Explore our anonymized card fraud detection dataset, perfect for developing robust machine learning models.

Dataset

This dataset was created by Deepika A

Released under Apache 2.0

Contents

The datasets contains transactions made by credit cards in September 2013 by European cardholders. This dataset present transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are ‘Time’ and ‘Amount’. Feature ‘Time’ contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature ‘Amount’ is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature ‘Class’ is the response variable and it takes value 1 in case of fraud and 0 otherwise.

The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on http://mlg.ulb.ac.be/BruFence and http://mlg.ulb.ac.be/ARTML.

The resources for this dataset can be found at https://www.openml.org/d/1597

Author: Andrea Dal Pozzolo, Olivier Caelen and Gianluca Bontempi Source: Credit card fraud detection - Date 25th of June 2015 Please cite: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015

Below is a draft DMP–style description of your credit‐card fraud detection experiment, modeled on the antiquities example:

1. Dataset Description

Research Domain
This work resides in the domain of financial fraud detection and applied machine learning. We focus on detecting anomalous credit‐card transactions in real time to reduce financial losses and improve trust in digital payment systems.

Purpose
The goal is to train and evaluate a binary classification model that flags potentially fraudulent transactions. By publishing both the code and data splits via FAIR repositories, we enable reproducible benchmarking of fraud‐detection algorithms and support future research on anomaly detection in transaction data.

Data Sources
We used the publicly available credit‐card transaction dataset from Kaggle (original source: https://www.kaggle.com/mlg-ulb/creditcardfraud), which contains anonymized transactions made by European cardholders over two days in September 2013. The dataset includes 284 807 transactions, of which 492 are fraudulent.

Method of Dataset Preparation

1. Schema validation: Renamed columns to snake_case (e.g. transaction_amount, is_declined) so they conform to DBRepo’s requirements.

2. Data import: Uploaded the full CSV into DBRepo, assigned persistent identifiers (PIDs).

3. Splitting: Programmatically derived three subsets—training (70%), validation (15%), test (15%)—using range‐based filters on the primary key actionnr. Each subset was materialized in DBRepo and assigned its own PID for precise citation.

4. Cleaning: Converted the categorical flags (is_declined, isforeigntransaction, ishighriskcountry, isfradulent) from “Y”/“N” to 1/0 and dropped non‐feature identifiers (actionnr, merchant_id).

5. Modeling: Trained a RandomForest classifier on the training split, tuned on validation, and evaluated on the held‐out test set.

2. Technical Details

Dataset Structure

• The raw data is a single CSV with columns:

  • actionnr (integer transaction ID)

  • merchant_id (string)

  • average_amount_transaction_day (float)

  • transaction_amount (float)

  • is_declined, isforeigntransaction, ishighriskcountry, isfradulent (binary flags)

  • total_number_of_declines_day, daily_chargeback_avg_amt, sixmonth_avg_chbk_amt, sixmonth_chbk_freq (numeric features)

Naming Conventions

• All columns use lowercase snake_case.

• Subsets are named creditcard_training, creditcard_validation, creditcard_test in DBRepo.

• Files in the code repo follow a clear structure:

  ├── data/         # local copies only; raw data lives in DBRepo 
  ├── notebooks/Task.ipynb 
  ├── models/rf_model_v1.joblib 
  ├── outputs/        # confusion_matrix.png, roc_curve.png, predictions.csv 
  ├── README.md 
  ├── requirements.txt 
  └── codemeta.json 
  

Required Software

• Python 3.9+

• pandas, numpy (data handling)

• scikit-learn (modeling, metrics)

• matplotlib (visualizations)

• dbrepo‐client.py (DBRepo API)

• requests (TU WRD API)

Additional Resources

• Original dataset: https://www.kaggle.com/mlg-ulb/creditcardfraud

• Scikit-learn docs: https://scikit-learn.org/stable

• DBRepo API guide: via the starter notebook’s dbrepo_client.py template

• TU WRD REST API spec: https://test.researchdata.tuwien.ac.at/api/docs

3. Further Details

Data Limitations

• Highly imbalanced: only ~0.17% of transactions are fraudulent.

• Anonymized PCA features (V1–V28) hidden; we extended with domain features but cannot reverse engineer raw variables.

