Fraud prediction in bank loan administration using decision tree

Eweoya, Ibukun; Adebiyi, Ayodele Ariyo; Azeta, A. A.; Azeta, Angela E.

doi:10.1088/1742-6596/1299/1/012037

Cited by 13 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, with the rapid development of artificial intelligence technology, machine learning (ML) algorithms such as logistic regression (LR) [ 23 ], back propagation neural networks (BPNNs) [ 24 ], support vector machines (SVMs) [ 25 ], and decision trees (DTs) [ 26 ] have been employed in the field of financial fraud identification by many scholars. Various deep-learning algorithms have also been employed in financial fraud identification research.…”

Section: Literature Reviewmentioning

confidence: 99%

Financial Fraud Identification Based on Stacking Ensemble Learning Algorithm: Introducing MD&A Text Information

Zhang

Yong

Yong-jun

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In recent years, there have been frequent incidents of financial fraud committed through various means. How to more efficiently identify financial fraud and maintain capital market order is a problem that scholars from all walks of life are discussing and urgently seeking to resolve. In this study, a financial fraud identification model is constructed based on the stacking ensemble learning algorithm, and the text of the management discussion and analysis (MD&A) chapter in annual reports is introduced based on financial and nonfinancial variables, using sentiment polarity, emotional tone, and text readability as text variables. The results show that when considering financial and nonfinancial variables and introducing text variables, the recognition effect of the stacking ensemble learning model constructed in this study is significantly better than the classification results of each single classifier model. In addition, the model recognition effect is better after adding text variables. Therefore, the model is expected to provide a new and more effective method of identifying financial fraud.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Financial Fraud Identification Based on Stacking Ensemble Learning Algorithm: Introducing MD&A Text Information

Zhang

Yong

Yong-jun

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…However, an error must not be mistaken for fraud. Economic sabotage is the term used to describe financial wrongdoing when taken as a whole [3]. It is concerning how quickly microfinance organizations lose money that should have gone to loan recipients because of default.…”

Section: Introductionmentioning

confidence: 99%

“…Case-based, analogy-based, and statistical approaches have been employed; however, they cannot identify 21st-century fraud attempts; thus, data mining tools use the method to anticipate fraud because credit history judging by humans is inefficient owing to the volume and a variety of data. [3].…”

Section: Introductionmentioning

confidence: 99%

Using Data Mining Techniques for Fraud Detection in The Non-banking Sector

Ali,

Raslan

2024

Journal of Computing and Communication

View full text Add to dashboard Cite

A method known as data mining is used to extract knowledge and insights from vast amounts of data. To find different correlational patterns several computational and statistical techniques can be used. Data mining and other tools and techniques, such as artificial intelligence, can be used to do this activity. It tries to reduce the monetary losses these kinds of operations bring and ensure the business complies with all applicable legal and regulatory obligations. To stop fraud from happening, organizations must be able to recognize it before it occurs. This paper explores the various facets of fraud detection and how businesses may use it to stop it from happening. Following that, we discuss the many methods employed in this procedure, including clustering, unsupervised learning, and neural networks. In addition, we discuss the various data pretreatment methods employed in fraud detection. These include feature selection, data normalization, and extraction. Data visualization is crucial for deciphering and comprehending mining analysis outcomes. The study then discusses the numerous data mining applications for fraud detection. These include insurance, financial statement, credit card, and healthcare fraud. We give instances of when these methods have been used to spot fraudulent activity. The limitations of data mining for fraud detection are then discussed. The significance of this technology in the battle against financial crime is highlighted in this article, which offers a thorough review of the various facets of these sectors.

show abstract

“…The rule-based technique has a high proportion of falsepositive outcomes and is time-consuming and expensive. However, these techniques are gradually losing their effectiveness as criminal behaviour patterns and operating procedures get more sophisticated [16]. The emphasis has shifted away from conventional, rule-based approaches to more advanced computational methods.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Machine Learning Models for Cyber Threat Detection and Prevention in Mobile Money Services

Akinyemi

Olalere²,

Sanni

et al. 2023

IJCAI

View full text Add to dashboard Cite

In this paper, an investigation was made to evaluate the effectiveness of the different classifiers suitable to predict the probability of a cyber-threat or fraudulent intent applicant during the Mobile Money Service on-boarding or service activation process, with the goal of determining the best machine learning model for the predictive model solution. Experimental work was carried out by formulating cyber threat predictive models using six supervised machine learning algorithms: Logistic regression(LR), Naïve Bayes, Shallow Neural Network (SNN), Deep Neural Network (DNN), Classification and Regression Trees (CART) and Random Forest (RF) of different configurations. Each model was simulated with both Synthetic Minority Operation Techniques (SMOTE) and without SMOTE (No-SMOTE) on 25,000 records of mobile money applicants. Twenty-four ( 24) different configurations of the formulated predictive models were simulated and evaluated using the Python programming language. Simulation results of the predictive models proved that the Random Forest model multiclass configurations with the SMOTE dataset outperformed all other configurations. The results also showed that the multiclass experiments with SMOTE had better performance than the binary configurations with NO-SMOTE in the predictive models. The study concluded that using the Random Forest-based predictive machine learning model will increase the security level of the Mobile Money solution by detecting and preventing anomalous customer registrations during the unbanked onboarding process.Povzetek: Napovedovanje kibernetskih groženj mobilnega denarja z uporabo algoritmov strojnega učenja. 22 RF Binary-SMOTE Random Forest(RF) with Binary feature configuration and with SMOTE application to Dataset 23 RF Multiclass-No SMOTE Random Forest(RF) with Multiclass feature configuration and No-SMOTE application to Dataset 24 RF Multiclass-SMOTE Random Forest(RF) with Multiclass feature configuration and with SMOTE application to Dataset

show abstract

Fraud prediction in bank loan administration using decision tree

Cited by 13 publications

References 10 publications

Financial Fraud Identification Based on Stacking Ensemble Learning Algorithm: Introducing MD&A Text Information

Financial Fraud Identification Based on Stacking Ensemble Learning Algorithm: Introducing MD&A Text Information

Using Data Mining Techniques for Fraud Detection in The Non-banking Sector

Performance Evaluation of Machine Learning Models for Cyber Threat Detection and Prevention in Mobile Money Services

Contact Info

Product

Resources

About