In 2025, the challenge of credit card fraud prevention for businesses is more relevant than ever. According to Business Wire, the projected global losses from credit card fraud are expected to reach a staggering USD 43 billion by 2026. The fraud prevention industry is booming, according to Juniper Research, as spending on fraud detection and prevention solutions is expected to reach USD 32.2 billion by 2029.

Here, at SPD Technology, we deliver powerful credit card fraud prevention software that protects the money and reputation of our global clients. Before looking at one of our projects, let’s start by discussing the basics of credit card fraud.

Understanding Fraud Detection: Key Questions Answered

Ensuring data security in fintech applications and systems of all kinds has always been a significant concern for software developers. Let’s run through a quick overview of essential things you should know about the modern ways of battling financial fraud. 

What is a Fraud Detection System?

It is a software solution designed to identify and prevent fraudulent activities in real-time or after transactions have occurred. It uses a combination of rules-based logic, statistical analysis, and machine learning algorithms to detect suspicious behavior or anomalies that may indicate fraud.

What are Fraud Detection Predictive Models?

They are machine learning or statistical models that are used to predict the likelihood that a transaction or activity is fraudulent before it is completed or flagged. Anomaly detection machine learning algorithms, which are at the core of these models, leverage historical data to recognize patterns associated with fraud. Among the main types of predictive models are:

  • Logistic Regression
  • Decision Trees and Random Forests
  • Gradient Boosting Machines (e.g., XGBoost)
  • Neural Networks
  • Variational Autoencoders

What Are the Most Prominent Fraud Detection Use Cases?

The list of possible implementations of machine learning to fight against criminal activities is constantly growing, and it is not feasible to cover all deserving examples here. However, we will mention the most interesting cases of fraud detection with machine learning to provide you with a perspective:

The Most Prominent Fraud Detection Use Cases
The Most Prominent Fraud Detection Use Cases
  • eCommerce Transaction Fraud: Focuses on detecting and blocking fraudulent online purchases using stolen credentials, suspicious IPs, or unusual buying patterns. In eCommerce fraud detection, machine learning helps identify anomalies in user behavior in real-time.
  • Money Laundering Detection (AML): Monitors and analyzes financial transactions to identify suspicious patterns consistent with money laundering, such as layering or structuring. Anti money laundering regulations are an important part of the Know Your Customer (KYC) standards in financial services. 
  • Insurance Fraud: Identifies false claims, inflated damages, or staged incidents by analyzing claim histories, customer behavior, and patterns across similar cases.
  • Lending and Loan Application Fraud: Detects identity theft, synthetic identities, and misrepresented financial data during loan applications.
  • Emerging Use Case: Buy Now Pay Later (BNPL) Fraud: Prevents misuse of BNPL services through stolen identities or coordinated fraud rings.
  • Core Use Case: Credit Card Fraud Prevention: Identifies unauthorized or suspicious credit card transactions by tracking location, transaction volume, and purchase patterns. It is difficult to overestimate the impact of credit card fraud detection using ML, as it is possible to build solutions with near-instant detection and response.

Anomaly Detection Solution for eCommerce Credit Card Transactions by SPD Technology

We excel at building outstanding custom software, earning us a place among the best fintech development companies. Let’s take a closer look at one of our most prominent credit card frauds detection projects. 

  • Development time – 3 months
  • Team size – 6 experts
  • Platform – Web

Overview of the Credit Card Fraud Detection Project

SPD Technology was contacted by an eCommerce and financial services company that offers products and services that can be paid for using mobile money or a bank card, with the request of making their platform a safer place for online transactions for their customers.

Along with the increase in the number of customers who faced issues with their money suddenly disappearing or being transferred to an unknown account, our client considered implementing a modern fraud prevention method for their platform. Therefore, they contacted us and decided to rely on our custom AI solution development services.

We have profound experience in working with innovative technologies such as Artificial Intelligence and machine learning, excelling in MLOps solutions. Recently, SPD Technology helped a leading financial data provider that covers the global venture capital, private equity, and public markets. Our experts were able to deliver AI features for promising markets spotting and evolving trends detection within a 6-month timeframe, helping to achieve a 50% decrease in manual data processing workload.