• Time‐bounded: only covers two days of transactions, may not capture seasonal patterns.

Licensing and Attribution

• Raw data: CC-0 (per Kaggle terms)

• Code & notebooks: MIT License

• Model artifacts & outputs: CC-BY 4.0

• DUWRD records include ORCID identifiers for the author.

Recommended Uses

• Benchmarking new fraud‐detection algorithms on a standard imbalanced dataset.

• Educational purposes: demonstrating model‐training pipelines, FAIR data practices.

• Extension: adding time‐series or deep‐learning models.

Known Issues

• Possible temporal leakage if date/time features not handled correctly.

• Model performance may degrade on live data due to concept drift.

• Binary flags may oversimplify nuanced transaction outcomes.

Dataset

This dataset was created by Kalyankaparaju01

Contents

Market Analysis for Credit Card Fraud Detection Platform The global credit card fraud detection platform market is estimated to reach USD 25.2 billion by 2033, growing at a CAGR of 14.3% from 2025 to 2033. The increasing adoption of digital payment methods, rising incidences of cybercrime, and stringent regulations on data security drive the market growth. The adoption of advanced technologies like machine learning and artificial intelligence in fraud detection solutions further fuels market expansion. The market is segmented into application (personal, enterprise) and type (manual screening, automatic screening). The enterprise segment dominates the market due to the growing demand for fraud protection in corporate environments. Automatic screening solutions are gaining popularity as they automate the fraud detection process, reducing operational costs and improving efficiency. Key market players include Kount, ClearSale, Stripe Radar, Riskified, Sift, SEON, Visa Advanced Authorization, Mastercard, Akkio, and Grid Dynamics. North America holds the largest market share due to the high adoption of advanced fraud detection technologies and the presence of major financial institutions in the region.

Dataset

This dataset was created by Piyush

Released under Apache 2.0

Contents

The dataset has been released by [1], which had been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of Université Libre de Bruxelles (ULB) on big data mining and fraud detection. [1] Pozzolo, A. D., Caelan, O., Johnson, R. A., and Bontempi, G. (2015). Calibrating Probability with Undersampling for Unbalanced Classification. 2015 IEEE Symposium Series on Computational, pp. 159-166, doi: 10.1109/SSCI.2015.33 open source kaggle : https://www.kaggle.com/mlg-ulb/creditcardfraud

The global credit card fraud detection platform market is experiencing robust growth, driven by the escalating volume of digital transactions and the increasing sophistication of fraud techniques. The market, valued at approximately $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This substantial growth is fueled by several key factors. The rising adoption of e-commerce and mobile payments creates a larger attack surface for fraudsters, necessitating advanced detection solutions. Furthermore, the increasing prevalence of sophisticated fraud schemes, such as synthetic identity theft and account takeover, demands more intelligent and adaptive fraud detection systems. The market is segmented by screening type (manual and automatic) and application (personal and enterprise), with automatic screening and enterprise applications driving the majority of growth due to their scalability and efficiency. The competitive landscape is dynamic, with established players like FICO, Mastercard, and Visa competing alongside innovative startups such as Forter and Feedzai. These companies continuously develop AI-powered solutions leveraging machine learning and big data analytics to identify and prevent fraudulent transactions effectively. Regional growth varies, with North America and Europe currently holding significant market share, but Asia-Pacific is expected to experience rapid expansion in the coming years due to rising digital adoption and economic growth in countries like India and China. The continued growth of the credit card fraud detection platform market hinges on several factors. The increasing demand for real-time fraud detection capabilities is driving the adoption of cloud-based solutions and the integration of advanced analytics. Regulatory compliance requirements, particularly around data privacy and security, also contribute to market growth. However, challenges remain. The cost of implementing and maintaining these sophisticated systems can be prohibitive for smaller businesses. Moreover, the constant evolution of fraud techniques necessitates ongoing investment in research and development to stay ahead of emerging threats. The market’s future trajectory will depend on the continued innovation in fraud detection technologies, the ability to adapt to evolving fraud tactics, and the successful integration of these solutions across various industries and geographies.

