Fraud Detection Terms Glossary: Fraud Detection Terms in 2024
A
Advanced Analytics
Advanced analytics refers to the use of sophisticated techniques and algorithms, such as machine learning and data mining, to analyze and extract insights from complex data sets for fraud detection.
Alert
An alert is a notification or signal indicating a potential fraudulent event.
Aml (Anti-Money Laundering)
AML refers to the set of regulations, policies, and procedures implemented to prevent the generation of income through illegal activities.
Anomaly
Anomaly refers to a deviation or irregularity from the expected or normal behavior, which can indicate the presence of fraud.
Anomaly Detection
Anomaly Detection is a technique used to identify patterns or data points that significantly differ from the norm or expected behavior.
Anomaly Score
Anomaly Score is a numerical value or metric assigned to each data point or instance, indicating its degree of deviance from the normal or expected behavior.
Anti-Money Laundering
Anti-Money Laundering (AML) refers to the regulations, policies, and procedures implemented by financial institutions to prevent or detect activities associated with money laundering, terrorism financing, or other illicit funds.
Artificial Intelligence
Artificial intelligence refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human intelligence.
Auc
AUC (Area Under the ROC Curve) is a metric used to evaluate the overall performance of a binary classification model, representing the area under the ROC Curve.
Audit Trail
Audit Trail is a record that captures and chronologically documents the activities, changes, or events related to a system or process, providing a comprehensive history for analysis and investigation.
Authentication
Authentication is the process of verifying the identity of a user or entity.
B
Behavioral Analytics
Behavioral Analytics is a technique used to analyze and detect patterns or anomalies in users' behavior, enabling the identification of potential fraud or malicious activities.
Bias
Bias in Machine Learning refers to the systematic error or deviation of predictions from the actual values or outcomes, often caused by an overly simplified or insufficiently representative model.
Big Data
Big data refers to large, complex data sets that cannot be easily managed or analyzed using traditional data processing techniques, often utilized in fraud detection to uncover hidden patterns.
Biometric Authentication
Biometric Authentication is a security method that uses unique biological characteristics, such as fingerprints, facial recognition, or iris scans, to verify an individual's identity.
Biometrics
Biometrics refers to the unique physical or behavioral characteristics of individuals, such as fingerprints, voice patterns, or facial features, used for authentication or identification purposes.
Black Box Model
Black Box Model is a Machine Learning model or algorithm that provides predictions or results without revealing the underlying internal workings or reasons behind those predictions.
Blacklist
A blacklist is a list of individuals, entities, or activities that are considered suspicious or high-risk.
Blockchain
Blockchain is a digital ledger that records transactions across multiple computers, making it highly secure and tamper-resistant.
C
Chargeback
A chargeback occurs when a customer disputes a transaction and the amount is returned to the customer.
Clustering
Clustering is an unsupervised learning technique that groups similar instances together based on their features, without predefined class labels.
Credit Card Fraud
Credit card fraud is a type of financial fraud where someone uses another person's credit card or card details without their authorization to make unauthorized transactions.
Cross-Site Scripting
Cross-Site Scripting (XSS) is a type of security vulnerability where malicious scripts are injected into web pages viewed by users, allowing attackers to bypass security mechanisms and execute malicious code on the victim's browser.
Cross-Validation
Cross-Validation is a resampling technique used to assess the performance and generalization ability of Machine Learning models by partitioning the data into multiple subsets.
Customer Due Diligence
Customer due diligence is the process of verifying the identity of customers to prevent fraud and money laundering.
Cybersecurity
Cybersecurity involves protecting computer systems and networks from unauthorized access and digital attacks.
D
Data Analysis
Data analysis involves inspecting, cleaning, and transforming data to discover useful information, patterns, and insights for fraud detection.
Data Analytics
Data analytics involves analyzing large datasets to uncover patterns, correlations, and insights that can be used to prevent fraud.
Data Breach
Data Breach is a security incident where unauthorized individuals gain access to sensitive or confidential information, often resulting in its theft, exposure, or misuse.
Data Imputation
Data imputation is the process of filling in missing values or replacing inaccurate data with estimated or calculated values to ensure the integrity and completeness of data used in fraud detection.
Data Integration
Data integration is the process of combining data from multiple sources or systems to create a unified view for analysis and fraud detection.
Data Integrity
Data integrity refers to the accuracy, consistency, and reliability of data, ensuring it remains unaltered and complete throughout its lifecycle.
Data Leakage
Data leakage refers to the unauthorized or accidental transfer of sensitive or confidential information, which can lead to identity theft or other fraudulent activities.
Data Mining
Data mining is the process of discovering patterns and insights from large datasets.
Data Preprocessing
Data preprocessing involves cleaning, transforming, and preparing data before it can be used for analysis, ensuring its quality and compatibility with fraud detection algorithms.
