In 2023, 21,489 individuals in the United States reported encountering business e-mail compromise (BEC) scams. This figure has slightly increased in the last three years, with 19,954 reported victims in 2021 and, 21,832 in 2022.

During the fourth quarter of 2024, nearly 23 percent of phishing attacks worldwide targeted social media. Web-based software services and webmail were targeted by over 23 percent of registered phishing attacks. Furthermore, financial institutions accounted for 12 percent of attacks.

Introduction

Phishing Statistics: Phishing is a kind of cyberattack in which criminals try to fool people into sharing personal information such as passwords or credit card numbers, often by pretending to be a trusted company or person through fake emails, websites, or messages. Phishing has become more common as many people use the Internet for banking, shopping, and communication.

In 2024, phishing attacks are a major threat to both individuals and businesses. Criminals are using more advanced techniques, and these attacks are costing billions of dollars globally. People need to stay aware and cautious online to avoid falling victim to these scams.

A partial dataset and document-term matrix of phishing emails targeting an institution of higher education and an associated script used for data analysis.

This API is providing the information of press releases issued by the authorized institutions and other similar press releases issued by the HKMA in the past regarding fraudulent bank websites, phishing E-mails and similar scams information.

Provide telecommunications fraud case data (This data is preliminary statistics at the beginning of each quarter, for reference only, the accurate statistics are based on the annual crime statistics data of this department).

In 2023, individuals aged 60 and older in the United States filed 17,810 complaints about tech support fraud cases. Approximately 7,300 victims reported incidents of personal data breach. Romance fraud was also frequently encountered by victims aged 60 and older in the measured year. Overall, the number of complaints about cryptocurrency crimes almost doubled compared to 2022.

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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

In 2023, individuals over the age of 60 accounted for the highest number of recorded cyber crime victims in the United States. According to the latest data, more than 104,068 people reported cyber crimes in the year examined. The second-most targeted were individuals between 30 and 39 years, with over 88 thousand complaints.

The dataset 1 contains the age, qualification level, their awareness about phishing and if they became victim to phishing. The dataset 1 contains the result to detection rate before awareness and briefing of phishing after a successful spear phishing.

The dataset 2 contains the age, qualification level, their awareness about phishing and if they became victim to phishing. The dataset 2 contains the result to detection rate after awareness and briefing of phishing after a successful smishing.

Estimates from Crime Survey for England and Wales (CSEW) on fraud and computer misuse. Also data from Home Office police recorded crime on the number of online offences recorded by the police and Action Fraud figures broken down by police force area.

These tables were formerly known as Experimental tables.

Please note: This set of tables are no longer produced. All content previously released within these tables has, or will be, redistributed among other sets of tables.

In 2000, Enron was one of the largest companies in the United States. By 2002, it had collapsed into bankruptcy due to widespread corporate fraud. The data has been made public and presents a diverse set of email information ranging from internal, marketing emails to spam and fraud attempts.

In the early 2000s, Leslie Kaelbling at MIT purchased the dataset and noted that, though the dataset contained scam emails, it also had several integrity problems. The dataset was updated later, but it becomes key to ensure privacy in the data while it is used to train a deep neural network model.

Though the Enron Email Dataset contains over 500K emails, one of the problems with the dataset is the availability of labeled frauds in the dataset. Label annotation is done to detect an umbrella of fraud emails accurately. Since, fraud emails fall into several types such as Phishing, Financial, Romance, Subscription, and Nigerian Prince scams, there have to be multiple heuristics used to label all types of fraudulent emails effectively.

To tackle this problem, heuristics have been used to label the Enron data corpus using email signals, and automated labeling has been performed using simple ML models on other smaller email datasets available online. These fraud annotation techniques are discussed in detail below.

To perform fraud annotation on the Enron dataset as well as provide more fraud examples for modeling, two more fraud data sources have been used, Phishing Email Dataset: https://www.kaggle.com/dsv/6090437 Social Engineering Dataset: http://aclweb.org/aclwiki

Label Annotation

To label the Enron email dataset two signals are used to filter suspicious emails and label them into fraud and non-fraud classes. Automated ML labeling Email Signals

Automated ML Labeling

The following heuristics are used to annotate labels for Enron email data using the other two data sources,

Phishing Model Annotation: A high-precision SVM model trained on the Phishing mails dataset, which is used to annotate the Phishing Label on the Enron Dataset.

