The rise of digital financial solutions has transformed how customers go about their day-to-day operations, yet also opened the doors for scammers to exploit vulnerabilities within the system. As fraud attempts and scheming techniques steadily rise, financial fraud detection using machine learning enters the stage, offering unparalleled prevention and protection.

Where traditional systems fail to safeguard institutions, ML models evolve to catch threats before they become critical losses. Although this approach seems novice for some businesses, it has progressed by leaps and bounds over the last few years and already battles fraud in major organizations.

In our guide, we will explore how such systems work, what types of fraud they can combat, which models to pay attention to, the benefits they bring to the table, real-life examples and use cases, and much more. So, let’s get started.

Financial Fraud Detection Using Machine Learning vs. Traditional Rule-Based Systems

The discourse around which type of system benefits financial organizations involves understanding what makes them different, how they operate at their core, as well as the pros and cons of each approach. One can argue that ML is a few steps ahead of the traditional methods, bringing efficiency and automation to the table, yet it’s reasonable to have your own reservations.

Before we can dive into comprehending the ins and outs of this technology, it is important to take a step back and evaluate the distinctions between machine learning fraud detection and rule-based systems.

Traditional Rule-Based Systems

Such systems have a long history of being utilized to protect both customers and finance institutions from fraudulent activities. They are praised for ease of implementation and a standardized approach to flagging dubious patterns based on predefined rules. So, how do these rules work?

Normally, an “if-then” logic is applied to traditional systems, meaning if a certain condition has been met, then a specific action is taken. For example, if a user exceeds a transaction limit, then the account could be flagged for a review.

Unlike fraud detection in financial transactions using machine learning, traditional systems require these rules to be put in place manually and call for consistent monitoring and updates to improve detection. These rules are crafted by specialists based on known scheming tactics, which is where the traditional system often falls short, as it has no way of automated adaptability.

The conditions that triggered fragging can be various, including exceeding a transfer amount, a predefined number of failed logins, high-frequency transactions in succession, and more. The benefit of this method lies in its simplicity, which does not require extensive expertise to implement and sustain it.

On the other hand, the downside of a traditional system is represented by a relatively high rate of false positives. Essentially, the system can unfairly flag legitimate operations due to the failure to recognize nuances, often leading to decreased customer satisfaction.

Machine Learning for Fraud

The versatility and flexibility of ML systems for fraud detection are what put them ahead of the traditional method. According to the IBM study, 22% of surveyed companies are adopting artificial intelligence to prevent fraud.

In simple terms, it is trained on a vast amount of historical data marked as either legitimate or fraudulent to learn the distinction between patterns.

Compared to the rigid rule-based approach, machine learning models constantly learn from new data and adapt to previously undefined suspicious activities, improving accuracy without manual supervision. The ability to pick up on subtle deviations within user behavior is also a leading factor in decreasing the number of false positive reports.

Still, there are challenges unique to the ML algorithms that reasonably raise doubts for organizations. One of them is the need for high computational resources to train the model accurately, which can initially be time- and resource-consuming. The other is an issue with transparency. Some models can be hard to interpret and explain why a certain action has been flagged.

Later in our guide, we will dive deep into how such models work precisely, but for now, let’s briefly summarize the differences between financial fraud detection using machine learning and rule-based systems.

Use Cases of Fraud Detection Using Machine Learning

In the financial sector, the concerns regarding various types of fraud call for advanced systems capable of handling them efficiently. Traditional approaches cannot keep up with evolving scheming methods and recognize nuanced patterns as fraud continues to rise.

As a result, machine learning has seen a surge in popularity, offering effective techniques for combating deceitful activities. Let’s dive deeper into this topic and overview some of the main areas in which ML can aid financial institutions in circumventing fraud.

Credit Card Fraud

This type of scam is possibly the most widespread one where a fraudster gains access to the credit card information to make an unauthorized purchase. In this case, financial fraud detection using machine learning is superior to traditional systems in recognizing nuanced patterns. Various algorithms are at play to analyze customer behavior and transaction history to detect suspicious activity while not interrupting legitimate transactions.

