This dataset provides a detailed look into transactional behavior and financial activity patterns, ideal for exploring fraud detection and anomaly identification. It contains 2,512 samples of transaction data, covering various transaction attributes, customer demographics, and usage patterns. Each entry offers comprehensive insights into transaction behavior, enabling analysis for financial security and fraud detection applications.

Key Features:

• TransactionID: Unique alphanumeric identifier for each transaction.
• AccountID: Unique identifier for each account, with multiple transactions per account.
• TransactionAmount: Monetary value of each transaction, ranging from small everyday expenses to larger purchases.
• TransactionDate: Timestamp of each transaction, capturing date and time.
• TransactionType: Categorical field indicating 'Credit' or 'Debit' transactions.
• Location: Geographic location of the transaction, represented by U.S. city names.
• DeviceID: Alphanumeric identifier for devices used to perform the transaction.
• IP Address: IPv4 address associated with the transaction, with occasional changes for some accounts.
• MerchantID: Unique identifier for merchants, showing preferred and outlier merchants for each account.
• AccountBalance: Balance in the account post-transaction, with logical correlations based on transaction type and amount.
• PreviousTransactionDate: Timestamp of the last transaction for the account, aiding in calculating transaction frequency.
• Channel: Channel through which the transaction was performed (e.g., Online, ATM, Branch).
• CustomerAge: Age of the account holder, with logical groupings based on occupation.
• CustomerOccupation: Occupation of the account holder (e.g., Doctor, Engineer, Student, Retired), reflecting income patterns.
• TransactionDuration: Duration of the transaction in seconds, varying by transaction type.
• LoginAttempts: Number of login attempts before the transaction, with higher values indicating potential anomalies.

This dataset is ideal for data scientists, financial analysts, and researchers looking to analyze transactional patterns, detect fraud, and build predictive models for financial security applications. The dataset was designed for machine learning and pattern analysis tasks and is not intended as a primary data source for academic publications.

Fraud detected in Defence SA for 2022-23 Financial Year.

Dataset

This dataset was created by TienNguyen143

Contents

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Fraud Detection And Prevention Market Size 2025-2029

The fraud detection and prevention market size is forecast to increase by USD 122.65 billion, at a CAGR of 30.1% between 2024 and 2029.

The market is witnessing significant growth, driven by the increasing adoption of cloud-based services. Businesses are recognizing the benefits of cloud solutions, such as real-time fraud detection, scalability, and cost savings. Additionally, technological advancements in fraud detection and prevention solutions and services are enabling organizations to better protect their assets from sophisticated fraud schemes. However, the complex IT infrastructure of modern businesses poses a challenge in implementing and integrating these solutions effectively. The complexity of the IT infrastructure, which integrates cloud computing, big data, and mobile devices, creates a vast network of devices with insufficient security features.
To capitalize on market opportunities, companies must stay abreast of these trends and invest in advanced fraud detection technologies. Effective implementation and integration of these solutions, coupled with continuous innovation, will be crucial for businesses seeking to mitigate fraud risks and protect their reputation and financial stability. Furthermore, the constant evolution of fraud techniques necessitates continuous innovation and adaptation from solution providers. Encryption techniques and network security protocols form the foundation of robust cybersecurity defenses, while compliance regulations and penetration testing help identify vulnerabilities and strengthen security posture.

What will be the Size of the Fraud Detection And Prevention Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by the constant emergence of new threats and the need for advanced technologies to mitigate risks across various sectors. Real-time fraud alerts, anomaly detection systems, forensic accounting tools, and risk mitigation strategies are integrated into comprehensive solutions that adapt to the ever-changing fraud landscape. Entities rely on these tools to maintain regulatory compliance frameworks and incident response planning, ensuring access control management and vulnerability assessments are up-to-date. Machine learning algorithms and transaction monitoring tools enable the detection of suspicious activity, providing valuable insights into potential threats.

Intrusion detection systems and behavioral biometrics offer real-time protection against cyberattacks and payment fraud, while identity verification methods and risk scoring models help prevent account takeover and data loss. Cybersecurity threat intelligence and authentication protocols enhance the overall security strategy, providing a layered approach to fraud prevention. Fraud investigation techniques and loss prevention metrics enable entities to respond effectively to incidents and minimize the impact of data breaches. Social engineering countermeasures and payment fraud detection solutions further fortify the fraud prevention arsenal, ensuring continuous protection against evolving threats.