Social Engineering Model Annotation: A high-precision SVM model trained on the Social Engineering mails dataset, which is used to annotate the Social Engineering Label on the Enron Dataset.

The two ML Annotator models use Term Frequency Inverse Document Frequency (TF-IDF) to embed the input text and make use of SVM models with Gaussian Kernel.

If either of the models predicted that an email was a fraud, the mail metadata was checked for several email signals. If these heuristics meet the requirements of a high-probability fraud email, we label it as a fraud email.

Email Signals

Email Signal-based heuristics are used to filter and target suspicious emails for fraud labeling specifically. The signals used were,

Person Of Interest: There is a publicly available list of email addresses of employees who were liable for the massive data leak at Enron. These user mailboxes have a higher chance of containing quality fraud emails.

Suspicious Folders: The Enron data is dumped into several folders for every employee. Folders consist of inbox, deleted_items, junk, calendar, etc. A set of folders with a higher chance of containing fraud emails, such as Deleted Items and Junk.

Sender Type: The sender type was categorized as ‘Internal’ and ‘External’ based on their email address.

Low Communication: A threshold of 4 emails based on the table below was used to define Low Communication. A user qualifies as a Low-Comm sender if their emails are below this threshold. Mails sent from low-comm senders have been assigned with a high probability of being a fraud.

Contains Replies and Forwards: If an email contains forwards or replies, a low probability was assigned for it to be a fraud email.

Manual Inspection

To ensure high-quality labels, the mismatch examples from ML Annotation have been manually inspected for Enron dataset relabeling.

Dataset Breakdown

Citations

Enron Dataset Title: Enron Email Dataset URL: https://www.cs.cmu.edu/~enron/ Publisher: MIT, CMU Author: Leslie Kaelbling, William W. Cohen Year: 2015

Phishing Email Detection Dataset Title: Phishing Email Detection URL: https://www.kaggle.com/dsv/6090437 DOI: 10.34740/KAGGLE/DSV/6090437 Publisher: Kaggle Author: Subhadeep Chakraborty Year: 2023

CLAIR Fraud Email Collection Title: CLAIR collection of fraud email URL: http://aclweb.org/aclwiki Author: Radev, D. Year: 2008

The Supplemental Fraud Survey (SFS) obtained additional information about fraud-related victimizations so that policymakers; academic researchers; practitioners at the federal, state, and local levels; and special interest groups who are concerned with these crimes can make informed decisions concerning policies and programs. The SFS asked questions related to victims' experiences with fraud. These responses are linked to the National Crime Victimization Survey (NCVS) survey instrument responses for a more complete understanding of the individual victim's circumstances. The 2017 Supplemental Fraud Survey (SFS) was the first implementation of this supplement to the annual NCVS to obtain specific information about fraud-related victimization and disorder on a national level. Since the SFS is a supplement to the NCVS, it is conducted under the authority of Title 34, United States Code, section 10132. Only Census employees sworn to preserve confidentiality may see the completed questionnaires.

Amazon-Fraud is a multi-relational graph dataset built upon the Amazon review dataset, which can be used in evaluating graph-based node classification, fraud detection, and anomaly detection models.

Dataset Statistics

Graph Construction

The Amazon dataset includes product reviews under the Musical Instruments category. Similar to this paper, we label users with more than 80% helpful votes as benign entities and users with less than 20% helpful votes as fraudulent entities. we conduct a fraudulent user detection task on the Amazon-Fraud dataset, which is a binary classification task. We take 25 handcrafted features from this paper as the raw node features for Amazon-Fraud. We take users as nodes in the graph and design three relations: 1) U-P-U: it connects users reviewing at least one same product; 2) U-S-V: it connects users having at least one same star rating within one week; 3) U-V-U: it connects users with top 5% mutual review text similarities (measured by TF-IDF) among all users.

To download the dataset, please visit this Github repo. For any other questions, please email ytongdou(AT)gmail.com for inquiry.

