious transactions. 

Case Studies of Machine Learning

Here are some case studies of machine learning for fraud detection:

Card Fraud

Credit card fraud is one of the most common types of financial fraud. Financial institutions use ML models to analyze transaction data in real time and flag any suspicious activity. They might use a combination of supervised and unsupervised learning to detect fraud. 

Research and industry reports consistently demonstrate that AI significantly enhances both the speed and accuracy of fraud detection, making it a crucial tool in safeguarding financial transactions.

Fraudulent Pattern Recognition

Artificial intelligence systems analyze past data and user behavior to detect trends that could indicate fraudulent activity, such as identity theft, payment fraud, and account takeover. In addition to advanced machine learning techniques, financial institutions prioritize cybersecurity measures to protect sensitive data. Technologies like Virtual Private Networks (VPNs), such as NordVPN or Surfshark, play a crucial role in securing their networks against unauthorized access and data breaches.

Tracking Transactions

Large-scale financial transaction volumes are instantly analyzed by AI-powered transaction monitoring systems. They can also alert users to questionable activity, including fraudulent transactions, odd spending patterns, and more.

Conclusion

Advanced Machine Learning techniques are transforming fraud detection in financial institutions. Through ML, institutions can examine huge volumes of data as it comes in real-time, detect new patterns of fraud, and decrease false alerts. 

While there are barriers such as data quality, interpretability, and regulatory compliance, the advantages of machine learning (ML) for detecting fraud are obvious. Get started with TAZI today and prevent any form of fraudulent activity in your business.