The ongoing dynamism of the market demands a proactive approach, with entities staying informed and agile to maintain a strong defense against fraudulent activities.

How is this Fraud Detection And Prevention Industry segmented?

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 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Solutions
  Services


End-user

  Large enterprise
  SMEs


Application

  Transaction monitoring
  Compliance and risk management
  Identity verification
  Behavioral analytics
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Russia
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

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, increasing regulatory compliance requirements, and the need to mitigate financial losses. Biometric authentication, encryption techniques, machine learning algorithms, and intrusion detection systems are among the key solutions driving market expansion. Regulatory frameworks, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), are mandating robust incident response planning, access control management, and data breach prevention strategies. Vulnerability as

Synthetic transactional data with labels for fraud detection. For more information, see: https://www.kaggle.com/ntnu-testimon/paysim1/version/2

1) Data Introduction • The Financial Payment Services Fraud Data Dataset is based on a real-world financial transaction simulation and was collected to detect fraudulent activities across various types of payments and transfers. It includes key financial data such as transaction time, type, amount, sender and recipient information, and account balances before and after each transaction. Each transaction is labeled as either fraudulent or legitimate.

2) Data Utilization (1) Characteristics of the Financial Payment Services Fraud Data Dataset: • With its large-scale transaction records, detailed account information, and diverse transaction types, this dataset is well-suited for developing and testing financial fraud detection models.

(2) Applications of the Financial Payment Services Fraud Data Dataset: • Real-time Fraud Detection: The dataset can be used to train machine learning classification models that quickly detect and prevent fraudulent transactions in real-world financial service environments. • Risky Transaction Pattern Analysis: By analyzing patterns according to transaction type, amount, and account, the dataset can support the advancement of fraud prevention policies and anomaly monitoring systems.

Lists instances of fraud over the last 5 years.

Dataset

This dataset was created by Shubham Joshi

Released under Data files © Original Authors

Contents

Financial Anti-Fraud Software Market Outlook

The global financial anti-fraud software market size was valued at USD 6.5 billion in 2023 and is projected to reach USD 15.8 billion by 2032, growing at a CAGR of 10.4% during the forecast period. The market is expected to witness significant growth driven by the increasing sophistication of cyber-attacks and the rising need for robust fraud detection mechanisms. Factors such as the rising digitization of financial transactions and stringent regulatory requirements are also contributing to the market's expansion.

One of the primary growth factors for the financial anti-fraud software market is the increasing sophistication of cyber-attacks. As cybercriminals employ more advanced techniques, organizations are compelled to adopt equally advanced systems to detect and prevent fraudulent activities. The use of artificial intelligence (AI) and machine learning (ML) in these software solutions has enabled real-time analysis and detection of anomalies, making it more difficult for fraudsters to succeed. Moreover, as financial institutions increasingly rely on digital channels, the exposure to potential security breaches has surged, necessitating advanced anti-fraud measures.

Another significant growth factor is the regulatory environment. Governments and regulatory bodies worldwide are implementing stringent policies to ensure the security of financial transactions. Compliance with these regulations requires financial institutions to adopt robust anti-fraud solutions. For instance, regulations like the General Data Protection Regulation (GDPR) in Europe and the Payment Card Industry Data Security Standard (PCI DSS) mandate rigorous data protection measures, which, in turn, drives the demand for advanced fraud detection software. The need for compliance not only mitigates risks but also builds customer trust.

Additionally, the rising digitization of financial services is a substantial growth driver. The shift from traditional banking methods to digital platforms has led to an increase in online transactions. While this transition offers convenience and efficiency, it also opens up new avenues for fraud. Financial institutions are investing heavily in anti-fraud software to safeguard their digital platforms. This includes mobile banking, online transactions, and even cryptocurrency exchanges. As digital financial activities continue to grow, the market for anti-fraud solutions is expected to expand correspondingly.

