Anomaly Detection Algorithms
Expert-defined terms from the Fraud Detection and Prevention course at LearnUNI. Free to read, free to share, paired with a professional course.
Abnormality #
in the context of fraud detection, an abnormality refers to a pattern or behavior that deviates from the expected or normal behavior, often indicating a potential threat or anomaly. Related terms: anomaly, outlier, deviation.
Accuracy #
a measure of how close a model or algorithm's predictions are to the actual values, often used to evaluate the performance of fraud detection systems. Related terms: precision, recall, F1 score.
Actionable Insight #
information or knowledge that can be used to inform decisions or take specific actions to prevent or mitigate fraud. Related terms: business intelligence, data analysis.
Activity Monitoring #
the process of tracking and analyzing user or system activity to identify potential security threats or fraudulent behavior. Related terms: logging, auditing, surveillance.
Adversarial Attack #
a type of cyber attack where an attacker attempts to evade or mislead a fraud detection system by manipulating input data or exploiting vulnerabilities. Related terms: evasion attack, spoofing attack.
Aggregation #
the process of combining multiple data points or values into a single value or summary statistic, often used in fraud detection to identify patterns or trends. Related terms: data reduction, summarization.
Algorithmic Modeling #
the use of mathematical or statistical algorithms to develop models that can detect or predict fraudulent behavior. Related terms: machine learning, predictive modeling.
Anomaly Detection #
the process of identifying patterns or behavior that deviate from the expected or normal behavior, often indicating a potential threat or fraud. Related terms: outlier detection, deviation detection.
Anomaly Detection Algorithms #
a type of algorithm designed to identify anomalies or patterns that deviate from the expected behavior, often used in fraud detection systems. Related terms: machine learning algorithms, statistical models.
Application Security #
the practice of designing and implementing security measures to protect applications from cyber threats or fraudulent activity. Related terms: software security, application firewall.
Artificial Intelligence #
a broad field of study focused on developing intelligent systems that can mimic human thought or behavior, often used in fraud detection to develop predictive models. Related terms: machine learning, deep learning.
Asset Protection #
the practice of implementing security measures to protect assets from cyber threats or fraudulent activity, such as data or systems. Related terms: asset management, risk management.
Authentication #
the process of verifying the identity of a user or entity, often used in fraud detection to prevent impersonation or identity theft. Related terms: authorization, identity management.
Authorization #
the process of granting or denying access to resources or systems based on a user's identity or role, often used in fraud detection to prevent unauthorized access. Related terms: access control, identity management.
Automated Decisioning #
the use of algorithms or models to make decisions automatically, often used in fraud detection to streamline the decision-making process. Related terms: decision support systems, business rules management.
Batch Processing #
a method of processing transactions or data in batches, often used in fraud detection to efficiently process large volumes of data. Related terms: real-time processing, streaming processing.
Behavioral Analysis #
the study of behavior or patterns to identify potential threats or fraudulent activity, often used in fraud detection to develop predictive models. Related terms: behavioral biometrics, psychographic analysis.
Binary Classification #
a type of classification problem where the goal is to predict one of two classes or labels, often used in fraud detection to predict fraud or legitimate transactions. Related terms: multi-class classification, regression analysis.
Blacklist #
a list of entities or IP addresses that are known to be malicious or fraudulent, often used in fraud detection to block or flag suspicious activity. Related terms: whitelist, greylist.
Business Intelligence #
the practice of using data and analytics to inform business decisions and drive growth, often used in fraud detection to identify threats and opportunities. Related terms: business analytics, data science.
Business Rules Management #
the practice of defining, managing, and executing business rules to automate decision-making processes, often used in fraud detection to streamline the decision-making process. Related terms: decision support systems, automated decisioning.
Card Verification Value #
a security code used to verify the identity of a credit or debit card holder, often used in fraud detection to prevent card fraud. Related terms: card security code, card identification number.
Case Management #
the practice of managing and investigating cases of fraud or suspicious activity, often used in fraud detection to streamline the investigation process. Related terms: incident response, case analysis.