Data Validation
Data validation is the process of ensuring that data entered or imported into a system is accurate, complete, and conforms to specified rules.
Data Visualization
Data visualization is the process of presenting data in visual formats, such as charts or graphs, to facilitate better understanding and analysis of fraud-related data.
Decision Tree
Decision Tree is a supervised learning algorithm used for classification and regression tasks, where the model creates a tree-like structure to make decisions based on feature conditions.
Decision Trees
Decision trees are tree-like models that use a hierarchical structure of nodes and branches to make decisions or classifications based on input data, often used in fraud detection as part of ensemble methods.
Deep Learning
Deep Learning is a subset of Machine Learning that focuses on modeling and learning high-level abstractions in data using artificial neural networks with multiple layers.
Detection
Detection is the process of identifying fraudulent activities, transactions, or behaviors.
Digital Forensics
Digital forensics is the process of collecting, analyzing, and preserving electronic evidence to investigate and prevent cybercrime, including fraud.
E
E-Commerce Fraud
E-commerce fraud involves fraudulent activities specifically targeted at online retail platforms and transactions.
Encryption
Encryption is the process of converting information into a form that can only be accessed or understood by authorized parties, preventing unauthorized access or interception.
Ensemble Learning
Ensemble Learning is a technique that combines multiple models, such as classifiers or regressors, to make predictions with improved accuracy and generalization.
F
F1 Score
F1 Score is a metric used to evaluate the harmonized balance between precision and recall of a model, calculated as the weighted average of these two metrics.
False Negative
False Negative is a term used to describe a situation where a fraudulent transaction or activity is incorrectly classified as legitimate.
False Positive
False Positive is a term used to describe a situation where a legitimate transaction or activity is mistakenly flagged as fraudulent.
Feature Engineering
Feature Engineering is the process of selecting, transforming, and creating relevant features from the available data to improve the performance of a Machine Learning model.
Feature Extraction
Feature Extraction is the process of transforming raw data, such as text or images, into a format suitable for Machine Learning models by extracting relevant features.
Feature Importance
Feature importance refers to the measure of the relative importance or contribution of each feature or variable in a machine learning model for fraud detection.
Feature Selection
Feature Selection is the process of identifying and selecting the most relevant features from a dataset to be used in a Machine Learning model.
Forensic Analysis
Forensic analysis involves investigating and analyzing evidence to uncover fraudulent activities.
Fraud
Fraud refers to the act of intentionally deceiving others for personal or financial gain.
Fraud Analytics
Fraud analytics involves using statistical techniques and data analysis to identify and prevent fraudulent activities.
Fraud Awareness
Fraud awareness refers to the knowledge and understanding of different types of fraud and how to prevent them.
Fraud Detection
Fraud Detection is the process of identifying and preventing fraudulent activities.
Fraud Detection Rules
Fraud detection rules are predefined criteria or conditions used to flag or identify potentially fraudulent transactions or activities based on specific patterns or behaviors.
Fraud Detection System
A fraud detection system is a software or system that uses various techniques and algorithms to automatically identify and prevent fraudulent activities in real-time or post-transaction analysis.
Fraud Intelligence
Fraud intelligence refers to the information, insights, and knowledge derived from analyzing fraud-related data, which can be used to identify new fraud patterns and improve detection strategies.
Fraud Investigator
A fraud investigator is an individual responsible for examining and resolving fraud cases.
Fraud Prevention
Fraud Prevention encompasses the strategies, techniques, and measures employed to proactively identify, mitigate, and deter fraudulent activities before they occur.
Fraud Rings
Fraud rings refer to organized groups or networks of individuals collaborating to commit fraudulent activities, often using sophisticated methods to evade detection.
Fraud Risk Assessment
Fraud risk assessment is the process of evaluating the potential risks and vulnerabilities to fraudulent activities within an organization.
Fraud Risk Management
Fraud risk management is the process of identifying and mitigating fraud risks within an organization.
Fraud Triangle
The fraud triangle is a framework used to analyze the three factors that contribute to fraudulent behavior: opportunity, pressure, and rationalization.
Fraudster
A fraudster is an individual or entity that engages in fraudulent activities.
Fraudulent Applications
Fraudulent applications refer to falsified or misleading information provided in applications, such as credit card applications or insurance claims, with the intention of committing fraud.
Fraudulent Claims
Fraudulent claims refer to false or exaggerated insurance claims, where individuals or organizations attempt to deceive insurance companies for financial gain.
Fraudulent Insurance Claims
Fraudulent insurance claims involve filing false or exaggerated claims for financial gain.
Fraudulent Transaction
A fraudulent transaction refers to any transaction or activity that is intentionally deceptive or misleading, aiming to cause financial harm.
G
Gradient Descent
Gradient Descent is an optimization algorithm used in Machine Learning to minimize the loss function and find the optimal values for the model parameters by iteratively updating them in the direction of steepest descent.
