The Importance Of Taxonomies In Understanding Scams, Fraud & Cybercrimes

The Importance Of Taxonomies: In Every Aspect of Criminology, and Law Enforcement – Having a Unified Agreed-Upon Taxonomy is Vital

Primary Category: Criminology

UPDATED 2025

Author:
•  Tim McGuinness, Ph.D., DFin, MCPO, MAnth – Anthropologist, Scientist, Polymath, Director of the Society of Citizens Against Relationship Scams Inc.

Author Biographies Below

About This Article

The article delineates the paramount significance of taxonomies in criminology, cybersecurity, fraud prevention, and law enforcement, emphasizing their role in creating unified frameworks for understanding criminal behavior.It elucidates how structured classifications facilitate comprehensive analysis, aiding researchers, law enforcement, policymakers, and businesses in discerning crime patterns and formulating effective prevention strategies. Emphasizing the necessity of standardized taxonomies, it delineates the perils of employing non-standard terms, delineating their adverse effects on victim support, investigation, and collaboration among law enforcement agencies. The discourse concludes by advocating for a unified taxonomy, fostering collaboration among diverse entities to combat fraud and scams effectively.

The Importance Of Taxonomies in Scams, Fraud, and Cybercrime

The Importance Of Taxonomies: In Every Aspect of Criminology, and Law Enforcement – Having a Unified Agreed-Upon Taxonomy is Vital

Taxonomies establish agreement on terminology that defines the work of professionals across fields. In criminology, cybersecurity, fraud prevention, and law enforcement, a unified taxonomy proves essential for clarity and effectiveness. Without standardized classifications, efforts to combat scams and fraud become fragmented, leading to miscommunication, inefficient resource allocation, and diminished public trust. This article explores the role of taxonomies in addressing scams, their importance, the risks posed by non-standardized approaches, and the broader impacts on law enforcement, victims, and organizations. It highlights the need for a cohesive framework to enhance understanding and response to these crimes.

The Role Of Taxonomies

Taxonomies provide a systematic framework for categorizing criminal behaviors, enabling researchers, law enforcement, and policymakers to analyze patterns and trends. In the context of scams and fraud, taxonomies differentiate between various schemes, facilitating targeted strategies. These classifications support information exchange among stakeholders, advancing knowledge and evidence-based policies. In cybercrime, taxonomies extend to digital fraud, where structured models identify modus operandi and criminal profiles. By grouping behaviors, taxonomies reveal connections between scams and broader criminal networks. This role extends to victim support, where clear categories help organizations tailor interventions, reducing the chaos that arises from ambiguous descriptions.

The Importance of Taxonomies in Understanding Scams

Taxonomies serve as a fundamental tool for organizing and classifying criminal behaviors, which is essential for several reasons.

• Firstly, taxonomies provide a standardized language and framework for describing and categorizing criminal activities. This allows researchers, practitioners, and policymakers to communicate effectively and share information consistently. By using common terminology and classifications, it becomes easier to compare and analyze crime data across different jurisdictions and time periods.
• Secondly, taxonomies help in identifying patterns and trends in criminal behavior. By categorizing crimes into distinct types and subtypes, researchers can identify similarities, differences, and recurring patterns within and across different criminal activities. This knowledge is crucial for understanding the root causes of crimes, predicting future criminal behavior, and developing effective prevention and intervention strategies.
• Furthermore, taxonomies aid in the development of criminal profiles. By classifying and categorizing offenders based on their behaviors, characteristics, and motivations, criminologists can create profiles that assist law enforcement agencies in identifying and apprehending criminals. This helps investigators narrow down their search, allocate resources efficiently, and develop targeted strategies for combating specific types of crime.
• Taxonomies also contribute to the formulation of evidence-based policies and interventions. By studying the characteristics and dynamics of different types of crimes, policymakers can make informed decisions on resource allocation, crime prevention initiatives, and the implementation of effective measures to address specific criminal activities. Taxonomies enable policymakers to understand the underlying factors that contribute to criminal behavior, which helps in designing interventions that target those factors directly.

Taxonomies serve as foundational tools for organizing criminal behaviors, offering standardized language for consistent communication. Researchers and practitioners benefit from shared terminology, enabling comparisons across jurisdictions and time periods. In fraud prevention, this standardization identifies patterns, allowing for predictive modeling and proactive measures. Furthermore, taxonomies support the development of criminal profiles, assisting law enforcement in resource allocation. By classifying offenders based on motivations and methods, agencies narrow investigations. Evidence-based policies emerge from these classifications, addressing root causes like economic vulnerabilities in scams targeting certain groups. Overall, taxonomies deepen insights into crime’s complexity, fostering safer communities through informed strategies.