The global credit card fraud detection platform market is experiencing robust growth, driven by the escalating volume of digital transactions and the increasing sophistication of fraudulent activities. While precise figures for market size and CAGR are not provided, based on industry reports and observed trends, a reasonable estimation places the 2025 market size at approximately $15 billion. Considering the rapid adoption of advanced technologies like AI and machine learning in fraud detection, a conservative Compound Annual Growth Rate (CAGR) of 15% is projected for the forecast period (2025-2033). This growth is fueled by several factors, including the rising prevalence of e-commerce, the expanding adoption of mobile payments, and the increasing demand for robust security solutions from both personal and enterprise users. The market is segmented by screening type (manual and automatic) and application (personal and enterprise), with the automatic screening and enterprise segments expected to witness faster growth due to their efficiency and scalability. The competitive landscape is highly dynamic, with a mix of established players like Visa, Mastercard, and FICO, alongside innovative technology companies like Kount, Riskified, and Feedzai. These companies are continuously developing and deploying advanced algorithms and analytics to stay ahead of evolving fraud techniques. Regional growth varies, with North America and Europe currently holding significant market share, though Asia-Pacific is projected to exhibit rapid expansion due to increasing internet penetration and e-commerce adoption in developing economies. Challenges to market growth include the high cost of implementation and maintenance of these platforms, along with the need for continuous updates to counter evolving fraud tactics. However, the increasing financial losses incurred due to fraud are incentivizing businesses and consumers to invest in more sophisticated fraud detection solutions, thereby sustaining the market's upward trajectory.

AI-Based Fraud Detection Tools Market Outlook

The global AI-based fraud detection tools market size was valued at approximately USD 6.5 billion in 2023 and is projected to reach USD 22.8 billion by 2032, growing at a robust CAGR of 15.1% during the forecast period. The significant growth factors driving this market include the increasing sophistication of fraudulent activities, the growing adoption of AI and machine learning technologies in various sectors, and the heightened demand for real-time fraud detection solutions.

One of the primary growth factors for the AI-based fraud detection tools market is the rising complexity of fraudulent activities. In today's digital age, fraudsters are employing increasingly sophisticated techniques to breach security systems, making traditional detection methods inadequate. AI-based solutions, which leverage advanced algorithms and machine learning, are capable of analyzing large volumes of data to identify patterns and anomalies indicative of fraud. This capability is crucial for organizations seeking to protect their assets and maintain customer trust in an environment where cyber threats are continually evolving.

Another significant growth driver is the widespread adoption of AI and machine learning technologies across various industries. Businesses are recognizing the potential of these technologies to enhance their fraud detection capabilities, leading to increased investments in AI-driven solutions. The banking and financial services sector, in particular, has been at the forefront of adopting AI-based fraud detection tools to combat financial crimes such as identity theft, credit card fraud, and money laundering. Furthermore, the retail and e-commerce sectors are increasingly implementing these tools to safeguard against fraudulent transactions and account takeovers.

The growing demand for real-time fraud detection solutions is also propelling the market forward. Traditional fraud detection systems often rely on rule-based approaches that can be slow and reactive, allowing fraudulent activities to go undetected until significant damage has been done. In contrast, AI-based solutions can process and analyze data in real-time, enabling organizations to identify and respond to threats rapidly. This real-time capability is essential for minimizing losses and mitigating risks, particularly in sectors where the speed of transactions is critical, such as online retail and financial services.

Regionally, North America currently dominates the AI-based fraud detection tools market, owing to the high adoption rate of advanced technologies and the presence of major industry players. However, other regions like Asia Pacific and Europe are also experiencing significant growth. Asia Pacific, in particular, is expected to exhibit the highest CAGR during the forecast period, driven by the increasing digitization of economies, rising internet penetration, and the growing awareness of cybersecurity threats. Europe is also witnessing substantial growth due to stringent regulatory requirements and the increasing focus on data privacy and security.

Component Analysis

The AI-based fraud detection tools market can be segmented by component into software, hardware, and services. The software segment is expected to hold the largest market share during the forecast period. This dominance can be attributed to the continuous advancements in AI algorithms and machine learning models, which enhance the accuracy and efficiency of fraud detection systems. Furthermore, the software solutions are designed to be scalable and easily integrated into existing systems, making them an attractive option for organizations of all sizes.