As of February 2024, the United States ranked first by the average cost of a data breach, 9.36 million U.S. dollars. The average cost of data breaches in the Middle East is 8.75 million U.S. dollars. Benelux followed in the ranking, with 5.9 million U.S. dollars. In the measured period, the global average data breach cost was 4.88 million U.S. dollars. Phishing scams in the U.S. Breached data often ends up in the hands of threat actors who use it for malicious purposes, including online scams. Phishing continues to be a major threat in North America, particularly on smartphones. In the second quarter of 2023, the region recorded the highest number of phishing and malicious attack attempts globally. The United States was particularly affected, with 45 percent of U.S. citizens reporting being targeted by scam texts, e-mails, and calls on a daily basis. Additionally, phishing and spoofing were the most common types of cybercrime, impacting 298 thousand individuals in 2023. These attacks led to financial losses, with U.S. victims reporting nearly 20 billion U.S. dollars in damages throughout the year. U.S. users and data privacy Despite only 20 percent of internet users in the United States being highly knowledgeable about data privacy and cybersecurity, a significant portion of users demonstrated caution and awareness in protecting their information. In fact, over half of surveyed U.S. users reported being somewhat confident in knowing the right steps to take in the event of a cyberattack. Furthermore, 43 percent of U.S. users actively decline cookies on websites, reflecting their increasing concern for data protection. Many respondents also take additional steps to safeguard their digital privacy, such as limiting or avoiding clicking on ads as well as not answering phone calls due to cybersecurity risks.

This dataset contains over 41,000 posts from the sextortion reporting board of scamsurvivors.com, as collected on the 14th of July, 2023. The data was collected and is shared with the approval of the Scam Survivors administrator. Of these posts, 23,705 were automatically identified as following a common structured report format, and the reported answers to specific questions were extracted into a tabular CSV format, which was then further processed to clean and standardise responses. The data does not contain identifiable or demographic victim information, as the reports are anonymous at source, but does include details of sextortion offenders' (purported) names and ages, as well as their online presence, meeting locations, conversation platforms, interaction dynamics, payment demands and some victim reflection on incidents. This dataset has been created as part of the REPHRAIN project (https://www.rephrain.ac.uk/).

Provides ad hoc query and standard report data on the measure for preventing the issuance of SSN cards to non-existent children.

Be aware of the common types of phishing scams that are out there.

The Identity Theft & Fraud Protection market is experiencing robust growth, projected to reach $6635.5 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 6.9% from 2025 to 2033. This expansion is fueled by several key drivers. Rising instances of cybercrime, data breaches, and sophisticated phishing scams are creating a heightened demand for robust identity and fraud protection solutions among both consumers and enterprises. The increasing adoption of digital technologies and online transactions further exacerbates vulnerabilities, making these services essential for safeguarding personal and financial information. Furthermore, stringent government regulations regarding data privacy and security are pushing organizations to invest heavily in advanced protection measures, contributing significantly to market growth. The market is segmented by type (Credit Monitoring, ID Monitoring, Other Services) and application (Consumer, Enterprise), with the consumer segment currently dominating due to increased individual awareness of online threats. However, the enterprise segment is poised for substantial growth as businesses increasingly prioritize cybersecurity and risk mitigation. Geographic regions such as North America and Europe are currently leading the market, but Asia-Pacific is expected to witness significant growth in the coming years due to increasing internet penetration and rising disposable incomes. Competitive dynamics within the market are intense, with established players such as NortonLifeLock, Experian, and Equifax alongside emerging innovative companies vying for market share through technological advancements and strategic partnerships. Looking ahead, the Identity Theft & Fraud Protection market will continue its upward trajectory, driven by the relentless evolution of cyber threats and the growing reliance on digital platforms. Technological advancements, such as artificial intelligence and machine learning, are expected to further enhance the accuracy and effectiveness of fraud detection systems. The adoption of advanced authentication methods and biometric technologies will also play a crucial role in improving security measures. However, challenges remain, including concerns about data privacy, the escalating sophistication of cyberattacks, and the need for continuous improvement in security awareness among consumers and businesses. The market's future success hinges on the ability of providers to adapt quickly to the ever-changing threat landscape and offer innovative, user-friendly solutions that effectively protect individuals and organizations from identity theft and fraud.