Naturally, fraud is a major, never-ending concern in the banking and finance industries. According to a report by the Association of Certified Fraud Examiners (ACFE), organizations lose around 5% of their annual revenues to fraud.
And as attempts to hack financial systems and deceive people and banks become more and more advanced, traditional methods of fraud detection such as manual review and rule-based systems are proving to be insufficient in detecting and preventing such crimes.
However, in recent years, the use of Machine Learning (ML) algorithms has become increasingly popular to counter fraudsters.
Stay with us as we discuss how ML can be used for fraud detection across the financial sector!
The Traditional Methods of Fraud Detection
These scenarios include manual review and the various rule-based systems banks and financial institutions utilize worldwide. In the case of a manual approach, a team of analysts review transactions and flag any suspicious activities, but the process is time-consuming and is still prone to human errors.
Rule-based systems use a set of pre-defined rules to detect cases of fraud, but these need to be constantly updated to keep up with newer, more advanced fraud attempts. Moreover, traditional methods also struggle to detect fraud in real-time, making it much more difficult to spot fraudulent activities in time to prevent them.
Machine Learning and Fraud Detection
On the other hand, ML algorithms can be used to detect fraud in banking and finance industries significantly faster and with greater precision. With ML, companies can analyze large amounts of data and identify patterns and anomalies that may indicate fraudulent activity.
They can also be used to detect fraud in real-time, which allows for immediate action to be taken in many cases, while the algorithms can be trained on historical data to improve their accuracy over time, which in theory makes them more effective at detecting fraud day after day.
Types of Fraud
There are several types of fraud in banking and finance industries, including:
- Credit card fraud occurs when someone uses a stolen or counterfeit credit card to make unauthorized transactions.
- Identity theft involves the theft of personal information to commit fraud, such as opening accounts or obtaining loans.
- Money laundering is the conversion of illegally obtained money into legitimate funds, often by moving the illicit money through legitimate channels to hide its origins.
- Insider fraud is committed by employees of an organization or other insiders who have access to sensitive financial or business information.
- In an account takeover, the fraudster gains unauthorized access to someone else’s account.
- Cyber fraud is committed with the use technology, e.g. a software or malware.
Fortunately, today’s advanced ML algorithms can now be trained to detect patterns and anomalies in large datasets, which can help detect almost all of the above mentioned types of fraud.
How Can ML Algorithms Help with Detection?
There are numerous methods an ML algorithm can sift through large sets of data as it tries to find anomalies and suspicious transactions. However, currently the most “popular” algorithms that can be used for fraud detection are the following:
- Logistic regression: a statistical algorithm that analyzes the relationship between variables to predict an outcome.
- Decision trees: a tree-like model that maps decisions and their possible consequences.
- Random forests: a collection of decision trees that work together to improve accuracy and avoid overfitting.
- Support vector machines (SVM): a classification algorithm that separates data into different classes using a hyperplane.
- Neural networks: a collection of interconnected nodes that work together to identify patterns and relationships in data.
Each of these algorithms can be taught how to do their jobs through supervised, unsupervised, and reinforcement learning.
In the case of supervised learning, the algorithms are trained on labeled data to predict whether a new transaction is fraudulent or not.
For unsupervised learning, the algorithms can be shown how to detect anomalies in data that could indicate fraudulent activity, while reinforcement learning compels ML algorithms to learn from experience and then go a few steps further by taking actions to prevent fraudulent activity.
Challenges in Implementing ML for Fraud Detection
While ML has quite a few advantages over traditional methods, there undoubtedly are challenges involved when it comes to the implementation of these algorithms.
One such challenge is of course the need to “feed” the algorithm with huge chunks of high-quality data to train it appropriately. However, with the constant evolution of fraud tactics and cyber fraud applications, there’s also the challenge of having to constantly supply the algorithms with new sets of data in order to keep it fully up to date.
Additionally, there is a risk of false positives and false negatives in the detection of fraudulent activity, and there also may be ethical considerations in the use of ML for this purpose, such as the potential for discrimination against certain groups, as the algorithm can actually pick up on the wrong data and forms of behavior that is unfortunately sometimes all too common on the Internet.
A Quick Overview of Successful Case Studies
Several banks and financial institutions have successfully implemented ML algorithms for detecting fraud:
For instance, JPMorgan Chase uses ML to analyze millions of transactions every day and identify any suspicious activities, while PayPal uses it to detect fraudulent activities in real-time and prevent losses.
These algorithms have also been successful in detecting credit card fraud; American Express, utilized ML to analyze transaction data in order to identify fraudulent activities happening with their customers’ cards.
Conclusion
All in all, while traditional methods are proving to be insufficient in detecting and preventing fraud, ML algorithms are on the rise, showing promising results in real-time fraud detection. As ML algorithms can be trained on historical data, their precision and efficiency increases substantially as well. And although implementing ML systems this way comes with its own set of challenges, the benefits far outweigh the costs.
If you’d like to have your own ML algorithms for detecting fraud (or to analyze colossal data sets of something else entirely), we can help you build and train these super-handy tools to meet your exact business needs.
If you would like to be informed about our opportunities in time, follow us on LinkedIn!
