Fraud Detection With Machine Learning: 5 Steps to Build One

Understanding Machine Learning in Fraud Detection

In the first section, let’s learn about the definition and technical overview of machine learning for fraud detection:

What is Machine Learning in Fraud Detection?

Four out of five fraud detection professionals admitted that there has been a significant increase in the sophistication of fraud attempts in past years. Such sophisticated fraud has become more of a threat to their businesses; however, it’s hard to detect with the naked eye or traditional approaches like rule-based.

With more fraudulent activities reported to government agencies, delays in updating the fraud detection system can expose your business to operational disruptions, financial loss, and reputational damage. Therefore, you need an intelligent tool that can analyze vast datasets in a second to spot signs of fraud, study the data over time, and adapt to new threats. This is the power of machine learning in fraud detection.

So how does it work? ML models are trained using historical data which covers both normal and fraudulent behaviors. By learning from the data, the models identify even insignificant patterns that might indicate fraud. For example, they can raise a red flag for repeated failed login attempts or suspicious spending behavior. Once trained, ML can forecast future fraud based on these patterns.

Beyond that, ML systems can track transactions in real-time and discover unusual activities immediately. This allows your business to promptly take preventive action before fraud causes any damage.

Technical Aspects of ML-Based Fraud Detection

Besides data, what stands behind the success of machine learning in fraud detection is its approaches and algorithms:

ML Approaches

You can leverage different machine learning types to identify fraudulent behavior:

Supervised Learning: This uses labeled data to spot already known patterns of fraud. For instance, in credit card fraud detection, past fraudulent transactions are labeled. ML models learn from these patterns and then flag any new transactions that match these known patterns. This method is useful when handling familiar and well-documented fraud types.

Unsupervised Learning: This approach detects completely new or unknown fraud. As such, it looks for unusual behavior or anomalies in customer activity, such as odd transaction locations or an unexpected soar in spending. Unsupervised learning is effective if you want to discover fraud that hasn’t been identified before. This offers more flexibility to deal with ever-changing threats.

Reinforcement Learning: This works by learning from a continuous feedback loop. Particularly, the machine learning model tries different ways to complete the fraud detection task, learning from its mistakes with each attempt to modify its approach. It keeps repeating this process until it becomes more skilled at identifying fraudulent behavior. One good thing about this approach is it doesn’t need labeled data. So it can work well even when having no prior knowledge of existing fraud patterns.

ML Algorithms

Algorithms are sets of instructions or rules that guide machine learning models on how to perform specific tasks. Accordingly, they allow the systems to analyze customer behavior, transaction data, and other relevant information to identify any signs of fraud.

Understanding these algorithms is crucial as they work differently based on the type of data and the complexity of the fraud patterns being analyzed. For example, clustering algorithms group similar behaviors to look for unusual outliers while neural networks can detect intricate data patterns. Using the right algorithms can produce desired outcomes and ensure accurate predictions.

There are various machine learning algorithms for fraud detection. Here are some common ones:

Decision Tree: This resembles a flowchart where each node represents a decision point based on key attributes (e.g., transaction amount or frequency). For fraud detection, a Decision Tree works by asking questions (i.e., does the transaction amount exceed $10,000?) to break down the data into smaller subsets (i.e., yes/no decisions) until it can classify a transaction as “fraudulent” or “non-fraudulent”.

Neural Network: This is a set of interconnected nodes (“neurons”) that discover complex fraud patterns in large databases. It works by handling transaction data (“inputs”) through various layers of neurons, each of which gives deeper insights into the data.

Logistic Regression (LR): This algorithm is used for binary or multiclass classification. It applies a logistic function called sigmoid to inputs to calculate the probability of fraud. For instance, it takes in input variables including customer location, transaction size, and device, and then computes a probability score between 0 (no chance of fraud) and 1 (a high likelihood of fraud). If the probability exceeds 0.5 (“threshold”), the transaction is marked as fraudulent.

5 Use Cases of Machine Learning in Fraud Detection

The demand for fraud detection and prevention is rocketing. This has increased the global revenue of relevant services. One research stated that the worldwide market for fraud detection will grow by annually 21.8% from 2024 to 2032. This is an unsurprising result of the increasing awareness of organizations and the wide adoption of cloud, machine learning, and other advanced techs. Alongside this growth, we’ve been witnessing a wider application of machine learning in different fraud detection practices.