Cloud Computing #
a model of delivering computing services over the internet, often used in fraud detection to scalably process large volumes of data. Related terms: cloud storage, cloud security.
Clustering Analysis #
a type of data analysis that groups similar data points or objects into clusters, often used in fraud detection to identify patterns or anomalies. Related terms: k-means clustering, hierarchical clustering.
Compliance #
the practice of adhering to regulations or standards to prevent or detect fraud, often used in fraud detection to ensure regulatory compliance. Related terms: regulatory compliance, risk compliance.
Credit Scoring #
a method of evaluating an individual's or business creditworthiness, often used in fraud detection to predict the likelihood of default or fraud. Related terms: credit worthiness, credit risk assessment.
Cross #
Validation: a technique used to evaluate the performance of a model or algorithm by training and testing it on multiple datasets, often used in fraud detection to improve model accuracy. Related terms: overfitting, underfitting.
Customer Due Diligence #
the practice of verifying the identity and legitimacy of customers, often used in fraud detection to prevent money laundering or terrorist financing. Related terms: know-your-customer, anti-money laundering.
Data Analytics #
the practice of analyzing data to extract insights and meaning, often used in fraud detection to identify patterns or trends. Related terms: data science, data mining.
Data Encryption #
the practice of protecting data by converting it into an unreadable format, often used in fraud detection to prevent data breaches. Related terms: data protection, data security.
Data Masking #
a technique used to protect sensitive data by masking or obscuring it, often used in fraud detection to prevent data exposure. Related terms: data obfuscation, data anonymization.
Data Minification #
the process of reducing the size of data while preserving its meaning, often used in fraud detection to improve data processing efficiency. Related terms: data compression, data aggregation.
Data Quality #
the practice of ensuring that data is accurate, complete, and consistent, often used in fraud detection to improve model accuracy. Related terms: data validation, data cleansing.
Data Warehouse #
a centralized repository of data from various sources, often used in fraud detection to store and analyze large volumes of data. Related terms: data mart, data lake.
Decision Support Systems #
a type of system that uses data and analytics to support decision-making processes, often used in fraud detection to streamline the decision-making process. Related terms: business rules management, automated decisioning.
Deep Learning #
a type of machine learning that uses neural networks to analyze data, often used in fraud detection to develop predictive models. Related terms: artificial intelligence, machine learning.
Deviation Detection #
the process of identifying patterns or behavior that deviate from the expected or normal behavior, often used in fraud detection to identify potential threats or anomalies. Related terms: anomaly detection, outlier detection.
Digital Forensics #
the practice of analyzing digital evidence to investigate cyber crimes or fraudulent activity, often used in fraud detection to gather evidence and build cases. Related terms: computer forensics, cyber forensics.
Discrete Wavelet Transform #
a mathematical technique used to decompose signals or data into different frequencies, often used in fraud detection to identify patterns or trends. Related terms: wavelet analysis, signal processing.
Ensemble Methods #
a type of machine learning that combines multiple models or algorithms to improve predictive accuracy, often used in fraud detection to develop robust models. Related terms: bagging, boosting.
Entity Disambiguation #
the process of identifying and resolving entities with similar names or attributes, often used in fraud detection to prevent identity theft or confusion. Related terms: entity resolution, entity matching.
False Negative #
a type of error where a legitimate transaction is incorrectly flagged as fraudulent, often used in fraud detection to evaluate the performance of models or algorithms. Related terms: false positive, true negative.
False Positive #
a type of error where a fraudulent transaction is incorrectly flagged as legitimate, often used in fraud detection to evaluate the performance of models or algorithms. Related terms: false negative, true positive.
Feature Engineering #
the process of selecting and transforming raw data into features that can be used to develop predictive models, often used in fraud detection to improve model accuracy. Related terms: feature selection, feature extraction.
Feedforward Networks #
a type of neural network where data flows only in one direction, often used in fraud detection to develop predictive models. Related terms: recurrent networks, convolutional networks.
