Statistical Gait Analysis Based on Surface Electromyography

Agostini, Valentina; Ghislieri, Marco; Rosati, Samanta; Balestra, Gabriella; Dotti, Gregorio; Knaflitz, Marco

doi:10.1007/978-3-030-87845-0_2

Cited by 1 publication

(1 citation statement)

References 36 publications

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“…Fallback fraud is reportedly already on the increase in EMV enabled markets. Fallback happens when the chip on an EMV card is damaged and systems have to fall back on magstripe in an attempt to authorise the transaction [7]. Some people claim that up to 45% of ATM transactions in EMV enabled markets have to fall back on magstripe, while other banks report absurd fallback figures of close to 100% in some cases [8].…”

Section: Research Objectivesmentioning

confidence: 99%

Credit Card Fraud Detection Using LSTM Algorithm

Mohmad

2022

WJCMS

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With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this search is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behavior with respect to two aspects: the probability of single and consecutive missed payments for credit card customers. The proposed model is based on the bidirectional Long-Short Term Memory (LSTM) model to give the probability of a missed payment during the next month for each customer. The model was trained on a real credit card dataset and the customer behavioral scores are analyzed using classical measures such as accuracy, Area Under the Curve, Brier score, Kolmogorov–Smirnov test, and H-measure. Calibration analysis of the LSTM model scores showed that they can be considered as probabilities of missed payments. The LSTM model was compared to four traditional machine learning algorithms: support vector machine, random forest, multi-layer perceptron neural network, and logistic regression. Experimental results show that, compared with traditional methods, the consumer credit scoring method based on the LSTM neural network has significantly improved consumer credit scoring.

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Section: Research Objectivesmentioning

confidence: 99%

Credit Card Fraud Detection Using LSTM Algorithm

Mohmad

2022

WJCMS

View full text Add to dashboard Cite

show abstract

Statistical Gait Analysis Based on Surface Electromyography

Cited by 1 publication

References 36 publications

Credit Card Fraud Detection Using LSTM Algorithm

Credit Card Fraud Detection Using LSTM Algorithm

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