1. Credit Card Fraud

Credit card fraud occurs when someone uses your card’s information to make purchases or withdraw money without authorization. This type of fraud can be conducted in many ways, like data breaches, phishing scams, or skimming devices. The European Banking Authority (EBA) reported the highest fraud values in credit transfers and card payments. Meanwhile, 60% of credit card holders in the US have also undergone financial fraud.

Besides good habits of using credit cards securely (like using multi-factor authentication or face ID for access), using machine learning also helps prevent credit card fraud. By analyzing real-time transaction data and instantly comparing it with historical data, ML can spot suspicious activities. These patterns may involve purchases made in a different location or unusual spending amounts. This enables your business to react promptly to potential fraud before it happens or becomes worse.

2. Point-of-Sale (POS) Anomaly

A POS anomaly involves suspicious or unusual activities happening during transactions at physical retail outlets. One famous example was a serious attack on Target’s POS terminals in 2013. At that time, malware (called RAM scraping) was installed in these systems to steal financial information from 40 million debit and credit cards. In addition to this card cloning tactic, POS fraud can take other forms like employee fraud, refund fraud, or legitimate POS terminals physically replaced by compromised ones.

To avoid these issues, POS terminals are often connected to back-end or cloud-based systems that store transaction information. Several places like restaurants even attach POS logs to their cash registers with the aim of recording transactions and analyzing logs. Through the data transmitted from these devices, machine learning can recognize fraudulent transactions in real-time.

3. Insurance Fraud

As the name states, insurance fraud refers to an individual or entity deceiving an insurance company to get benefits or payments to which they’re not entitled. This fraudulent activity can appear in various forms, from exaggerating claims and staging incidents to creating false claims.

Insurance fraud has a profound impact on insurance and genuine customers. First, to recover additional costs incurred by fraudulent claims, the companies have to raise premiums. This negatively affects all policyholders. Second, it takes longer time and more resources to conduct deep investigations and possibly engage in legal proceedings in case the companies need to handle fraudulent claims.

To release pressure from this fraudulent activity, about 30% of organizations have used ML-based techniques like predictive models, image analytics, and data visualization to spot anomalies. Machine learning helps them examine claims data in real-time and recognize inconsistencies that human investigators can otherwise overlook.

4. Mobile Payment Fraud

This type of fraud involves all deceptive practices targeting transactions on mobile payment apps (e.g., Google Pay or Apple Pay). Threat actors can exploit some vulnerabilities in these apps to steal sensitive information or make unauthorized transactions.

Companies like PayPal use measures like device fingerprinting and behavioral biometrics to protect their users from fraud. When a user logs in from an unrecognized device, PayPal will automatically activate further authentication and track patterns through behavioral biometrics.

So, how do these measures work with the assistance of machine learning? First, ML creates a unique profile (“fingerprint”) for each user’s device by using lots of features like the device’s IP address, operating system, or browser settings. ML will flag the activity as unusual if an activity occurs on a device that doesn’t match the known profile.

Additionally, ML can also analyze user interactions with mobile devices, like typing speed or swiping patterns. If the activity differs from the user’s identified pattern, ML can classify it as fraudulent. Monitoring biometrics will strengthen authentication and security during mobile transactions.

5. Money Laundering

This illegal activity refers to concealing big sums of money acquired from criminal activities (e.g., fraud or drug trafficking) in order to make them appear legitimate. Its goal is to “clean” the money so it can be used in the legal financial system without raising any suspicion. According to the US Department of Treasury, investment scams and healthcare fraud continue to be two illegal activities that result in the largest profits for perpetrators.

To avoid this fraudulent activity, 87% of organizations have integrated AI/ML technologies into their anti-money laundering (AML) initiatives and found them effective. ML is widely adopted to address existing AML challenges. They include:

Reduction of False Positives & Negatives: False positives are situations where a legitimate transaction is incorrectly marked as suspicious. Meanwhile, false negatives are instances where a suspicious transaction is incorrectly flagged as legitimate. ML helps minimize these errors through closed-loop learning. This process allows the output of an ML model to be fed back into the model as input, which enables continuous learning and improvement.

Automated Transaction Monitoring: ML can analyze vast volumes of financial data in real-time to flag suspicious transactions.