Random or Non-Standardized Taxonomies

The use of random or non-standardized taxonomies in criminology and allied occupations can introduce several dangers and challenges:

1. Lack of Consistency: Random taxonomies lack a standardized and consistent framework for classifying criminal behavior. This inconsistency can lead to confusion and miscommunication among researchers, practitioners, policymakers, and even the victims. It becomes difficult to compare and analyze data across different studies, jurisdictions, or time periods, hindering the development of a comprehensive understanding of crime trends and patterns.
2. Impaired Data Analysis: Random taxonomies make it challenging to aggregate and analyze data effectively. Without a consistent classification system, it becomes difficult to identify commonalities, trends, and relationships within crime data. This hampers the ability to draw meaningful conclusions, make evidence-based decisions, and develop targeted strategies for crime prevention and intervention.
3. Limited Knowledge Accumulation: Random taxonomies hinder the accumulation of knowledge in criminology and allied fields. A consistent taxonomy allows for the accumulation of research findings, theories, and best practices over time. It enables the field to build upon existing knowledge, refine concepts, and develop a deeper understanding of criminal behavior. Random taxonomies fragment knowledge and impede the progress of criminological research.
4. Challenges in Collaboration and Comparability: In criminology, collaboration and comparability are essential for advancing knowledge and practice. Random taxonomies impede collaboration among researchers & practitioners, as there is no shared language or framework for understanding and discussing crime. It becomes difficult to compare findings, replicate studies, or conduct meta-analyses, limiting the ability to draw robust conclusions or make broader generalizations.
5. Ineffective Policy Development: Random taxonomies hinder the development of effective policies and legislation. Without a consistent framework to guide policy discussions, it becomes challenging to identify and prioritize key issues, understand the root causes of crime, and design evidence-based strategies. Policy development relies on a clear understanding of the nature and dynamics of criminal behavior, which is impeded by random taxonomies.
6. Reduced Public Trust: Random taxonomies can erode public trust in the field of criminology, law enforcement, victim support, fraud prevention, and related activities. Inconsistencies and arbitrary classifications can lead to skepticism and confusion among the general public. When the language and classifications used in criminology are perceived as arbitrary or lacking rigor, it undermines the credibility of the field and reduces public confidence in the effectiveness of crime prevention and justice systems.
7. Minimization of Crime: Often, terminology tends to minimize the impact of crimes. The news media is especially prone to this in coining new catchy phrases that make good sound bites but obscure the true nature and severity of the crimes in question.

Random or non-standardized taxonomies introduce significant risks in criminology and fraud prevention. Lack of consistency leads to confusion among stakeholders, impeding data comparison and analysis across regions. Without uniformity, aggregation of crime data suffers, hindering the ability to draw meaningful conclusions or develop targeted strategies. This fragmentation limits knowledge accumulation, as inconsistent terms prevent building on past research or refining concepts. Collaboration and comparability falter without a shared language, obstructing joint investigations and meta-analyses. Ineffective policy development follows, as unclear frameworks complicate identifying key issues or designing interventions. Additionally, random taxonomies erode public trust, as arbitrary classifications undermine credibility in criminology and law enforcement efforts. Media-coined terms, such as catchy phrases that minimize crime severity, further obscure the true nature of scams, exacerbating these dangers.

The Impact on Law Enforcement

Fraud taxonomies have a significant impact on law enforcement and the criminal justice system in several ways:

1. Improved Investigations: Fraud taxonomies provide a structured framework for classifying different types of fraudulent activities, such as identity theft, financial fraud, or online scams. Law enforcement agencies can utilize these taxonomies to categorize and analyze fraud cases more effectively, leading to more targeted investigations. By understanding the specific characteristics and patterns associated with different types of fraud, investigators can gather relevant evidence, identify suspects, and build stronger cases.
2. Resource Allocation: Fraud taxonomies assist in resource allocation by helping law enforcement agencies identify high-risk areas and prioritize their efforts. By analyzing the prevalence and severity of different types of fraud, agencies can allocate resources, personnel, and technology to combat the most significant threats. Taxonomies enable agencies to make informed decisions about where to focus their resources to maximize their impact in preventing and detecting fraud.
3. Intelligence Sharing: Fraud taxonomies facilitate the sharing of information and intelligence between law enforcement agencies, both domestically and internationally. By utilizing a standardized classification system, agencies can communicate effectively, exchange data, and collaborate on cross-jurisdictional cases. This sharing of information helps to identify patterns, track criminal networks, and enhance the overall effectiveness of investigations and prosecutions.
4. Prevention Strategies: Taxonomies play a crucial role in developing proactive prevention strategies. By analyzing fraud patterns and understanding the underlying factors contributing to different types of fraud, law enforcement agencies and policymakers can design targeted prevention campaigns and educate the public about specific risks. Fraud taxonomies enable the identification of common vulnerabilities and provide guidance on implementing preventive measures, such as public awareness campaigns, improved security protocols, and regulatory changes.
5. Policy Development: Fraud taxonomies inform the development of policies and legislation related to fraud. By categorizing and classifying fraudulent activities, policymakers can gain a better understanding of the nature and scope of fraud in society. This understanding allows them to create effective laws, regulations, and enforcement strategies that address the specific challenges posed by different types of fraud. Taxonomies provide policymakers with the necessary insights to design comprehensive approaches that deter fraud, protect victims, and hold perpetrators accountable.

Fraud taxonomies have a significant impact on law enforcement and the criminal justice system. These frameworks provide a structured approach for classifying fraudulent activities, enabling more effective categorization and analysis of cases. This leads to targeted investigations, as agencies better understand specific characteristics and patterns associated with different fraud types. Taxonomies assist in resource allocation by highlighting high-risk areas, allowing agencies to prioritize efforts and deploy personnel and technology strategically. They facilitate intelligence sharing between domestic and international law enforcement, enhancing communication and collaboration on cross-jurisdictional cases. Additionally, taxonomies support proactive prevention strategies by identifying common vulnerabilities, guiding public awareness campaigns, and informing security protocol improvements. They also inform policy development, providing insights to create laws and regulations that address specific fraud challenges, ultimately strengthening the response to evolving fraud-related crimes. Conversely, random or diffuse taxonomies complicate these efforts, making law enforcement’s task significantly harder.

Impact on Crime Victims

Scam or fraud taxonomies have a direct impact on scam or fraud victims in several ways:

1. Victim Support and Validation: Scam taxonomies provide a standardized framework for classifying different types of scams and frauds. When victims encounter a scam, they can refer to these taxonomies to identify the specific type of fraud they have experienced. This classification helps victims understand that they are not alone and that others have fallen victim to similar schemes. It validates their experiences, reducing the feelings of isolation and self-blame often associated with being scammed.
2. Awareness and Education: Scam taxonomies contribute to public awareness and education campaigns. By categorizing and defining various types of scams, taxonomies help raise awareness among potential victims about the tactics and techniques used by fraudsters. Victims can educate themselves on common scam patterns, warning signs, and red flags associated with specific types of fraud. This knowledge empowers individuals to be more vigilant, recognize potential scams, and take proactive steps to protect themselves and their finances.
3. Recovery and Healing: Scam taxonomies can assist in the recovery and healing process for victims. Understanding the specific type of scam they fell victim to helps individuals make sense of their experience and process their emotions. It allows them to seek support from appropriate resources, such as counseling services or support groups tailored to victims of that particular type of fraud. Connecting with others who have experienced similar scams can provide comfort, empathy, and a sense of validation, which are crucial elements in the healing journey.
4. Reporting and Legal Actions: Scam taxonomies enable accurate reporting of fraud incidents and aid in legal actions against scammers. Victims can provide detailed information to law enforcement agencies, using the standardized classifications provided by taxonomies. This consistency in reporting assists in identifying trends, patterns, and the scope of different types of scams. It helps law enforcement agencies build stronger cases against scammers and potentially recover funds or assets for victims through legal proceedings.
5. Prevention and Awareness Resources: Scam taxonomies contribute to the development of prevention resources and support materials for victims. By categorizing scams, organizations and agencies can create targeted educational materials, online resources, and guides to help individuals recognize and avoid specific types of scams. Victims can access these resources to learn about recovery options, steps to take after being scammed, and available support services. This aids in empowering victims, helping them regain their confidence, and minimizing the risk of falling victim to scams again.