The anomalies illustrative of credit card fraud that ML can detect are diverse. A few of the common ones include using an account in one location but the transaction registering in another, small purchases over a short period of time followed by a large transaction, etc.

Insurance Fraud

Insurers process large volumes of claims daily, making it difficult to keep up with emerging deception tactics and pinpoint scheming patterns. Machine learning algorithms can be trained to assess claims and flag dubious ones using a set of parameters, such as type of claim, damage costs, previous claim history, and more. This substantially automates processing, allocating only high-risk cases for human intervention.

Natural Language Processing (NLP) is also typically leveraged to analyze text-heavy descriptions of claims for inconsistencies, conflicting information, exaggerations, and other factors that would indicate an attempt at fraud. Such models can help identify subtleties that would otherwise go unnoticed by professionals.

Account Takeover (ATO)

Regarding machine learning for fraud in the area of account takeover, where a scammer accesses someone else’s account, the models have advanced significantly. Specifically, behavioral biometrics and device fingerprinting have been making waves in some financial institutions (we will provide you with an example later in the article).

The former is a model that analyzes how users interact when logging in, particularly their typing speed, screen navigation, and mouse movements. If abnormal behavior is detected, the system can flag it as unauthorized access. The latter is a technique that collects data about a user’s device, like operating system, browser version, plugins, etc., so the alarms can be raised if the access is made from an unidentified device.

Identity Theft

Identity theft refers to when a scammer steals a customer's personal information, such as their name, social security number, password, and so on, to log into an existing financial account or create a new one. Fraud detection using machine learning comes to the rescue in such a serious event by inspecting account activities that would be atypical for a specific user.

In cases of new account creation, safeguards are put in place to verify a customer's identity, requiring facial recognition and ID scanning to determine if the documents are forged. Instances where one piece of personal information is used to create several accounts, can also be flagged as identity theft and restricted from any use.

Money Laundering

This type of criminal activity poses a major challenge for traditional systems to detect as they generally apply various techniques for concealing illicit funds and making them appear legitimate. Machine learning fraud detection models are capable of recognizing subtle manipulations within transfers that would suggest money laundering.

This scheme often involves multiple accounts and layering transactions to obscure the origins of money, such as transferring amounts that are below suspicious levels. ML systems can further pinpoint relationships between accounts that would suggest a questionable link. Deviant patterns like large international transfers that are atypical for a particular user can also be put under scrutiny.

Explore more insights: AI in Fintech: The Cutting-Edge Technology Driving Future of Financial Services

Benefits of Financial Fraud Detection Using Machine Learning

As you can see, the applications of ML in fraudulent activity detection are expansive, and it would be unusual, to say the least, if it didn’t offer compelling advantages to the business. Taking into consideration that the needs of vendors are unique and the areas that these models can improve differ, we will be talking about the overarching benefits you can reap with machine learning in this sector.

Precision in Fraud Detection

Fraud detection with machine learning offers a notable advantage — precision. Yes, these models are not completely error-proof, and occasional mistakes are bound to happen, but improving the overall accuracy cannot be overlooked. Since the systems are trained on massive amounts of complex datasets, they are more capable of identifying fraudulent activities.

The assessment of a variety of features and patterns within the transactions is what enables it to distinguish between legitimate and deceitful operations. On top of that, such models are capable of minimizing the occurrences of false positive and false negative flags.

Efficiency of the Process

Financial fraud detection using machine learning works in real-time, processing transactions as they happen. This means if the forgery is detected right away, the financial institutions are able to put a halt to the operation and prevent any tangible harm before it occurs.

Compared to a manual response to risks, where it can take some time for the specialist to validate the transaction, machine learning systems can take action in less than a minute. As a result, significant financial losses to both the vendor and the client can be swiftly avoided before the situation escalates.

Resource Saving

Speaking of financial savings, this point extends further as machine learning for fraud is fully automated, which means the need for manual investigations substantially reduces. By minimizing the number of false flags, the workers would not need to spend additional time verifying identities and scrutinizing the transaction histories.

