Behavior-Based Credit Card Fraud Detecting Model

Zhang, Yongbin; You, Fei; Liu, Huaqun

doi:10.1109/ncm.2009.54

Cited by 17 publications

(6 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A series of deviations in behavior represents a stronger predictor of potential events of interest. This is similar to the credit monitoring industry’s (e.g., Lifelock, Privacy Armor) usage of pattern-of-life analysis: looking for unusual financial activity such as large purchases or new accounts being opened to make real-time credit/lending decisions or to detect identity theft (Zhang et al, 2009). Further still, when a networked group of individuals systematically deviates from their standard pattern of life, a collective event may be occurring or immanent.…”

Section: The Pond Frameworkmentioning

confidence: 89%

Information system use antecedents of nursing employee turnover in a hospital setting

Thompson

Holmgren

Ford

2021

Health Care Manage Rev

View full text Add to dashboard Cite

Background: Voluntary turnover (VTO) of nursing employees is expensive for hospital systems and is often associated with lower levels of patient satisfaction, as well as adverse patient outcomes such as falls and medication errors. Purpose: The aim of this study was to establish nurses' electronic medical record (EMR) use patterns and test if they can be used to predict VTO. Methodology/Approach: The study followed 1,836 hospital nurses via the collection of EMR metadata through two 1-month time periods that were 1 year apart. Machine learning algorithms were then used to derive patterns of EMR utilization using VTO as a key variable for classification. Post hoc analysis of the most predictive variables was conducted.Results: The predictive model was effective in identifying which nurses would turnover 73.4% of the time and which nurses would not turnover 84.1% of the time. Practice Applications: The ability to accurately predict nurses' intentions to leave is critical to reducing turnover. Early identification can lead to specific interventions to mitigate factors that are adversely impacting the nursing experience. Post hoc analysis and the key informant interviews indicated that many nurses do not appear to have good EMR navigation skills and spend significant effort in search of patient information.

show abstract

Section: The Pond Frameworkmentioning

confidence: 89%

Information system use antecedents of nursing employee turnover in a hospital setting

Thompson

Holmgren

Ford

2021

Health Care Manage Rev

View full text Add to dashboard Cite

show abstract

“…There were many other approaches in the past and there are going to be many in the future and there is still a lot of scope for this field as the frauds continue to be inevitable. Some of interesting approaches in the past were meta-classifier based fraud detection [23] , fraud detection based on behavior [16] , machine learning models [1], [22], [25] , data mining approaches [4], [25] , neural classification [9] , game-theory approach [21] web services based detection [24] , Hidden Markov Model [11], [14] , Predictive Analysis [19] and so on. In addition, applying some of the most robust classification algorithms [10] such as SVM [27] and ensemble algorithms [20] such as Random Forest [3], [15], [17] and Adaboost [13] was also preferred by a lot of researchers.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Credit Card Fraud detection using Machine Learning models on balanced and imbalanced datasets

2021

IJETER

View full text Add to dashboard Cite

With the advent of modern transaction technology, many are using online transactions to transfer money from one person to another. Credit Card Fraud, a rising problem in the financial department goes unnoticed most of the time. A lot of research is going on in this area.The Credit Card Fraud Detection project is developed to spot whether a new transaction is fraudulent or not with the knowledge of previousdata. We use various predictive models to ascertain how accurate they are in predicting whether a transaction is abnormalor regular. Techniques like Decision Tree, Logistic Regression, SVMand Naïve Bayes are the classification algorithms to detect non-fraud and fraud transactions.

show abstract

“…The results of the study investigations were encouraging in that GAs applied to ANNs for credit card fraud detection improved detection engine performance. [14] suggested a behaviour based credit card fraud detection system. Here the historical behaviour pattern of a customer is used to detect fraud.…”

Section: Related Review On Credit Card Fraud Detectionmentioning

confidence: 99%

Hybrid Design using Counter Propagation Neural Network-Genetic Algorithm Model for the Anomaly Detection in Online Transaction

Amusan

Ogunmakinde

Ojo

et al. 2019

IJASRE

View full text Add to dashboard Cite

In e-commerce, credit card fraud is an evolving challenge. The increase in the number of credit card transactions provides more opportunity for fraudsters to steal credit card numbers and execute fraud. Fraud detection is a continuously evolving discipline to tackle ever changing tactics to commit fraud. Existing fraud detection systems have not been so much efficient to reduce fraud transaction rate. Improvement in fraud detection practices has become essential to maintain existence of payment system. This research designed hybrid of Counter Propagation Neural Network and genetic algorithm (CPNN-GA) for the detection of anomaly in any online transactions.

show abstract

Behavior-Based Credit Card Fraud Detecting Model

Cited by 17 publications

References 1 publication

Information system use antecedents of nursing employee turnover in a hospital setting

Information system use antecedents of nursing employee turnover in a hospital setting

Analysis of Credit Card Fraud detection using Machine Learning models on balanced and imbalanced datasets

Hybrid Design using Counter Propagation Neural Network-Genetic Algorithm Model for the Anomaly Detection in Online Transaction

Contact Info

Product

Resources

About