Financial Crime #
a type of crime that involves financial transactions or instruments, often used in fraud detection to prevent money laundering or terrorist financing. Related terms: financial regulation, financial compliance.
Fuzzy Logic #
a mathematical approach that uses fuzzy sets and logic to make decisions under uncertainty, often used in fraud detection to develop predictive models. Related terms: fuzzy sets, fuzzy rules.
Genetic Algorithm #
a type of optimization technique that uses evolutionary principles to find the best solution, often used in fraud detection to develop predictive models. Related terms: genetic programming, evolutionary computation.
Graph Theory #
a branch of mathematics that studies graphs and their applications, often used in fraud detection to identify patterns or relationships in data. Related terms: graph mining, graph analysis.
Greylist #
a list of entities or IP addresses that are suspected to be malicious or fraudulent, but require further investigation, often used in fraud detection to flag or block suspicious activity. Related terms: blacklist, whitelist.
Hash Function #
a mathematical function that takes input data and produces a fixed-size string of characters, often used in fraud detection to protect data or verify identities. Related terms: hash code, hash value.
Identity Theft #
a type of crime where an individual's identity is stolen or misused, often used in fraud detection to prevent identity theft or impersonation. Related terms: identity fraud, identity misuse.
Incident Response #
the process of responding to and managing incidents of fraud or cyber crime, often used in fraud detection to contain and mitigate damage. Related terms: incident management, incident handling.
Information Entropy #
a measure of the uncertainty or randomness of a system or process, often used in fraud detection to identify patterns or anomalies. Related terms: information theory, entropy measure.
Insider Threat #
a type of threat that comes from within an organization, often used in fraud detection to prevent insider fraud or sabotage. Related terms: insider attack, insider threat detection.
Insurance Fraud #
a type of fraud that involves insurance claims or policies, often used in fraud detection to prevent insurance fraud or abuse. Related terms: insurance claim fraud, insurance policy fraud.
Integrity Constraint #
a rule or constraint that ensures the accuracy and consistency of data, often used in fraud detection to prevent data manipulation or corruption. Related terms: data integrity, data quality.
Internet Protocol Address #
a unique address assigned to a device on a network, often used in fraud detection to track or identify devices. Related terms: IP address, IP tracking.
Intrusion Detection System #
a system that detects and alerts on potential security threats or intrusions, often used in fraud detection to prevent cyber attacks or intrusions. Related terms: intrusion prevention system, intrusion detection and prevention system.
K- Means Clustering: a type of clustering algorithm that groups sim… #
Related terms: k-means algorithm, clustering analysis.
Know #
Your-Customer: a practice of verifying the identity and legitimacy of customers, often used in fraud detection to prevent money laundering or terrorist financing. Related terms: customer due diligence, anti-money laundering.
Machine Learning #
a type of artificial intelligence that uses algorithms to learn from data and make predictions, often used in fraud detection to develop predictive models. Related terms: deep learning, neural networks.
Mahalanobis Distance #
a measure of the distance between a point and the center of a distribution, often used in fraud detection to identify anomalies or outliers. Related terms: mahalanobis metric, distance metric.
Malicious Activity #
a type of activity that is intended to harm or exploit a system or user, often used in fraud detection to prevent cyber attacks or malware. Related terms: malicious code, malicious software.
Maximum Likelihood Estimation #
a statistical technique used to estimate the parameters of a model, often used in fraud detection to develop predictive models. Related terms: maximum likelihood method, likelihood estimation.
Model Validation #
the process of evaluating the performance of a model or algorithm, often used in fraud detection to ensure the accuracy and reliability of models. Related terms: model evaluation, model selection.
Money Laundering #
a type of crime that involves hiding or disguising the source of money, often used in fraud detection to prevent money laundering or terrorist financing. Related terms: money laundering detection, anti-money laundering.
Neural Networks #
a type of machine learning that uses neural networks to analyze data, often used in fraud detection to develop predictive models. Related terms: deep learning, artificial intelligence.
