Realtime Fraud Detection in the Banking Sector Using Data Mining Techniques/Algorithm

2016 ◽  
Author(s):  
S.N. John ◽  
C. Anele ◽  
O. Okokpujie Kennedy ◽  
F. Olajide ◽  
Chinyere Grace Kennedy
Keyword(s):  
Data Mining ◽  
Banking Sector ◽  
Fraud Detection ◽  
Data Mining Techniques ◽  
Using Data

scholarly journals Performance Analysis and Prediction Student Performance to build effective student Using Data Mining Techniques

2019 ◽  
Vol 3 (2) ◽  
pp. 10
Author(s):  
Ardalan Husin Awlla
Keyword(s):  
Data Mining ◽  
Performance Analysis ◽  
Student Performance ◽  
Fraud Detection ◽  
Classification Model ◽  
Data Mining Techniques ◽  
Incipient Stage ◽  
The Everyday ◽  
Using Data

In this period of computerization, schooling has additionally remodeled itself and is not restrained to old lecture technique. The everyday quest is on to discover better approaches to make it more successful and productive for students. These days, masses of data are gathered in educational databases, however it stays unutilized. To be able to get required advantages from such major information, effective tools are required. Data mining is a developing capable tool for examination and expectation. It is effectively applied in the field of fraud detection, marketing, promoting, forecast and loan assessment. However, it is in incipient stage in the area of education. In this paper, data mining techniques have been applied to construct a classification model to predict the performance of students.

Data mining techniques for predicting the financial performance of Islamic banking in Indonesia

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammed Ayoub Ledhem
Keyword(s):  
Data Mining ◽  
Financial Performance ◽  
Banking Sector ◽  
Capital Adequacy ◽  
Future Research ◽  
Islamic Banking ◽  
Accuracy Score ◽  
Content Type ◽  
Data Mining Techniques ◽  
Using Data

Purpose The purpose of this paper is to apply various data mining techniques for predicting the financial performance of Islamic banking in Indonesia through the main exogenous determinants of profitability by choosing the best data mining technique based on the criteria of the highest accuracy score of testing and training. Design/methodology/approach This paper used data mining techniques to predict the financial performance of Islamic banking by applying all of LASSO regression, random forest (RF), artificial neural networks and k-nearest neighbor (KNN) over monthly data sets of all the full-fledged Islamic banks working in Indonesia from January 2011 until March 2020. This study used return on assets as a real measurement of financial performance, whereas the capital adequacy ratio, asset quality and liquidity management were used as exogenous determinants of financial performance. Findings The experimental results showed that the optimal task for predicting the financial performance of Islamic banking in Indonesia is the KNN technique, which affords the best-predicting accuracy, and gives the optimal knowledge from the financial performance of Islamic banking determinants in Indonesia. As well, the RF provides closer values to the optimal accuracy of the KNN, which makes it another robust technique in predicting the financial performance of Islamic banking. Research limitations/implications This paper restricted modeling the financial performance of Islamic banking to profitability through the main determinants of return of assets in Indonesia. Future research could consider enlarging the modeling of financial performance using other models such as CAMELS and Z-Score to predict the financial performance of Islamic banking under data mining techniques. Practical implications Owing to the lack of using data mining techniques in the Islamic banking sector, this paper would fill the literature gap by providing new effective techniques for predicting financial performance in the Islamic banking sector using data mining approaches, which can be efficient tools in business and management modeling for financial researchers and decision-makers in the Islamic banking sector. Originality/value According to the author’s knowledge, this paper is the first that provides data mining techniques for predicting the financial performance of the Islamic banking sector in Indonesia.

Using Data Mining Techniques in Fiscal Fraud Detection

1999 ◽  
pp. 369-376 ◽  
Author(s):  
F. Bonchi ◽  
F. Giannotti ◽  
G. Mainetto ◽  
D. Pedreschi
Keyword(s):  
Data Mining ◽  
Fraud Detection ◽  
Data Mining Techniques ◽  
Using Data
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