Financial Fraud Detection Using ML and Measures to Prevent It

The financial industry involves an extremely high volume of real-time online transactions. This is what makes it particularly vulnerable to fraud. In 2021, 2.8 million people filed a fraud complaint to the Federal Trade Commission. The overall losses reached a staggering $5.8 billion. Yet consumers are not the main party to suffer from fraud since each instance of fraud hits related financial organizations even harder. That's why financial providers seek ways to detect and prevent risks early on.

Fortunately, besides creating vulnerabilities, big data also provides solutions to deal with them. Financial fraud detection using machine learning enables a financial institution to identify fake accounts, suspicious transactions, and payment frauds.

Would you like to learn more about payment fraud detection using ML? Read on to understand how it works.

What is fraud detection?

Fraud detection is using security measures to prevent third parties from obtaining funds or property through fraud. This process involves a manual check and/or automated verification of transaction details to spot any unusual activity that may be a sign of attack and block it. Fraud detection is the most widely used in banking and finances, insurance, healthcare, eCommerce, and other industries that collect lots of personal information and handle transactions.

Generally, fraud detection falls into several categories, as illustrated in the image below:

Even though there are many ways to verify financial transactions, financial fraud detection using machine learning algorithms is the most widely used approach. It's fast, cost-effective, productive, and has extensive use cases. Especially valuable is the use of ML for fraud detection in banking.

Use cases of fraud detection machine learning

Financial fraud detection using machine learning models is popular for a reason. It helps fight the most common types of fraud, including email phishing, identity theft, insurance claims, etc. Here are the problems fraud detection machine learning can help with:

• Email phishing. In a phishing attack, fraudsters email users fake messages and URLs that invite them to visit an attacker-controlled website and steal their credentials. Machine learning for fraud detection relies on classification and regression analysis to eliminate such messages.
• Identity theft. When a fraudster tries to steal a user's bank details, name, login credentials, and other personal information, fraud detection machine learning algorithms match the provided identity documents against databases to detect discrepancies. Machine learning also enables face recognition and biometrics scanning as additional security measures against identity theft.
• Insurance claims. Financial fraud detection using machine learning algorithms processes new claims to automatically detect fake or suspicious info. Hence, insurers don't need to check every request separately and can reliably handle more inquiries faster.
• Credit card fraud. Such fraud happens when attackers steal debit or credit card numbers via an unsecured internet connection to withdraw the funds. Using data science to prevent financial fraud, you can detect unusual transactions or user behavior changes to automatically alert banks of the threat.
• Mobile fraud. Fraud detection in banking using machine learning can identify unusual payment activity regardless of the end-user device. Hence, it's an effective way to secure mobile payments by protecting the transmitted personal information.

With fraud detection using machine learning in banking, it is now possible to better protect your customers and prevent financial losses. And this, in turn, is very important for many banking institutions.

The process of financial fraud detection using machine learning

First, let's clarify how machine learning works and why this technology helps with fraud detection.

Machine learning algorithms automatically find improvement possibilities based on the processed data. In particular, deep learning in fraud detection analyzes vast data sets to spot abnormalities that may be a sign of fraud.

Technology providers that build software with bank fraud detection using machine learning rely on the idea that fraudulent transactions have specific patterns (e.g., a new device, increased transaction amounts). These patterns allow ML algorithms to differentiate them from regular banking activities. Hence, when the risky pattern appears, machine learning algorithms trigger the required action.

Here's a step-by-step process of financial fraud detection using machine learning algorithms:

1. Information collection

The ML system needs to gather big data to have the foundation to learn from. Hence, you must have a base of users' records from the start and keep updating them as you go.

2. Abnormal patterns selection

Now you need to set what customer behaviors are good and what are suspicious. The abnormal financial transaction information allows the system to learn how to detect risky user activity. The patterns may include the user's identity, location, payment methods, number of orders, average order value, and other characteristics.

3. Training an algorithm

Set the rules to train your algorithm on how to differentiate regular user activity from fraudulent. The following section describes the models applied for financial fraud detection using machine learning techniques.

4. Creating a fraud detection model

After training, you will have the ML model ready to detect fraud. Mind that it's essential to keep updating the system as long as you use it to ensure its accuracy and adapt it to new security threats.

Models for fraud detection using machine learning

Fraud detection in financial services using ML has several models. Some machine learning models are more suitable for fraud detection than others. That's why we want to list the main models and algorithms you can apply to detect fraud and explain when to use each of them.

Supervised learning

Supervised learning is the most common way to implement fraud detection using machine learning in fintech. In this approach, the computer is trained based on the information labeled as good or bad. It means data pieces are already tagged with the correct answers.

This fraud detection machine learning model involves predictive data analysis, and its accuracy depends on the accuracy of training data. Hence, its main drawback is that if a fraud case is not present in historical data used for training, the model won't detect it.

Unsupervised learning

To improve financial fraud detection using machine learning, consider unsupervised learning techniques, among other approaches. An algorithm continuously processes and analyzes new untagged data to detect patterns and build a corresponding model. This model can identify unusual behavior when transaction data is limited or missing at all. It's fully autonomous, meaning no human intervention is required.

Semi-supervised learning

As you may have already guessed, semi-supervised learning is something in between supervised and unsupervised approaches. In this case, a fraud detection algorithm processes a small amount of labeled data with a large volume of unlabeled information. The semi-supervised approach is suitable when you cannot label information for any reason or labeling is too expensive.

Reinforcement learning

In the reinforcement learning approach, the system learns the optimal behavior in a specific environment for maximum reward. It interacts with the environment to observe how it responds and evaluate the feedback for further learning.

Best practices for machine learning fraud prevention from Binariks

As a financial software development company, Binariks has vast experience with machine learning implementation. Based on our expertise, we'd like to share some life hacks to help fintech companies implement ML predictive analytics and prevent fraud.

Consolidate data beforehand

Businesses and data scientists need to order all information first to detect financial fraud by using big data. Hence, be sure to connect data from multiple systems, standardize, and format it to facilitate machine learning and make the resulting model more accurate. Interoperability and smooth integration of your system's components should help with that.

Analyze end-to-end lifecycle

Analyze what actions people complete while using your services to see vulnerabilities. Such an initial analysis should help you figure out how to improve ML-based financial fraud detection. Once you spot the opportunities for fraudsters, you'll know what abnormal behaviors to look for.

Create a fraud-risk profile

Identify what types of fraud are likely to happen in your company and do a risk assessment to understand the most critical areas. This way, you can create a roadmap and gradually deal with all primary vulnerabilities to prevent fraud.

Educate users

Alert users when they engage in risky activities like using an insecure Internet connection or opening third-party emails. Simple notifications can do a lot to prevent fraud even before machine learning capabilities capture it.

Implement continuous auditing and updates

Regularly review your system after deployment, taking into account new fraud schemes, risks, and abnormal user behavior patterns. It will allow you to maintain a high level of accuracy and prevent fraud in a higher percentage of cases.

Consider Binariks your trusted partner

Binariks is a software development company providing a whole range of services, including fraud detection solutions for fintech using AI/ML, AWS consulting services , and machine learning fintech development. We can advise you on implementing machine learning to prevent fraud or do it for you. Binariks will evaluate your unique business needs to assemble a dedicated engineering team and develop ML-based predictive analytics features.

A secure messaging platform based on ID authentication and a cloud-based analytics dashboard are just a few of the machine learning projects we have completed. View more in our portfolio and contact us to discuss yours.