Fraud Risk Management Services play a crucial role in the financial sector by providing a comprehensive approach to identifying, assessing, and mitigating fraud risks. These services encompass a range of activities, including fraud risk assessments, the development of anti-fraud strategies, and the implementation of robust controls to prevent fraudulent activities. By leveraging data analytics and advanced technologies, fraud risk management services enable financial institutions to proactively detect and respond to potential threats. This proactive approach not only helps in minimizing financial losses but also enhances the overall security posture of organizations. As the financial landscape continues to evolve, the demand for specialized fraud risk management services is expected to rise, driven by the increasing complexity of fraud schemes and the need for compliance with regulatory requirements.

On the regional front, North America currently holds the largest market share, driven by the high adoption rate of advanced technologies and stringent regulatory requirements. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. Factors such as the rapid digitization of financial services, increasing internet penetration, and growing awareness about financial fraud are contributing to this growth. Countries like China and India are expected to be major contributors due to their large population base and increasing adoption of digital financial services.

Component Analysis

The financial anti-fraud software market can be segmented by component into software and services. The software segment holds the largest market share due to the increasing adoption of advanced fraud detection technologies by financial institutions. These software solutions incorporate advanced analytics, machine learning algorithms, and artificial intelligence to provide real-time fraud detection and prevention. Companies are continually investing in R&D to e

Credit Card Fraud Detection Dataset (European Cardholders, September 2013)

As a data contributor, I'm sharing this crucial dataset focused on the detection of fraudulent credit card transactions. Recognizing these illicit activities is paramount for protecting customers and the integrity of financial systems.

About the Dataset:

This dataset encompasses credit card transactions made by European cardholders during a two-day period in September 2013. It presents a real-world scenario with a significant class imbalance, where fraudulent transactions are considerably less frequent than legitimate ones. Out of a total of 284,807 transactions, only 492 are instances of fraud, representing a mere 0.172% of the entire dataset.

Content of the Data:

Due to confidentiality concerns, the majority of the input features in this dataset have undergone a Principal Component Analysis (PCA) transformation. This means the original meaning and context of features V1, V2, ..., V28 are not directly provided. However, these principal components capture the variance in the underlying transaction data.

The only features that have not been transformed by PCA are:

• Time: Numerical. Represents the number of seconds elapsed between each transaction and the very first transaction recorded in the dataset.
• Amount: Numerical. The transaction amount in Euros (€). This feature could be valuable for cost-sensitive learning approaches.

The target variable for this classification task is:

• Class: Integer. Takes the value 1 in the case of a fraudulent transaction and 0 otherwise.

Important Note on Evaluation:

Given the substantial class imbalance (far more legitimate transactions than fraudulent ones), traditional accuracy metrics based on the confusion matrix can be misleading. It is strongly recommended to evaluate models using the Area Under the Precision-Recall Curve (AUPRC), as this metric is more sensitive to the performance on the minority class (fraudulent transactions).

How to Use This Dataset:

1. Download the dataset file (likely in CSV format).
2. Load the data using libraries like Pandas.
3. Understand the class imbalance: Be aware that fraudulent transactions are rare.
4. Explore the features: Analyze the distributions of 'Time', 'Amount', and the PCA-transformed features (V1-V28).
5. Address the class imbalance: Consider using techniques like oversampling the minority class, undersampling the majority class, or using specialized algorithms designed for imbalanced datasets.
6. Build and train binary classification models to predict the 'Class' variable.
7. Evaluate your models using AUPRC to get a meaningful assessment of performance in detecting fraud.

Acknowledgements and Citation:

This dataset has been collected and analyzed through a research collaboration between Worldline and the Machine Learning Group (MLG) of ULB (Université Libre de Bruxelles).

When using this dataset in your research or projects, please cite the following works as appropriate:

• 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.
• Andrea Dal Pozzolo. Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi).
• Fabrizio Carcillo, Andrea Dal Pozzolo, Yann-Aël Le Borgne, Olivier Caelen, Yannis Mazzer, Gianluca Bontempi. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier.
• Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Gianluca Bontempi. 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...

Medicaid Fraud Control Units (MFCU or Unit) investigate and prosecute Medicaid fraud as well as patient abuse and neglect in health care facilities. OIG certifies, and annually recertifies, each MFCU. OIG collects information about MFCU operations and assesses whether they comply with statutes, regulations, and OIG policy. OIG also analyzes MFCU performance based on 12 published performance standards and recommends program improvements where appropriate.

