Credit Card Fraud Detection with Artificial Immune System

Gadi, Manoel Fernando Alonso; Wang, Xidi; Lago, Alair Pereira do

doi:10.1007/978-3-540-85072-4_11

Cited by 74 publications

(56 citation statements)

References 5 publications

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“…The results reinforce adaptive spike detection and its effectiveness for ranking and selection [4]. In This Paper [5], they apply Arterial Immune Systems (AIS) for credit card fraud detection and compare it to other methods such as Bayesian Nets (BN) and Neural Nets (NN), Decision Trees (DT) and Naive Bayes. Genetic Algorithm (GA) and Exhaustive search are used to select optimized parameters sets, which minimizes the fraud cost basis on a credit card database, which is provided by a Brazilian card issuer.…”

Section: Different Techniques For Fraud Detectionmentioning

confidence: 58%

“…Neural Nets is usually widely used in the market today. So this Arterial Immune Systems has given the best performance when parameters optimized by Genetic Algorithm are used [5]. This paper [6] presents a system which is able to prevent subscription fraud in fixed telecommunications with the high impact on long distance carriers.…”

Section: Different Techniques For Fraud Detectionmentioning

confidence: 99%

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Survey on Fraud Detection Techniques Using Data Mining

Arif¹,

Dar²

2015

IJUNESST

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Section: Different Techniques For Fraud Detectionmentioning

confidence: 58%

Section: Different Techniques For Fraud Detectionmentioning

confidence: 99%

Survey on Fraud Detection Techniques Using Data Mining

Arif¹,

Dar²

2015

IJUNESST

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“…A neural network-based method on the parallel machines to facilitate rule production for the credit card fraud detection has been proposed in [11].…”

Section: Neural Networkmentioning

confidence: 99%

An Overview of the Bank Fraud and its Detection Techniques Through Data Mining

Nejad¹,

Nikbakht²

2017

IJMNCT

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“…In recent years, many bioinspired algorithms are presented for solving classification problems such as credit card fraud detection [37], credit scoring, security, and other applications [38]. Among these approaches, AIS is one of the newest methods that has been applied for the credit scoring purposes.…”

Section: Literature Reviewmentioning

confidence: 99%

Credit Risk Prediction Using Fuzzy Immune Learning

Kamalloo

Abadeh

2014

Advances in Fuzzy Systems

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The use of credit has grown considerably in recent years. Banks and financial institutions confront credit risks to conduct their business. Good management of these risks is a key factor to increase profitability. Therefore, every bank needs to predict the credit risks of its customers. Credit risk prediction has been widely studied in the field of data mining as a classification problem. This paper proposes a new classifier using immune principles and fuzzy rules to predict quality factors of individuals in banks. The proposed model is combined with fuzzy pattern classification to extract accurate fuzzy if-then rules. In our proposed model, we have used immune memory to remember good B cells during the cloning process. We have designed two forms of memory: simple memory and k-layer memory. Two real world credit data sets in UCI machine learning repository are selected as experimental data to show the accuracy of the proposed classifier. We compare the performance of our immune-based learning system with results obtained by several well-known classifiers. Results indicate that the proposed immune-based classification system is accurate in detecting credit risks.

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Credit Card Fraud Detection with Artificial Immune System

Cited by 74 publications

References 5 publications

Survey on Fraud Detection Techniques Using Data Mining

Survey on Fraud Detection Techniques Using Data Mining

An Overview of the Bank Fraud and its Detection Techniques Through Data Mining

Credit Risk Prediction Using Fuzzy Immune Learning

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