Hardware components, though not as dominant as software, play a crucial role in the deployment of AI-based fraud detection systems. High-performance computing hardware, including GPUs and specialized AI processors, are essential for handling the large datasets and complex computations required for real-time fraud detection. As the demand for more powerful and efficient hardware grows, this segment is expected to see steady growth, particularly in large enterprises that require robust infrastructure to support their AI initiatives.

The services segment, encompassing consulting, integration, and maintenance services, is also poised for significant growth. Organizations often lack the in-house expertise required to develop and implement AI-based fraud detection systems, leading to an increased reliance on external service providers. These services help organizations to customize and opti

The size and share of this market is categorized based on Deployment Type (On-Premise, Cloud-Based) and Application (Transaction Monitoring, Customer Authentication, Fraud Analytics, Reporting and Monitoring, Risk Assessment) and End-User (Banks, Financial Institutions, E-commerce, Retail, Payment Processors) and geographical regions (North America, Europe, Asia-Pacific, South America, Middle-East and Africa).

Dataset

This dataset was created by Karthik

Contents

Snapshot img

Fraud Detection And Prevention Market Size 2024-2028

The fraud detection and prevention market size is forecast to increase by USD 86.68 billion at a CAGR of 27.17% between 2023 and 2028.

In the current business landscape, the market is experiencing significant growth due to several key factors. The increasing adoption of cloud infrastructure services, such as cloud computing and big data, is driving market expansion. These technologies enable organizations to store and process large volumes of data, which is essential for advanced fraud detection techniques like anomaly detection. Moreover, the healthcare services sector is increasingly relying on fraud detection solutions to safeguard sensitive patient data. In addition, the rise of business intelligence (BI) and machine-to-machine (M2M) services is leading to an increased need for robust fraud prevention measures. Phone-based authentication solutions are also gaining popularity as an effective method for securing user identities and preventing fraud. The technological advancement in fraud detection and prevention solutions and services, coupled with the complexity of IT infrastructure, is further fueling market growth.

What will be the Size of the Fraud Detection And Prevention Market During the Forecast Period?

Request Free Sample

The market encompasses a range of solutions designed to safeguard businesses and organizations from various types of financial and data breaches. Key end-use industries, including healthcare, manufacturing, governments, and IT , business intelligence and telecom, among others, increasingly rely on advanced technologies to mitigate risks. Market dynamics are driven by the growing adoption of cloud-based solutions, big data analytics, and blockchain technology. These innovations enable real-time fraud detection, enhancing the ability to prevent incidents such as payment fraud, identity theft, phishing scams, and money laundering. 
SMEs and large enterprises across sectors like travel and transportation, energy and utilities, media and entertainment, professional services, and insurance claims face similar challenges, making the market expansive and diverse. Authentication solutions, real-time fraud detection, and managed services are integral components of the market, catering to the evolving needs of businesses in an increasingly digital world.

How is this Fraud Detection And Prevention Industry segmented and which is the largest segment?

The fraud detection and prevention industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Solutions
  Services


End-user

  Large enterprise
  SMEs


Geography

  North America

    US
    Canada


  Europe

    Germany
    Spain
    UK


  APAC

    China
    Japan
    India


  South America

    South Africa


  Middle East and Africa

By Component Insights

The solutions segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth due to escalating cyber threats and the increasing need for robust security measures. Key drivers include the rising number of fraudulent activities such as identity theft, money laundering, and phishing scams, as well as economic uncertainty and the pandemic. In the solutions segment, authentication solutions have emerged as a major revenue generator. However, the high cost of biometric technology may hinder growth in this area. SMEs, healthcare, manufacturing, end-use enterprises, governments, IT and telecom, travel and transportation, energy and utilities, media and entertainment, and financial institutions are among the key industries investing in fraud detection and prevention. Digital technologies, including cloud-based solutions, Big Data, artificial intelligence, and machine learning, are increasingly being adopted for real-time fraud detection. Fraud complexity and online data transactions pose significant challenges, necessitating proactive measures and trained cybersecurity professionals.

Get a glance at the Fraud Detection And Prevention Industry report of share of various segments Request Free Sample

The Solutions segment was valued at USD 11.84 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 40% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The North American the market is projected to expand substantially due to the increasing prevalence of cyber threats in sectors like healthcare

Credit Card Generator Market Outlook

The global credit card generator market is projected to experience robust growth with a market size of approximately USD 580 million in 2023, and it is anticipated to reach USD 1.2 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 8.5%. The rising need for secure and efficient credit card testing tools, driven by the expansion of e-commerce and digital transactions, forms a significant growth catalyst for this market. As online retail and digital financial services burgeon, the demand for reliable credit card generators continues to escalate, underscoring the importance of this market segment.