These synthesized online fraud detection data sets are used to evaluate different machine learning methods. All features in the data sets, except the amount of transaction and classifier, are masked using a PCA transformation. Classifier is the first column and different instances have different ratios between fraudulent and normal transactions.

This Datasets consists of 2 csv files both containing information on ecommerce transactions made by customers. To detect Fraud using this data one needs to perform proper EDA and feature engineering to obtain good results. This is what makes it the perfect dataset to practice Data Analysis, Feature Engg. and Machine Learning. Do check it out!! Thanks, Aryan Rastogi

Annual data on the nature of fraud and computer misuse offences. Data for the year ending March 2021 and March 2022 are from the Telephone-operated Crime Survey for England and Wales (TCSEW).

Healthcare Fraud Detection Market Outlook

The global healthcare fraud detection market size was valued at approximately USD 2.5 billion in 2023 and is projected to reach around USD 10.8 billion by 2032, exhibiting a robust Compound Annual Growth Rate (CAGR) of 17.8% during the forecast period. This impressive growth trajectory can be attributed to the increasing sophistication of fraudulent activities in the healthcare sector, coupled with the advancement in data analytics technologies. As healthcare systems worldwide become more digitized, the need for robust fraud detection solutions is becoming critical, leading to market expansion and adoption of innovative detection technologies.

One of the primary factors driving the growth of the healthcare fraud detection market is the increasing incidence of healthcare fraud, which results in significant financial losses for healthcare organizations and insurance companies. Fraudulent claims and billing activities are rampant, costing billions annually, thus necessitating the deployment of advanced fraud detection mechanisms. Additionally, the healthcare sector's transition towards electronic health records and digital payment systems has exposed vulnerabilities that fraudsters can exploit, further fuelling the demand for comprehensive fraud detection solutions. Governments and healthcare organizations are increasingly investing in fraud detection technologies to safeguard financial resources and ensure the integrity of healthcare systems.

Another growth factor is the rapid advancement in data analytics and artificial intelligence (AI) technologies, which are revolutionizing the way fraud is detected and prevented in the healthcare industry. The deployment of machine learning algorithms and predictive analytics enables real-time monitoring and identification of suspicious activities, thereby significantly enhancing the efficiency and effectiveness of fraud detection measures. By leveraging big data analytics, organizations can proactively analyze vast amounts of data to detect patterns and anomalies indicative of fraud, reducing the reliance on manual processes and minimizing the scope for human error.

The stringent regulations and policies imposed by governments and regulatory bodies worldwide to combat healthcare fraud are also contributing to the market's growth. Compliance with these regulations necessitates the implementation of robust fraud detection solutions, thereby driving market demand. Regulatory frameworks often mandate healthcare providers and insurance companies to have systems in place that can detect and report fraudulent activities, creating a fertile ground for the growth of the healthcare fraud detection market. Moreover, public awareness campaigns and initiatives aimed at educating stakeholders about the importance of fraud detection further propel market expansion.

Component Analysis

The healthcare fraud detection market is segmented by component into software and services, with each segment playing a critical role in the overall functioning and effectiveness of fraud detection systems. The software segment, accounting for the largest market share, is driven by the continuous development and adoption of advanced analytics software designed to identify and mitigate fraudulent activities. Fraud detection software offers features such as anomaly detection, pattern recognition, and predictive analytics, empowering healthcare organizations to efficiently monitor and analyze data for potential fraud. The growing integration of AI and machine learning technologies into fraud detection software is further enhancing its capabilities, driving demand in this segment.

The services segment is experiencing substantial growth as healthcare organizations increasingly seek expert guidance and support in implementing and managing fraud detection systems. This segment includes professional services, such as consulting, training, and support services, which are essential for the successful deployment and operation of fraud detection solutions. Service providers offer tailored solutions and expertise to help organizations navigate the complexities of fraud detection, ensuring systems are effectively integrated and utilized. As the complexity and volume of healthcare data continue to rise, the demand for specialized services to support fraud detection initiatives is anticipated to grow.