Another fascinating project is our scaling of Morningstar’s financial platform with AI and cloud. Our engineers delivered a 10x reduction in data storage costs and optimized data processing time, automated data collection from over 500 websites, and achieved a 10x improvement in processing power and stability, among other benefits.

There is another prominent project we should mention, in which we provided merchant and transaction risk management features development for Poynt, helping the client acquire thousands of customers soon after the product’s release.

Challenges

  • Sparse Transaction History Problem: Traditional fraud models rely on user behavior over time, but performance drops significantly for users with very few transactions, such as new or infrequent customers. Applying the same model for both high-activity users and low-activity ones led to inconsistent scoring and reduced detection accuracy due to insufficient behavioral signals.
  • Oversimplified Identity-Only Modeling: Treating low-activity users as accounts with no transaction history provided more stable results, but at the cost of underfitting and ignoring valuable, albeit limited, behavioral data.
  • Limitations of Few-Shot Learning: Exploring few-shot learning, designed to work with minimal data, showed promise in theory but didn’t deliver the expected improvements in early Proof-of-Concept (PoC) testing. Real progress came from deepening our understanding of the client’s business logic, which led to crafting domain-driven features that significantly improved the model’s performance.
Serhii Leleko: ML & AI Engineer at SPD Technology

Serhii Leleko

ML & AI Engineer at SPD Technology

“So, the most complicated part of the solution was to achieve good metrics for users who have made only a few transactions. We proceeded with experimenting and diving into our client’s business domain, which allowed us to develop features that could generalize well even with minimal user history. Because of the domain features, our main score improved by more than 15%, and it became the production solution.”

Solution

We used classification rather than classical anomaly detection methods. After an intense feature generation phase (resulting in approximately 700 features in total), they proceeded to feature selection to choose only the most relevant ones. Finally, it was a blend of classification methods, such as XGBoost, Catboost, and LightGBM that got us close to the desired score.

Credit Card Fraud Detection Dataset

The dataset used for a fraud detection system for our client included a sample of approximately 140,000 transactions that occurred between October 2021 and April 2024. 

One of the fintech applications development challenges regarding fraud detection is that the data is highly imbalanced. There were around 130,000 everyday transactions, and only 6% of them were fraudulent. We addressed the problem of an imbalanced dataset using various techniques, including data oversampling (augmenting the existing data samples) and data sample generation.

Credit Card Fraud Detection Algorithm

Once the machine learning-driven fraud protection module was integrated into the eCommerce platform, it started tracking the transactions. Whenever a user requests a transaction, it is processed for a certain amount of time. 

Depending on the level of predicted fraud probability, there are three kinds of possible outputs:

  • If the probability is less than 10%, the transaction is allowed
  • If the probability is between 10% and 80%, an additional authentication factor should be applied.
  • If the probability is greater than 80%, the transaction is frozen and should be processed manually.

The model estimates the probability of a fraudulent transaction based on the following transaction information: date and time, product category, amount, provider (seller), client information, agent information, location, and the client’s behavioral patterns. Contextual and aggregated data is produced by a machine learning engineer based on the previously mentioned data.

Technology for Credit Card Fraud Detection Software

Our machine learning-driven fraud detection system combines proven supervised learning models and advanced feature engineering to ensure both accuracy and scalability.

We choose XGBoost, LightGBM, and CatBoost as our machine learning classification models for fraud probability scoring based on tabular transaction data, because these models remain highly efficient for structured financial data.

As for the anomaly detection layer, our team leveraged complementary unsupervised models to detect emerging and unknown fraud patterns, including:

  • Autoencoders for high-dimensional anomaly detection.
  • Isolation Forest improved for extreme outliers.
  • Representation Learning is better suited to capture complex relationships between user behavior and transaction context.

As mentioned previously, over 700 behavioral and transactional features were generated, including:

  • Transaction velocity (frequency and recency)
  • Geolocation anomalies
  • Device fingerprinting data
  • User behavioral biometrics (where applicable)
  • Aggregated merchant and product category risk profiles

We addressed class imbalance using oversampling, SMOTE-like synthetic data generation, and targeted sampling strategies.