Instead, they can focus on more high-risk cases where their skills are better suited. Ultimately, this can decrease operational costs for the institutions in the long run while delivering an enhanced customer experience.

Scalability for the Future

Considering that scheming tactics continue to evolve and introduce new complex patterns, fraud detection machine learning systems will be tasked with handling protection on a larger scale. Luckily, the advantage of ML lies in its ability to expand and grow alongside the data that it’s trained on.

Forecasts for the future tell us that the number of transactions will only keep rising, pushing vendors to tackle large volumes of data. This is where such models become integral to supporting high loads without sacrificing performance, as they are capable of analyzing millions of operations simultaneously.

Machine Learning Models for Fraud Detection

Knowing its varieties, purposes, and functional capabilities is vital to understating which model to choose for your business. One ML system can make a great fit for your objectives and resources, while others may be unsuitable to handle your needs.

Luckily, there is an abundance of choices available to you. To make this part easier to digest, we will break down models for financial fraud detection using machine learning into supervised and unsupervised categories, shedding light on what these options have to offer.

Supervised Models

In this approach, algorithms are trained on input features and corresponding labels, which serve as correct answers. The model learns to acknowledge patterns from the input and map them to the right label. Advanced models can also predict labels for new data without being trained on it. Here are some of the most commonly used supervised models:

• Neural Networks. These powerful models are structured to mimic the human brain with interconnected nodes designed to process inputs and recognize anomalies from extensive datasets. Recurrent neural networks (RNNs) can evaluate patterns in a sequence of transactions, while Convolutional neural networks (CNNs) aim to process ID cards, checks, and other images.
• Random Forest. Leveraging a collection of decision trees, which are essentially branches of data with unique sets of values and labels, this model of machine learning for fraud takes the average of votes on whether the action is fraudulent or not to make a classification. Random Forest is regarded as one of the most precise models in this area for its ability to balance data.
• Logistic Regression. It is a fairly simplistic model that performs well with smaller datasets. It uses binary classifications to detect the probability of fabrication based on the relationship between features, outputting a value between 0 and 1. This helps assess whether an action belongs to one category or the other. The model’s interpretability and ease of implementation make it popular.
• Extreme Gradient Boosting (XGBoost). Known for its flexibility and efficiency, XGBoost creates an assembly of decision trees sequentially, allowing it to correct each previous tree's mistakes. The algorithm is also praised for its ability to handle missing data and imbalances typically present in an area like fraud detection.
• Support Vector Machine (SVM). An advanced model of fraud detection using machine learning that utilizes hyperplanes to separate different data points of classifications with the largest margin. One major attraction of this algorithm lies in the capacity to tackle non-linear relationships within data and predict with high accuracy.

Unsupervised Models

In unsupervised learning models, there are no labels to guide the algorithm to the correct prediction. Instead, training involves clustering data by similar points and identifying anomalies that showcase atypical patterns. Below are a few of the popular models of this type:

• Isolation Forest. An anomaly detection algorithm that operates on the principle that outliers are fewer and different compared to regular data points. This makes it easier to isolate outliers without scrutinizing normal data and having prior knowledge of what these anomalies might look like.
• K-means Clustering. In financial fraud detection using machine learning, this algorithm is particularly useful for identifying abnormalities. It works by selecting similar data points into K clusters, so when a transaction does not appear to fit into any cluster, it can be flagged as a potentially fraudulent activity.
• Autoencoders. A type of data reconstruction algorithm that works in two parts: the encoder compresses data into a latent representation to define the main characteristics of the input. Then, the decoder reconstructs that data in a way that is as similar to the original as possible. The catch is that fraudulent transactions will have a high error rate, making them easier to detect.

How Does an ML System Work for Fraud Detection?

Wrapping your head around how ML-based systems work may seem like exploring uncharted territory, but the process is fairly straightforward when you break it down. You don’t need to be a data scientist to have a basic understanding of this process.

Let’s take a condensed look at the way a system for financial fraud detection using machine learning functions from creation to deployment.