The continuous evolution of software solutions, coupled with the increasing reliance on data-driven decision-making in healthcare, is expected to drive the growth of the software segment. Software developers are focusing on enhan

sparshb4tra/fraud-detection-data dataset hosted on Hugging Face and contributed by the HF Datasets community

Fraud Detection and Prevention (FDP) Software Market Outlook

In 2023, the global market size for Fraud Detection and Prevention (FDP) software is projected to be valued at approximately USD 25 billion. This burgeoning market is anticipated to escalate with a compound annual growth rate (CAGR) of 11% from 2024 to 2032, reaching an estimated USD 58 billion by the end of the forecast period. The proliferation of digital transactions, coupled with the increasing sophistication of cyber threats, is propelling the adoption of FDP solutions across various industry sectors. The market's growth is further fueled by an escalating demand for advanced analytics and machine learning technologies, which are integral to modern fraud detection mechanisms.

The burgeoning volume of online transactions, driven by the rapid uptake of e-commerce and digital payment solutions, is one of the primary growth factors of the FDP software market. As businesses transition to digital platforms, they face heightened exposure to fraud risks, necessitating robust fraud detection solutions. The expansion of the e-commerce sector has particularly intensified the need for comprehensive digital security strategies, as fraudulent activities such as identity theft, payment fraud, and account takeovers become increasingly prevalent. FDP software, leveraging advanced algorithms and real-time analytics, plays a pivotal role in mitigating such risks, thereby safeguarding businesses and consumers alike.

Moreover, the increasing regulatory pressures worldwide are another significant driver for the FDP software market. Governments and regulatory bodies are intensifying their focus on data protection and financial integrity, mandating businesses to implement stringent fraud prevention measures. Compliance with regulations such as the GDPR in Europe and CCPA in California demands sophisticated fraud detection systems to ensure data privacy and security. Consequently, businesses are increasingly investing in FDP solutions to not only protect themselves from fraud but also to remain compliant with evolving legal requirements.

Furthermore, technological advancements in artificial intelligence and machine learning are revolutionizing the fraud detection landscape, contributing to market growth. These technologies enable the development of intelligent systems capable of identifying suspicious activities with greater accuracy and speed. Machine learning models can learn from historical data to predict potential fraudulent activities, thus allowing businesses to proactively address security threats. The integration of AI in FDP solutions enhances their ability to adapt to new and ever-evolving fraud tactics, ensuring continuous protection for enterprises across various sectors.

Regionally, North America holds a significant share of the FDP software market, primarily due to the high adoption of advanced technologies and the presence of key market players. The region's strong financial infrastructure and the prevalence of online transactions further boost the demand for FDP solutions. The Asia Pacific region is poised for the highest growth rate during the forecast period, driven by digital transformation initiatives across emerging economies and increasing internet penetration. In Europe, stringent data protection regulations and a high concentration of e-commerce activities are driving the adoption of FDP software. Latin America and the Middle East & Africa are also witnessing growing interest in fraud prevention solutions, although these regions are still developing in terms of technological infrastructure.

Component Analysis

In the Fraud Detection and Prevention software market, the component segment is bifurcated into software and services. The software component is further sub-divided into various types of applications and platforms that cater to different aspects of fraud detection, such as identity verification, transaction monitoring, and behavioral analysis. The software division constitutes the lion's share of the market, as businesses prioritize robust technological solutions to combat sophisticated fraud techniques. These software solutions leverage machine learning, data analytics, and artificial intelligence to deliver real-time insights and predictive analytics, which are essential for identifying and mitigating fraudulent activities swiftly.

On the other hand, the services component encompasses support and maintenance services, consulting, and training. These services are critical for the effective deployment and functioning of FDP software solutions. Service providers offer expertise

Dataset

This dataset was created by Tài Đỗ Như

Released under Apache 2.0

Contents

Fraud Datasets Collection

Dataset

This dataset was created by Balaram Panigrahy

Contents

Context

This data-set contains >300,000 anonymized transactions. The variables are anonymized to protect the consumers information but they represent fields such as how long has the consumer had the account in a way which protects the information. Each row represents a users transaction. This data-set was built so that using the classifier you can build a model which can use the anonymized variables to predict which transactions are potentially fraudulent.

Content

The data-set contains a fraud rate of ~0.1% and thus is highly unbalanced.

The variables are as follows: Time, anonymized variables (30 variables), $ Amount, Class (Fraud Classifier)

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?