Gray Box Model
Gray Box Model is a Machine Learning model or algorithm that provides some insight or limited access to the internal workings or intermediate representations used in the prediction process.
H
Holdout Set
Holdout Set is a subset of the data used for the final evaluation of a Machine Learning model after training and model selection, providing an unbiased measure of its performance.
Hyperparameter
Hyperparameter is a parameter defined outside of a Machine Learning model that determines its behavior, settings, or structure, often set before the learning process begins.
I
Identity Theft
Identity Theft is a form of fraud where an individual's personal or financial information is stolen without their consent and used for illegal purposes, such as accessing accounts or making unauthorized transactions.
Imbalanced Data
Imbalanced data refers to a situation where the classes in a dataset for fraud detection are not represented equally, posing challenges for accurate model training and detection of the minority class (fraud cases).
Imbalanced Dataset
Imbalanced Dataset is a dataset where the distribution of classes is unequal or skewed, with one or more classes having significantly more or fewer instances than others.
Insider Threat
An insider threat refers to a current or former employee who uses their access to commit fraudulent activities.
Insider Trading
Insider trading occurs when individuals trade stocks or other securities based on material non-public information.
Insurance Fraud
Insurance fraud involves making false claims or providing misleading information to obtain insurance benefits.
Internet Of Things
The Internet of Things refers to the network of physical devices connected to the internet that can collect and exchange data.
K
K-Means
K-means is a popular clustering algorithm that partitions data into K clusters by minimizing the within-cluster sum of squared distances from each point to the centroid of its cluster.
Know Your Customer
Know Your Customer (KYC) is a process followed by financial institutions to verify and gather relevant information about their customers in order to assess their identity, risk level, and suitability for financial services.
Know Your Customer (Kyc)
KYC is the process of verifying the identity of customers to prevent fraudulent activities, money laundering, and terrorist financing.
Knowledge-Based Systems
Knowledge-based systems are AI systems that utilize expert knowledge and rules to make decisions or recommendations in specific domains, such as fraud detection.
L
Logistic Regression
Logistic Regression is a statistical algorithm used for binary classification problems by estimating the probability of an input belonging to a certain class.
M
Machine Learning
Machine Learning is a subset of Artificial Intelligence (AI) that focuses on getting machines to learn and improve from data without being explicitly programmed.
Machine Learning Algorithms
Machine learning algorithms are mathematical models and techniques used in machine learning to enable automated learning from data and make accurate predictions or classifications.
Model Deployment
Model deployment is the process of integrating and operationalizing machine learning models into existing systems or applications to make real-time predictions or classifications for fraud detection.
Model Evaluation
Model Evaluation is the process of assessing the performance and effectiveness of a Machine Learning model using various metrics and techniques.
Model Optimization
Model optimization is the process of fine-tuning machine learning models to improve their performance and accuracy in fraud detection, often through hyperparameter tuning or algorithm selection.
Model Training
Model training is the process of training machine learning models using labeled data to learn patterns and make accurate predictions or classifications in fraud detection.
Model Validation
Model validation involves verifying and assessing the performance, accuracy, and generalizability of machine learning models for fraud detection using independent test data.
Money Laundering
Money laundering is the process of making illegally-gained proceeds appear legal.
Multi-Factor Authentication
Multi-factor authentication is a security system that requires users to provide multiple forms of identification to access a system or service.
N
Natural Language Processing
Natural Language Processing (NLP) is a field of Artificial Intelligence that focuses on enabling machines to understand, interpret, and generate human language.
Neural Network
Neural Network is a Machine Learning model inspired by the structure and functioning of the human brain, consisting of interconnected nodes (neurons) and layers.
Neural Networks
Neural networks are computational models inspired by the structure and functioning of the human brain, used in machine learning to recognize complex patterns and relationships in data for fraud detection.
O
Overfitting
Overfitting refers to a situation where a Machine Learning model performs extremely well on the training data but fails to generalize well to new or unseen data.
Oversampling
Oversampling is a technique used to mitigate the class imbalance problem in Machine Learning by artificially increasing the number of instances in the minority class.
P
Pattern Recognition
Pattern recognition is the automated identification of patterns or regularities in data, often used in fraud detection to identify abnormal activities.
Payment Card Fraud
Payment card fraud is the unauthorized use of another person's payment card information.
Payment Card Skimming
Payment Card Skimming is a technique used by attackers to steal credit or debit card information by installing malicious devices or software on point-of-sale (POS) systems or ATMs.
Pci Dss (Payment Card Industry Data Security Standard)
PCI DSS is a widely recognized set of security standards designed to ensure the safe handling of payment card information.
Penetration Testing
Penetration testing, or ethical hacking, involves simulating cyber attacks to identify vulnerabilities and weaknesses in a system's security.