One of the pivotal growth drivers for the credit card generator market is the increasing complexity and sophistication of online payment systems. As e-commerce platforms and digital payment solutions proliferate worldwide, there is a growing need for comprehensive testing tools to ensure the reliability and security of these systems. Credit card generators play a crucial role in this context by providing developers and testers with the means to simulate various credit card scenarios, thereby enhancing the robustness of payment processing systems. Additionally, the rise in cyber threats and fraud necessitates stringent testing, further propelling market growth.

Another significant factor contributing to the market's expansion is the growing emphasis on fraud prevention and security. Financial institutions and businesses are increasingly investing in sophisticated tools to combat fraud and secure financial transactions. Credit card generators offer a practical solution for testing the efficacy of anti-fraud measures and ensuring that security protocols are adequately robust. By enabling the simulation of fraudulent activities and various transaction scenarios, these tools help organizations better prepare for and mitigate potential security breaches.

Furthermore, the marketing and promotional applications of credit card generators are also driving market growth. Companies leveraging digital marketing strategies use these tools to create dummy credit card numbers for various promotional activities, such as offering free trials or discounts, without exposing real customer data. This capability not only aids in marketing efforts but also ensures compliance with data privacy regulations, thereby enhancing consumer trust and brand reputation. The versatility of credit card generators in supporting both operational and marketing functions underscores their growing importance in the digital age.

Regionally, North America holds a significant share of the credit card generator market, driven by the high penetration of digital payment systems and advanced cybersecurity measures in the region. The presence of numerous financial institutions and technology companies further bolsters the market in North America. Meanwhile, Asia Pacific is expected to witness the fastest growth, fueled by the rapid digitalization of economies, increasing internet penetration, and burgeoning e-commerce activities. Europe also presents substantial opportunities due to stringent data protection regulations and the widespread adoption of digital transaction systems.

Type Analysis

The credit card generator market can be segmented by type into software and online services. Software-based credit card generators are widely used by developers and testers within organizations to simulate credit card transactions and validate payment processing systems. These tools are typically integrated into the development and testing environments, providing a controlled and secure platform for generating valid credit card numbers. The demand for software-based generators is driven by their ability to offer customizable options and advanced features, such as bulk generation and API integration, which enhance the efficiency of testing processes.

Online services, on the other hand, cater to a broader audience, including individual users, small businesses, and marketers. These services are accessible via web platforms and provide an easy-to-use interface for generating credit card numbers for various purposes, such as testing, fraud prevention, and marketing promotions. The growing popularity of online credit card generators can be attributed to their convenience, accessibility, and the increasing need for temporary and disposable credit card numbers in the digital economy. These services are particularly useful for busin

Credit Card Fraud Detection Dataset (Updated)

This dataset contains 284,807 transactions from a credit card company, where 492 transactions are fraudulent. The data is highly imbalanced, with only a small fraction of transactions being fraudulent. The dataset is commonly used to build and evaluate fraud detection models.

Dataset Details:

• Number of Transactions: 284,807
• Fraudulent Transactions: 492 (Highly Imbalanced)
• Features:
  • 28 anonymized features (V1 to V28)
  • Transaction amount
  • Timestamp
• Label:
  • 0: Legitimate
  • 1: Fraudulent

Data Preprocessing:

• SMOTE (Synthetic Minority Oversampling Technique) has been applied to address the class imbalance in the dataset, generating synthetic examples for the minority class (fraudulent transactions).
• Additional Operations: Various preprocessing steps were performed, including data cleaning and feature engineering, to ensure the quality of the dataset for model training.

Processed Files:

The dataset has been split into training and testing sets and saved in the following files: - X_train.csv: Feature data for the training set - X_test.csv: Feature data for the testing set - y_train.csv: Labels for the training set (fraudulent or legitimate) - y_test.csv: Labels for the testing set

This updated dataset is ready to be used for training and evaluating machine learning models, specifically designed for credit card fraud detection tasks.