Network Intrusion Detection #
a type of system that detects and alerts on potential security threats or intrusions, often used in fraud detection to prevent cyber attacks or intrusions. Related terms: network intrusion prevention, intrusion detection and prevention system.
Neural Network Architecture #
the design and structure of a neural network, often used in fraud detection to develop predictive models. Related terms: neural network design, neural network implementation.
Nearest Neighbor Algorithm #
a type of algorithm that finds the most similar data points or objects to a given input, often used in fraud detection to identify patterns or trends. Related terms: k-nearest neighbor algorithm, nearest neighbor search.
Normal Distribution #
a statistical distribution that is commonly used to model continuous data, often used in fraud detection to identify anomalies or outliers. Related terms: normal curve, gaussian distribution.
Online Learning #
a type of machine learning that involves training a model on data as it becomes available, often used in fraud detection to develop real-time models. Related terms: online training, incremental learning.
Outlier Detection #
the process of identifying data points or objects that are significantly different from the rest of the data, often used in fraud detection to identify anomalies or fraudulent activity. Related terms: anomaly detection, deviation detection.
Overfitting #
a phenomenon where a model is too complex and fits the noise in the data, often used in fraud detection to prevent overfitting and improve model generalization. Related terms: underfitting, regularization.
Pattern Recognition #
the process of identifying patterns or relationships in data, often used in fraud detection to identify anomalies or fraudulent activity. Related terms: pattern analysis, pattern matching.
Payment Card Industry Data Security Standard #
a set of security standards for the payment card industry, often used in fraud detection to prevent credit card fraud or data breaches. Related terms: PCI DSS, payment security.
Phishing #
a type of cyber attack that involves tricking users into revealing sensitive information, often used in fraud detection to prevent phishing attacks or identity theft. Related terms: phishing attack, phishing scam.
Principal Component Analysis #
a statistical technique used to reduce the dimensionality of data, often used in fraud detection to identify patterns or trends. Related terms: principal component regression, principal component analysis.
Privacy Enhancing Technologies #
a set of technologies designed to protect user privacy, often used in fraud detection to prevent data breaches or identity theft. Related terms: privacy protection, data protection.
Probabilistic Modeling #
a type of modeling that uses probability theory to make predictions, often used in fraud detection to develop predictive models. Related terms: probabilistic graphical models, probabilistic programming.
Random Forest #
a type of machine learning that uses multiple trees to make predictions, often used in fraud detection to develop predictive models. Related terms: random forest algorithm, random forest classifier.
Real #
time Processing: the ability to process and analyze data in real-time, often used in fraud detection to prevent fraud or cyber attacks. Related terms: real-time analytics, real-time decision making.
Recommendation Systems #
a type of system that provides personalized recommendations to users, often used in fraud detection to prevent fraud or abuse. Related terms: recommendation algorithms, recommendation engines.
Regression Analysis #
a statistical technique used to model the relationship between a dependent variable and one or more independent variables, often used in fraud detection to identify patterns or trends. Related terms: regression modeling, regression analysis.
Relational Databases #
a type of database that stores data in tables with well-defined relationships, often used in fraud detection to store and analyze data. Related terms: relational database management, relational database systems.
Risk Assessment #
the process of identifying and evaluating potential risk or threats, often used in fraud detection to prevent fraud or cyber attacks. Related terms: risk management, risk mitigation.
Robust Statistics #
a branch of statistics that deals with outliers and anomalies in data, often used in fraud detection to identify patterns or trends. Related terms: robust estimation, robust regression.
Root Cause Analysis #
a method of identifying the underlying cause of a problem or issue, often used in fraud detection to identify the source of fraud or cyber attacks. Related terms: root cause identification, root cause analysis.
Rule #
Based Systems: a type of system that uses rules to make decisions, often used in fraud detection to prevent fraud or cyber attacks. Related terms: rule-based reasoning, rule-based systems.
Security Information and Event Management #
a type of system that monitors and analyzes security events and information, often used in fraud detection to prevent cyber attacks or security breaches. Related terms: security information management, security event management.
Self #
Organizing Maps: a type of neural network that uses self-organizing