KYC (Know Your Customer): ML can extract key information and insights about customers from unstructured data (e.g., news articles or social media posts). This helps you verify a customer’s identification and evaluate their risk profile as well as sources of cash.

Behavioral Analysis for Risk Assessment & Improved Customer Due Diligence: ML helps evaluate customer behavior and detect unusual patterns in transactions. This allows ML models to predict potential money laundering activity.

Benefits & Challenges of Machine Learning in Fraud Detection

Pros

Through the applications outlined above, machine learning reveals its huge potential in fraud detection. This power is fueled by a range of exciting benefits, including:

Efficiency & Accuracy

Compared to traditional approaches, machine learning proves more effective and accurate in identifying fraudulent behaviors. It has self-learning abilities and super-fast processing speed to analyze vast amounts of data in real-time, constantly learn from new data, and improve its predictions.

ML doesn’t follow any predefined rules, but focuses on the unusual patterns and anomalies presented in the data. For this reason, it can adapt to evolving behaviors and develop fraud detection tactics. This flexibility allows them to discover even unknown fraud schemes and reduce false positives/negatives, hence improving overall accuracy and efficiency.

Richer Data Pool

Traditional approaches mainly work with structured data because this type of data is organized in a preset format (e.g., databases or spreadsheets). This data often includes transaction amounts, product categories, or customer IDs. But these approaches struggle with handling unstructured data (e.g., text, images, or social media interactions) as this data lacks a predefined format or clear structure. For this reason, they might overlook meaningful insights derived from this data.

Machine learning, however, can process both types as it uses algorithms to learn from any data format. This ability makes machine learning an adaptive solution that can detect any hidden or emerging fraud behaviors.

Better Compliance

Machine learning empowers fraud detection systems to track unusual practices that could violate laws. This enables your company to stay compliant with regulatory requirements and avoid unexpected penalties. Further, ML-enabled systems can be tailored to meet your specific industry standards.

Cons

You’ve understood how powerful ML is in fraud detection. But it doesn’t mean machine learning presents no challenges. Here’s what you should consider before adopting this cutting-edge tech:

Data Quality

As we all know, ML depends heavily on data for analytics and predictions. It means if your data is poor quality, incomplete, or unavailable, the model’s outcomes may be inaccurate. This can result in false positives or negatives, consequently impacting your decision-making and fraud detection.

Solution: You should train an ML-based fraud detection model on large validation datasets to enhance accuracy. Further, identify a suitable threshold for fraud detection. This threshold often defines the minimum conditions that will activate the model to mark an activity as fraudulent. This ensures your model doesn’t label too many false positives or negatives.

The threshold relies on which level of risk your business can tolerate and on which type of transaction is being analyzed. For instance, your model wants to analyze low-value transactions (like small online purchases), so you may set a less strict threshold to avoid disrupting customer experience for minor issues.

Data Privacy

Privacy is another significant concern when using customer data to discover fraud. Not to mention that in highly sensitive industries like finance or healthcare, using data sometimes can conflict with industry standards. This can lead to financial or legal penalties.

Solution: You can leverage different techniques to safeguard sensitive information when using machine learning for fraud detection. They involve data masking, encryption, federated learning, differential privacy, tokenization, etc. These techniques ensure data privacy and compliance with regulations like GDPR although they work differently.

For example, data masking replaces sensitive data with anonymized one. The data’s structure remains intact for analytics, while the real data values are hidden to protect data privacy. Federated learning, on the other hand, helps process data locally on devices instead of sharing it with a central server. While the local systems are trained with sensitive information, only the learned parameters are delivered to the central database.

Complexity & Cost

Developing ML models can be complex and expensive, especially when it comes to bespoke solutions. Besides, setting up one requires extensive expertise in data science and machine learning. For smaller businesses, the total cost of creating, maintaining, and updating these models might be overwhelming.

Solution: You may count on ready-made ML services that provide pre-trained models, built-in algorithms, and scalable computational resources to develop your own system. Some popular services include Amazon SageMaker and Azure Machine Learning.

5 Key Steps to Build a Machine Learning Model for Fraud Detection

If you want to build an ML-based fraud detection solution from scratch, it’s essential to follow a technical process. The following step-by-step guide will cover the full development cycle to help you craft one successfully:

Step 1: Define Problems & Collect Data

First, define problems related to existing fraud, technical infrastructure, and functional requirements.