This description highlights the key aspects of the dataset, including its preprocessing steps and the availability of the processed files for ease of use.

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

About Data

The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.

Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.

Acknowledgements

The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project

Please cite the following works:

Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015

Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon

Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE

Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)

Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier

Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing

Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019

Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019

Yann-Aël Le Borgne, Gianluca Bontempi Reproducible machine Learning for Credit Card Fraud Detection - Practical Handbook

Bertrand Lebichot, Gianmarco Paldino, Wissam Siblini, Liyun He, Frederic Oblé, Gianluca Bontempi Incremental learning strategies for credit cards fraud detection, IInternational Journal of Data Science and Analytics

Online Fraud Detection Market size was valued at USD 45.6 Billion in 2024 and is projected to reach USD 254.93 Billion by 2031, growing at a CAGR of 24.2% from 2024 to 2031.

The Online Fraud Detection Market is driven by the escalating incidence of cyber fraud and financial crimes, which are prompting organizations to adopt advanced fraud detection solutions. The increasing sophistication of cyber-attacks, including phishing, identity theft, and account takeovers, necessitates robust fraud prevention mechanisms. The proliferation of digital transactions and the growth of e-commerce further heighten the need for effective fraud detection systems. Regulatory requirements and compliance mandates are also pushing companies to implement stringent fraud detection and prevention measures. Technological advancements in artificial intelligence, machine learning, and big data analytics are enhancing the accuracy and efficiency of fraud detection systems, making them more capable of identifying and mitigating fraudulent activities in real-time. Additionally, the rise in mobile banking and the expanding digital economy are contributing to the increased demand for online fraud detection solutions.

The online fraud protection market is experiencing robust growth, driven by the increasing sophistication of cybercrime and the expanding digital economy. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $45 billion by 2033. This expansion is fueled by several key factors. The rising adoption of e-commerce and online banking creates more opportunities for fraudulent activities, demanding robust protection measures. Furthermore, the increasing frequency and severity of data breaches are prompting both consumers and enterprises to invest heavily in fraud prevention solutions. The market is segmented by application (consumer and enterprise) and type (credit monitoring, identity monitoring, and others). The consumer segment currently holds a larger market share, but the enterprise segment is anticipated to exhibit faster growth due to the rising need for robust security infrastructure in businesses. Key players in this market include established names like NortonLifeLock, Experian, Equifax, and TransUnion, along with emerging technology providers specializing in AI-powered fraud detection. Regional variations exist, with North America and Europe currently dominating the market share, but significant growth is expected from Asia-Pacific regions like India and China as their digital economies mature. However, the market faces challenges such as the rising cost of advanced fraud detection technologies and the evolving nature of cyber threats, requiring continuous innovation to stay ahead of the curve. The competitive landscape is dynamic, with both established players and innovative startups vying for market share. The increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies is transforming the fraud detection landscape, leading to more effective and proactive solutions. This technological advancement is driving the development of more sophisticated and personalized fraud protection services, catering to the specific needs of different customer segments. Regulatory changes and increasing consumer awareness of data privacy are also shaping the market. Companies are focusing on compliance with data protection regulations and building trust with their customers through transparent data handling practices. This coupled with the ongoing need for enhanced security measures, even with evolving fraudulent techniques, reinforces the long-term growth potential of this critical sector.

The fraud detection and prevention (FDP) market was estimated to be worth 19.5 billion U.S. dollars in 2017. The projection for the market in 2023 exceeded 63 billion U.S. dollars. Firms offer FDP methods to prevent fraudulent insurance claims, identity theft, and money laundering.

How much fraud exists?

As of October 2018, around 23 percent of internet users have been a victim of online identity theft. These crime activities can be in the form of credit card fraud, tax related issues, or bank fraud, among other issues. While wire transfers still account for the highest value of fraud loss, technology-enabled frauds such as card-not-present (CNP) credit card fraud are increasingly common.

Other forms of fraud

When financial fraud is mentioned, it is sometimes associated with identity theft or Ponzi schemes like that carried out by Bernie Madoff. However, the most common economic crime reported is asset misappropriation, simply stealing something. Bribery, accounting fraud, and insider trading are also possible infringements. FDP vendors such as IBM, Oracle, SAP, and FICO watch against these, trying to stay one step ahead of the criminals.