Scam or fraud taxonomies have a direct impact on victims in several ways. These frameworks offer a standardized approach for classifying different types of scams and frauds, allowing victims to identify the specific scheme they encountered. This classification validates their experiences, reducing feelings of isolation and self-blame often associated with being scammed. Taxonomies contribute to awareness and education campaigns by defining various scam tactics, empowering individuals to recognize warning signs and protect themselves. They assist in the recovery and healing process by helping victims process their emotions and seek tailored support, such as counseling or peer groups. Accurate reporting becomes possible with standardized classifications, aiding law enforcement in building cases and potentially recovering losses. Additionally, taxonomies support the development of prevention resources, including educational materials and guides, which help victims regain confidence and minimize future risks. By providing support, validation, and a framework for understanding, taxonomies guide victims through the aftermath of scams.

Self-Invented Terms & Taxonomies

The use of incorrect or self-invented terms when discussing scams and fraud can have negative impacts on both victims and law enforcement. Terms like “catfishing” or “pig butchering,” if not integrated into a broader taxonomy, risk creating confusion. Miscommunication arises when victims struggle to describe their experiences, hindering accurate reporting and investigation. This can delay justice and frustrate law enforcement efforts, especially when departments misclassify scams as outside their jurisdiction. Inadequate support follows, as non-standard terms complicate access to appropriate resources, steering victims toward ineffective or hostile groups. Underreporting results from victims’ hesitation to report without recognized terminology, obscuring the true scope of fraud. Investigation and prosecution face challenges, as inconsistent terms impede case connections and pattern recognition. Lack of coordination among agencies, both domestically and internationally, further weakens the response. Standardized terminology remains crucial to ensure clear communication, effective victim support, and a robust fight against scams.

The Danger of Self-Invented Terms

The use of incorrect or self-invented terms when discussing scams and fraud can have negative impacts on both victims and the police. Here are some of the consequences:

1. Miscommunication and Confusion: When incorrect or self-invented terms are used, it can lead to miscommunication and confusion between victims and the police. Victims may struggle to effectively describe their experiences, making it difficult for law enforcement to understand and investigate the crime accurately. This can hinder the reporting and investigation process, potentially delaying justice for the victims. We see examples of them in police response to scams vs. fraud – they have a fraud department, but often tell victims there is nothing they can do with scams.
2. Inadequate Support and Resources: Incorrect terms may lead to a lack of appropriate support and resources for victims. If victims use non-standard or self-invented terms, it becomes challenging for support organizations and agencies to identify and provide specific assistance tailored to their needs. This can result in victims not receiving the appropriate guidance, counseling, or recovery resources required to cope with the emotional, financial, and legal aftermath of the scam. This also tends to direct victims away from professional services and towards amateur, ineffective, or hostile groups.
3. Underreporting and Data Inconsistencies: Inconsistent or incorrect terms can contribute to underreporting of scams and frauds. Victims may be hesitant to report their experiences if they cannot find a recognized term that accurately describes their situation. This leads to a distorted understanding of the prevalence and impact of scams, making it more challenging for law enforcement and policymakers to allocate resources and develop effective prevention strategies.
4. Challenges in Investigation and Prosecution: When incorrect or self-invented terms are used, it can create challenges for law enforcement agencies during the investigation and prosecution phases. Investigators may struggle to connect cases or identify patterns if victims use non-standard terminology. This can impede the ability to build strong cases, track criminal networks, and bring scammers to justice.
5. Lack of Coordination and Collaboration: Standardized terminology facilitates coordination and collaboration between different law enforcement agencies, both domestically and internationally. If incorrect or self-invented terms are used, it becomes difficult to establish a common understanding and share information effectively. This can hinder the exchange of intelligence, hinder joint investigations, and limit the overall effectiveness of combating scams and fraud on a broader scale. We see this constantly in the invention of new terms for crimes, which are actually a technique or tactic and not a different type of crime.

The use of incorrect or self-invented terms when discussing scams and fraud can result in miscommunication, inadequate support for victims, underreporting, challenges in investigation and prosecution, and a lack of coordination among law enforcement agencies. It is crucial to utilize standardized and recognized terminology to ensure clear communication, facilitate effective support for victims, and enhance the overall response to scams and fraud.

Organizational Chaos

Diverse organizations addressing scams and fraud often develop conflicting taxonomies, reflecting their unique priorities and orientations. This creates organizational chaos, as each entity carves out its own piece of the discussion. The challenge lies in reconciling these differences to establish a single, standardized taxonomy that meets the needs of all stakeholders, including government, non-profits, and industry. Historically, standards bodies have emerged from such chaos to set unified frameworks, suggesting a need for a new collaborative organization. This body could include representatives from all relevant constituencies, drawing on past successes in creating standards like those for healthcare data. A unified taxonomy would streamline efforts, enhance collaboration, and address the growing complexity of scam-related crimes, offering a path toward greater coherence in the field.