Data Collection

Ensuring the data you train on is high quality is among the most crucial aspects of making an ML system perform accurately and consistently. This step involves collecting relevant information, such as transaction histories, locations, devices, user behavior, etc. The data also needs to include defining information about both legitimate and fraudulent activities to help the system make distinctions.

Information Preprocessing

The raw data you gathered for the model of fraud detection using machine learning has to be cleaned up. Missing values need to be handled, categorical variables established, outliers removed, and more. The dataset must be appropriate for the algorithm to learn, so clearing out irrelevant information is vital.

Feature Engineering

This stage includes transforming the data into meaningful features or variables that would let the system know which actions are fraudulent and which are not. Conditions like transfer frequency, discrepancies in location, sudden transaction spikes, and so on will define if the user's behavior is suspicious.

Model Training and Evaluation

Now that you know which models exist, you get to choose one of the types of machine learning for fraud that best represents your goals. Based on the preprocessed historical data, the model will be trained to recognize the connection between features and labels. Evaluation must also take place, testing the algorithm on metrics like recall, accuracy, precision, and more.

Deployment and Monitoring

Once you reach the optimal performance of your ML-based model, it can be deployed to production environments and integrated into your system to begin detecting fraud right away. Ongoing monitoring, introduction of updates with new data, and possible retraining, if necessary, become your main priority to make sure your solution adapts as new concerns emerge.

Examples of Fraud Detection Using Machine Learning

With all the information you have learned so far, you may ask yourself: “Okay, but how do I put this into a real-life perspective?”

The good news is that a few major financial companies already employ machine learning in their fraud prevention systems, and we can paint a full picture of how it works in a realistic scenario. Let’s break down the three most demonstrative examples.

PayPal

PayPal, being one of the largest online payment systems in the world, utilizes systems for fraud detection using machine learning to supplement the enormous amounts of transactions it handles on a daily basis. Such popularity and high activity make it paramount for the company to implement a fast and reliable prevention system.

The solution that PayPal opted for operates in real-time, evaluating factors such as geolocation, device, user behaviors, and more to identify anomalies and take action immediately. For example, if the accounts start to exhibit unusual behaviors, like many small transactions in succession, the system can flag them as suspicious.

The nuanced system is also trained adaptively, meaning if the user begins to showcase new patterns of behavior, the model can cluster this data for further overview. Whether or not the fraud is validated, the model adapts to the newly found data.

JPMorgan Chase

In the case of JPMorgan Chase, the firm leverages a sophisticated machine learning fraud prevention model that relies on neural networks to profile user behavior and detect anomalies. Recognizing the evolution of scheming techniques, which are becoming more complex and hard to pinpoint, the company’s system can pick up on subtle anomalies across several aspects.

To put it into perspective, let’s say a scammer tries to make transactions based on stolen identities using different accounts. The model can analyze the correspondences of data points, such as IP addresses and devices, to identify a premeditated scheme and prevent it from going forward.

American Express

American Express puts its best foot forward with its approach to fraud detection in financial transactions using machine learning. Specifically, it shines in the development of a system that leverages ML techniques to evade account takeovers, identifying whether the login was made by the real customer.

In the company’s own words, the model can almost instantaneously predict fraud risk by scoring every login. For instance, if the system deems the attempt fraudulent based on patterns like keystroke and typing speed, it will require the user to provide additional proof of authenticity. These methods demonstrate the advanced behavioral biometrics and device fingerprinting employed in the machine learning algorithm used by American Express.

Detect Fraud in FinTech With Us

As the finance sector faces emerging forgery tactics and scheming techniques, the need for a robust system to protect the institution and the customer is more prevalent than ever. Fortunately, the technology evolves alongside new threats, learning to circumvent them and prevent critical losses.

Companies leveraging financial fraud detection using machine learning are already seeing improvements, paving the way for a more secure future. To help you get on board and navigate the complex landscape of ML systems, we explained every detail associated with this subject. Knowing its purpose, how it can amplify your business, the multitude of ways it works, real-life examples, and more, you won’t have an issue picking the right solution for you.

If you want to implement machine learning to prevent fraud, be sure to contact us. We will deliver a state-of-the-art system that will support you long-term and keep abreast of industry developments.