Phishing
Phishing is a fraudulent activity where attackers impersonate legitimate entities or organizations to deceive users into revealing sensitive information or performing malicious actions.
Precision
Precision is a metric used to measure the proportion of correctly predicted positive instances out of the total predicted positive instances by a model.
Predictive Modeling
Predictive modeling is the process of creating a mathematical model to predict future outcomes based on historical data.
Privacy Laws
Privacy laws are regulations that govern the collection, use, and protection of personal data.
R
Random Forest
Random Forest is an ensemble learning method that combines multiple Decision Trees to make predictions, resulting in improved accuracy and robustness.
Real-Time Monitoring
Real-time monitoring involves the continuous and immediate observation of events or activities as they occur, enabling prompt detection and response to fraudulent activities.
Recall
Recall is a metric used to measure the proportion of correctly predicted positive instances out of the total actual positive instances in a dataset.
Regularization
Regularization is a technique used in Machine Learning to prevent or reduce overfitting by adding a penalty term to the loss function, discouraging complex or extreme model parameter values.
Regulatory Compliance
Regulatory Compliance refers to the adherence and conformance to laws, regulations, and guidelines set by governmental or industry-specific authorities, ensuring the legality, security, and ethical standards of an organization's operations.
Risk
Risk is the potential for financial loss or harm resulting from fraudulent activities.
Risk Assessment
Risk assessment is the evaluation of potential risks associated with fraudulent activities.
Risk Mitigation
Risk mitigation involves implementing strategies to reduce the likelihood or impact of fraudulent activities.
Risk Score
A risk score is a numerical value that assesses the likelihood of a transaction being fraudulent.
Roc Curve
ROC Curve (Receiver Operating Characteristic Curve) is a graphical representation of the performance of a binary classification model at various classification thresholds.
S
Semi-Supervised Learning
Semi-Supervised Learning is a type of Machine Learning where the model is trained on a combination of labeled and unlabeled data, leveraging both types of information.
Smote
SMOTE (Synthetic Minority Over-sampling Technique) is a popular algorithm used to address the class imbalance problem by generating synthetic instances of the minority class based on the existing instances.
Social Engineering
Social Engineering is a method used by attackers to manipulate or deceive individuals into revealing sensitive information or performing actions that can be exploited for malicious purposes.
Stolen Identity
Stolen identity refers to the unauthorized use of someone else's personal information to commit fraud or deception.
Supervised Learning
Supervised Learning is a type of Machine Learning where the model is trained on labeled data with predefined outcomes, enabling it to make predictions or classifications.
Suspicious Activity
Suspicious activity refers to behavior or actions that are indicative of potential fraud.
Suspicious Activity Report
Suspicious Activity Report (SAR) is a report submitted by financial institutions to relevant authorities when they suspect potentially unlawful or suspicious activities, such as money laundering or terrorist financing.
T
Text Classification
Text Classification is a task in Natural Language Processing (NLP) that involves categorizing text documents into predefined classes or categories.
Token Authentication
Token Authentication is a security method that uses a unique token, often generated by a trusted third party, to authenticate and authorize users, reducing the exposure of sensitive credentials.
Tokenization
Tokenization is the process of replacing sensitive data, such as credit card numbers or social security numbers, with uniquely generated tokens that have no meaningful information, reducing the risk of data exposure.
Transaction
A transaction is an exchange or transfer of goods, services, or funds.
Transaction Fraud
Transaction fraud involves fraudulent activities carried out during financial transactions, such as unauthorized use of credit cards, money laundering, or falsifying transaction records.
Transaction Monitoring
Transaction Monitoring is a process used by financial institutions to review, analyze, and evaluate customer transactions or activities for potential suspicious or fraudulent behavior, ensuring compliance with regulatory requirements.
Two-Factor Authentication
Two-Factor Authentication (2FA) is a security mechanism that requires users to provide two different forms of authentication, typically a password and a unique code sent to their mobile device, to access an account or system.
U
Underfitting
Underfitting refers to a situation where a Machine Learning model is too simple and fails to capture the underlying patterns or relationships in the training data.
Undersampling
Undersampling is a technique used to mitigate the class imbalance problem in Machine Learning by reducing the number of instances in the majority class.
Unsupervised Learning
Unsupervised Learning is a type of Machine Learning where the model learns from unlabeled data, identifying patterns or structures without predefined outcomes.
V
Validation Set
Validation Set is a subset of the data used for evaluating the performance of a Machine Learning model during training, often used to fine-tune the hyperparameters.
Variance
Variance in Machine Learning refers to the variability or spread of predictions across different instances or datasets, often caused by an excessively complex or overfitted model.
W
White Box Model
White Box Model is a Machine Learning model or algorithm where the internal workings, structure, and reasoning behind the predictions are fully transparent, allowing for full visibility and control.