We at the SCARS Institute have seen a broad range of taxonomy conflicts between organizations that have carved out their piece of the overall discussion. Organizations such as The Noble, and many others all have their pet taxonomies that reflect their priorities and orientation. SCARS also has its own taxonomy.

We believe that, as both an industry of support providers, fraud prevention professionals, industry, and government, we need to come together to develop a single standardized taxonomy that meets the needs of all constituencies, or at least a limited number of them, oriented toward specific audiences. But the question is, how do we get there?

In the past, standards bodies have emerged from the chaos to help establish such standards. We propose that now is the time to explore this in the form of an organization that can include all appropriate constituencies from government, to NGOs, to Industry. I myself have been a participant in creating such bodies in the past for other schemes, such as HIPAA.

Here is the current SCARS Taxonomy for Financial Fraud & Scams – DOI: 10.13140/RG.2.2.26572.59523

Conclusion

The critical role of taxonomies in combating scams and fraud cannot be overstated, serving as a cornerstone for understanding and addressing these pervasive crimes. By providing a structured framework, taxonomies enable researchers, law enforcement, and policymakers to identify patterns, develop targeted strategies, and enhance collaboration across jurisdictions. They offer victims a vital tool for validation and recovery, empowering them with knowledge to navigate the aftermath and prevent future victimization. The dangers of random or non-standardized taxonomies—confusion, impaired data analysis, and eroded public trust—underscore the urgent need for uniformity. Self-invented terms further complicate the landscape, hindering effective communication and support, while organizational chaos among stakeholders amplifies the challenge.

A unified taxonomy promises to bridge these gaps, fostering a cohesive response that strengthens prevention, investigation, and victim assistance. The call for a collaborative standards body, drawing on diverse expertise from government, industry, and non-profits, offers a practical path forward. Such an initiative could harmonize efforts, ensuring a shared language and approach to tackle the evolving threat of scams. For scam victims and the public, embracing standardized taxonomies equips them with clarity and resilience, turning the tide against fraudsters who thrive in ambiguity.