What fraud problem does your business confront and want to resolve? If your company has to address card fraud, the model needs to detect unauthorized transactions. Meanwhile, if insurance fraud is your current bottleneck, the model should focus on handling false claims.

Further, you have to assess whether your current technical infrastructure can handle ML models and vast amounts of data. If not, consider upgrading or switching to another. Don’t forget to consider which functions your potential system needs. They can be essential features associated with your company, intuitive UI/UX design, robust security measures, etc.

Understanding these factors will help you envision clearly what your potential ML system may deal with.

Next, collect raw data. This data can come from different sources, including transactional data (e.g., customer details or payment history), behavioral data (e.g., device usage or spending habits), and external data (e.g., blacklists of fraudulent accounts). Ensure you have large databases that cover both fraudulent and non-fraudulent activities to help the system distinguish them.

Step 2: Design a Model

Now, sketch out the system architecture, including key components, UI/UX design, and integrations with other existing systems. You also have to define the tech stack, which covers a wide range of tools and technologies you’ll leverage to craft the model. Before actual development, you should create a proof of concept to test how feasible your project is. This helps you identify possible challenges you may encounter during development.

Step 3: Develop & Train a Model

Next, develop a model by commencing data preprocessing. The raw data you gather might contain duplicates, missing values, or any errors. So, cleaning and formatting data is essential to ensure your machine learning system will interpret it. By cleaning data, you can ensure your model will perform effectively and create precise outputs. This might involve:

Getting rid of duplicates and irrelevant data;
Processing missing values by removing them or filling in with mean/mode values;
Normalizing all numerical variables so that they can be properly scaled in a consistent range;
Encoding categorical variables (e.g., categories such as “Cash” or “Credit Card” in “Payment Method”) for algorithms to understand.

The quality and relevance of your data are important to develop an effective solution. So, find which data points (“features”) are relevant to your system. These features are individual measurable attributes of the database that help your model understand and identify fraud patterns. Accordingly, you can modify existing features or develop new ones through feature engineering.

Once the data is ready, you’ll use the right machine learning algorithms to train the solution. Sometimes, you may need to build various models and compare their performance until you find one that meets your requirements.

Step 4: Test & Verify a Model

To ensure the model works as expected, you need to test it on a separate dataset that it hasn’t been trained on before. This helps you assess how well the model can perform on new, unseen data, thus ensuring its efficiency in real-world scenarios.

You can also leverage testing approaches like a train-test split or cross-validation to evaluate the model’s metrics (e.g., accuracy, recall, AUC-ROC, or F1-score). Train-test split divides the dataset into two parts: a training set (training the model on patterns and relationships in the data) and a testing set (evaluating the trained model with unseen data). Meanwhile, cross-validation splits the dataset into multiple folds (e.g., 10-fold), trains the model on each fold, and evaluates its performance on the remaining ones.

Step 5: Deploy & Monitor a Model

Once the model has been tested and validated, it’s time to configure and integrate it into your existing corporate systems – be it payment gateways or customer databases – through APIs or other interfaces.

Then, deploy the model into your chosen environment (e.g., a cloud platform, an on-premise server, or a SaaS system). Ensure your machine learning model has real-time processing capability to spot and react to potential fraud instantly.

Upon launch, it’s crucial to continuously monitor your model’s performance. Track its accuracy and retrain it with new data frequently to ensure it can adapt to evolving fraud patterns.

Conclusion

This article has outlined all the key information you need to know about machine learning for fraud detection. From ML’s technical aspects, common use cases, and key steps to build an effective fraud detection system, we covered them all! Now, it’s your turn to focus on your bespoke solution!

Do you want to build a customized, scalable ML model, yet struggle with expertise related to this advanced tech? Don’t worry, Designveloper is here to help you realize your idea! Our team has extensive expertise and experience in crafting cutting-edge AI solutions tailored to your specific business needs.

Leveraging the latest AI advancements and advanced algorithms, we’re committed to creating dynamic and innovative models that seamlessly integrate your existing infrastructure and offer robust security practices. These solutions boost your operational efficiency, enhance customer experience, and propel your business growth. Contact us and discuss your idea!