About: Data analysis techniques for fraud detection

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Fraud represents a significant problem for governments and businesses and specialized analysis techniques for discovering fraud using them are required. Some of these methods include knowledge discovery in databases (KDD), data mining, machine learning and statistics. They offer applicable and successful solutions in different areas of electronic fraud crimes.

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rdf:type	company
rdfs:label	Data analysis techniques for fraud detection (en)
rdfs:comment	Fraud represents a significant problem for governments and businesses and specialized analysis techniques for discovering fraud using them are required. Some of these methods include knowledge discovery in databases (KDD), data mining, machine learning and statistics. They offer applicable and successful solutions in different areas of electronic fraud crimes. (en)
dcterms:subject	Applications of artificial intelligence Fraud Data analysis
Wikipage page ID	24932989 (xsd:integer)
Wikipage revision ID	1124249144 (xsd:integer)
Link from a Wikipage to another Wikipage	Beneish M-score Quantile Benford's law Anomaly detection Patterns Peer Group Analysis Decision theory Decision tree learning Applications of artificial intelligence Purchasing card Gap analysis Geolocation software Credit card fraud Labelling Fraud Machine Learning Statistics Cluster analysis Fraud Fraud deterrence Pattern recognition Synthetic data Cell phone Data collection Data mining Data preparation Link analysis Data Mining Bayesian networks False alarm Forensic accountant Form 10-Q Government procurement Data mining Profiling (information science) Regression analysis Tax return User profile Artificial intelligence Artificial intelligence in fraud detection Data analysis Digital data Average Data association Data clustering Data preprocessing Pattern matching Statistical classification Expert system Neural networks Error correction Insurance claim Computational forensics Knowledge Discovery in Databases Neural nets dbr:Break_Point_Analysis dbr:Data_matching
sameAs	Data analysis techniques for fraud detection Data analysis techniques for fraud detection Data analysis techniques for fraud detection Data analysis techniques for fraud detection
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has abstract	Fraud represents a significant problem for governments and businesses and specialized analysis techniques for discovering fraud using them are required. Some of these methods include knowledge discovery in databases (KDD), data mining, machine learning and statistics. They offer applicable and successful solutions in different areas of electronic fraud crimes. In general, the primary reason to use data analytics techniques is to tackle fraud since many internal control systems have serious weaknesses. For example, the currently prevailing approach employed by many law enforcement agencies to detect companies involved in potential cases of fraud consists in receiving circumstantial evidence or complaints from whistleblowers. As a result, a large number of fraud cases remain undetected and unprosecuted. In order to effectively test, detect, validate, correct error and monitor control systems against fraudulent activities, businesses entities and organizations rely on specialized data analytics techniques such as data mining, data matching, the sounds like function, regression analysis, clustering analysis, and gap analysis. Techniques used for fraud detection fall into two primary classes: statistical techniques and artificial intelligence. (en)
gold:hypernym	Business
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page length (characters) of wiki page	18322 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Data_analysis_techniques_for_fraud_detection
is Link from a Wikipage to another Wikipage of	Cash App Beneish M-score String metric Data Analysis Techniques for Fraud Detection Data stream Forensic accounting Profiling (information science) Hamzat Lawal Artificial intelligence for fraud detection THG plc
is Wikipage redirect of	Data Analysis Techniques for Fraud Detection Artificial intelligence for fraud detection
is foaf:primaryTopic of	wikipedia-en:Data_analysis_techniques_for_fraud_detection

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