Glossary

• Aggregated reporting — This term means combining many individual cases into summarized totals. Aggregation helps show trends over time or across regions. It also hides personal details, which protects victims while still supporting analysis.
• Case typology — This refers to a structured list of case types used to sort incidents into similar groups. A clear typology makes searches, reports, and comparisons easier. It helps victims find guidance that matches their situation.
• Category schema — This is the map of top-level groups and subgroups that a system uses to organize scams. A good schema keeps labels consistent across forms, websites, and reports. Consistency reduces errors and confusion.
• Coding rubric — This is a short set of rules that explains how to label a case. Rubrics reduce guesswork for staff who enter data. Clear rules improve accuracy and speed.
• Controlled vocabulary — This is a fixed list of approved terms that writers and analysts agree to use. Control prevents near-duplicate labels that split statistics. It also improves search results for victims and investigators.
• Cross-jurisdiction comparability — This phrase describes how well data from different regions can be compared. Comparable data requires shared definitions, calendars, and counting rules. Better comparability means better planning and response.
• Data dictionary — This is a plain-language guide that defines every field in a database. It explains what to enter, what to avoid, and what each field means. A good dictionary keeps data clean and useful.
• Data harmonization — This is the process of aligning different datasets so they match on names, dates, and formats. Harmonization allows separate systems to work together. It also reduces duplicate counting.
• Data lineage — This means a clear record of where data came from and how it changed. Lineage shows who added each item, when, and why. A strong lineage supports trust and auditing.
• Data normalization — This process converts varied entries into a standard form. Examples include turning “Oct 1” and “10/01” into the same date format. Normalized data supports accurate totals and alerts.
• Deconfliction — This is the practice of checking whether two reports refer to the same event, person, or payment. Deconfliction reduces double counting and crossed investigations. It also protects evidence quality.
• Denominator problem — This describes not knowing how many total people were exposed to a scam, which makes risks hard to estimate. Better denominators come from surveys and standardized intake. Clear denominators improve public guidance.
• Evidence tagging — This means labeling documents, images, and messages so they can be found quickly later. Good tags include date, source, and case number. Proper tagging speeds investigations and victim support.
• Event classification — This is the step where an incident is placed into a defined category. Sound classification relies on facts, not headlines. Accurate class choices drive correct action.
• Granularity — This refers to how detailed the categories and fields are. Finer granularity can reveal new patterns, but takes more time to enter. Balanced detail supports both speed and insight.
• Ground truth — This phrase means confirmed facts checked against reliable sources. Ground truth anchors analysis and prevents rumor-led decisions. Victims benefit when guidance rests on verified facts.
• Hierarchical taxonomy — This is a tree-like structure of categories and subcategories. It allows broad summaries and precise drill-downs in the same system. A clear hierarchy keeps growth manageable over time.
• Incident coding — This is the assignment of a standard label and attributes to a single report. Strong coding includes the scam type, channel, and payment path. Better coding leads to faster prevention advice.
• Indicator standardization — This means using shared signals and fields to track key features across cases. Standard indicators can include payment method, first contact medium, and loss window. Uniform tracking improves early warnings.
• Interoperability — This is the ability of different systems to share and use data without manual rework. Interoperable systems reduce delays for victims and agencies. Shared formats make cooperation routine.
• Label drift — This occurs when people begin to use a term in new ways that break comparability. Drift hides trends and weakens alerts. Regular reviews keep labels aligned with definitions.
• Mapping table — This is a simple list that translates one set of labels into another. Mapping helps merge legacy data with a new taxonomy. It preserves history while improving current accuracy.
• Metadata — This is data about data, such as who entered a record and when. Good metadata helps audits, privacy controls, and case reviews. It is essential for quality assurance.
• Ontology alignment — This is a method for matching different taxonomies so they can work together. Alignment preserves each group’s needs while enabling shared reporting. It supports cross-agency action.
• Open standard — This is a public, documented specification that anyone may adopt. Open standards lower costs and speed adoption. They help victims by making information easier to exchange.
• Reference model — This is a published example structure that others can copy or adapt. A reference model shortens setup time and reduces errors. It also encourages consistent reporting.
• Research reproducibility — This means another analyst can follow the same steps and get the same results. Reproducibility builds trust in findings that drive policy. It protects victims from advice based on weak methods.
• Severity ladder — This is a ranked scale that shows harm levels, from attempt to major loss. A shared ladder helps triage and resource planning. It also sets clear thresholds for escalation.
• Standard operating definition — This is a precise statement of what qualifies for a label and what does not. Operating definitions remove ambiguity during intake and analysis. Clear boundaries prevent overcounting and undercounting.
• Tagging protocol — This is a short guide that explains how to apply tags to cases and evidence. A protocol keeps tags consistent across teams and time. Consistency improves search power.
• Taxonomy change log — This is a dated record of edits to names, definitions, and structures. A change log preserves comparability across years. It tells analysts what changed and why.
• Term deprecation — This is the formal retirement of a term that causes confusion or overlap. Deprecation includes a replacement term and a timeline. Managed change keeps systems readable.
• Threat pattern catalog — This is a curated list of repeated methods used by offenders. Pattern catalogs support training, alerts, and prevention design. They help victims recognize risk early.
• Typology — This is a set of descriptive types based on shared features, often used for field work and education. Typologies connect real-world stories to structured labels. They make guidance practical.
• Version control — This is a system that tracks edits, who made them, and when. Versioning prevents accidental loss and supports rollback if needed. It also helps teams collaborate safely.
• Victim segmentation — This is the grouping of cases by shared victim features to tailor support. Segments can include age range, channel of contact, or payment method. Targeted support respects differences while improving outcomes.

Reference

• A taxonomy of cybercrime: Theory and design – Akhilesh Chandra, Melissa J. Snowe – https://doi.org/10.1016/j.accinf.2020.100467
• The Liminality of Fraud: Reimagining Fraud Theory to Inform Financial Crime Prevention Open Access – Nicola Harding, Emily Cooper, Tony Sales, Andy McDonald, Sarah Kingston – https://doi.org/10.1093/bjc/azae069
• FRAMEWORK FOR A TAXONOMY OF FRAUD – https://longevity.stanford.edu/financial-fraud-research-center/wp-content/uploads/2016/03/Full-Taxonomy-report.pdf
• Fraud Definitions, Models, and Taxonomies – https://catalogimages.wiley.com/images/db/pdf/0471785911.excerpt.pdf
• Analyzing Threats to Financial Market Integrity – A Taxonomy of Financial Fake News Schemes – https://scholarspace.manoa.hawaii.edu/bitstreams/62cc1927-32a6-4263-a5b3-fa7f4162e8eb/download
• A Taxonomy of Frauds and Fraud Detection Techniques – https://doi.org/10.1007/978-3-642-00405-6_28

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