Serhii Leleko: ML & AI Engineer at SPD Technology

Serhii Leleko

ML & AI Engineer at SPD Technology

“After the deployment, we provided our client with a real-time fraud scoring API, ongoing model monitoring to track data drift and evolving fraud patterns, as well as scheduled retraining using newly collected transaction data to adapt to emerging fraud schemes.”

Results

Our team successfully delivered a cutting-edge AI credit card fraud detection solution, adhering to the strictest demands of payment processing compliance. The client’s eCommerce platform received several tangible benefits after only 6 months in production, including:

  • Reduced Fraud-Related Losses: The introduction of an advanced credit card fraud detection solution helped the client reduce the number of undetected fraud cases and save up to 40% of financial losses. 
  • Lowered Manual Review Workload: Intelligent risk scoring minimized false positives, cutting the number of transactions requiring manual review by more than half and easing pressure on the fraud operations team. 
  • Improved Checkout Success Rate: The decline in false positives resulted in fewer legitimate transactions being blocked, directly increasing successful checkout completions by approximately 10%.

Fraud Detection Machine Learning: What to Look for in 2025?

According to Statista, the Artificial Intelligence market is projected to reach USD 244.22 billion by the end of 2025, opening unprecedented opportunities for online payment fraud detection solutions. This year, you should be aware of the following trends:

Online Credit Card Fraud Detection
Fraud Detection Machine Learning: What to Look for in 2025
  • Continuous Learning to Adapt to Evolving Fraud Schemes: Static models, which are trained only once a year, are no longer viable since updates to credit card fraud checks are required daily. Companies will seek platforms that support real-time or near-real-time learning from new data.
  • Hybrid Models for Combining Supervised and Unsupervised Learning: This combination enhances flexibility and coverage. Supervised learning remains essential for leveraging labeled fraud data, but unsupervised techniques help uncover new, previously unseen fraud schemes.
  • Behavioral and Contextual Feature Engineering: Rather than relying solely on transactional fields like amount or location, modern credit card fraud detection platforms analyze user behavior patterns, device usage, session timing, and contextual cues.
  • Graph-Based Fraud Detection: This is a powerful technique that enables the detection of coordinated fraud rings and identity abuse. By modeling users, devices, and transactions as interconnected nodes, ML systems can detect subtle, non-obvious relationships that traditional models might miss.
  • Scalable, Cloud-Native ML Infrastructure: As transaction volumes continue to expand, there is a growing demand for credit card fraud detection using AI to scale up efficiently. Cloud-native architectures enable automated retraining, rapid deployment of new models, and seamless integration across platforms.

Partner with SPD Technology for Credit Card Fraud Detection Solutions Development

Developing a versatile credit card fraud management system from scratch and successfully implementing it requires deep domain expertise, technical proficiency, and flawless execution. Here, at SPD Technology, we help capitalize on the advantages of strategic technology consulting, proficient implementation, and consistent maintenance. 

We have experience in applied AI and machine learning, backed by numerous successful, cross-industrial projects. Throughout almost two decades of developing custom software, we have gained a deep understanding of domain-specific credit card fraud protection services. We use a hybrid modelling approach, combining supervised learning, unsupervised anomaly detection, and advanced techniques. Our experts possess in-depth expertise in data and feature engineering, offering full-cycle delivery capabilities that span the entire process.

Conclusion

Credit card fraud detection is becoming increasingly sophisticated, as AI-driven solutions are replacing traditional methods. At SPD Technology, we specialize in delivering advanced fraud detection systems that combine machine learning, deep domain knowledge, and full-cycle development expertise. Feel free to contact us at any time, and we will develop a customized solution tailored to your unique case.

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FAQ

  • How Does AI Detect Credit Card Fraud in Real Time?

    AI analyzes transaction data, including amount, location, time, and user behavior, in milliseconds to identify anomalies and assign a fraud risk score, allowing real-time decisions to block or approve transactions.