A two‐step system for direct bank telemarketing outcome classification

Lahmiri, Salim

doi:10.1002/isaf.1403

Cited by 19 publications

(12 citation statements)

References 48 publications

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“…Moreover, a profit driven artificial NN approach was proposed in [62] and a similar study applied two steps model of K-mean clustering and classification [63]. Recently, Lahmiri [64] proposed a two step system that combined a NN ensemble model and Particle Swarm Optimization for optimizing the initial weights of each NN in the ensemble framework. This was also verified by the bank direct marketing data with outstanding performance in relation to the baseline approaches.…”

Section: Customer Development and Customizationmentioning

confidence: 99%

Digitalisation and Big Data Mining in Banking

Hassani

Huang

Silva

2018

BDCC

117

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Banking as a data intensive subject has been progressing continuously under the promoting influences of the era of big data. Exploring the advanced big data analytic tools like Data Mining (DM) techniques is key for the banking sector, which aims to reveal valuable information from the overwhelming volume of data and achieve better strategic management and customer satisfaction. In order to provide sound direction for the future research and development, a comprehensive and most up to date review of the current research status of DM in banking will be extremely beneficial.Since existing reviews only cover the applications until 2013, this paper aims to fill this research gap and presents the significant progressions and most recent DM implementations in banking post 2013. By collecting and analyzing the trends of research focus, data resources, technological aids, and data analytical tools, this paper contributes to bringing valuable insights with regard to the future developments of both DM and the banking sector along with a comprehensive one stop reference table. Moreover, we identify the key obstacles and present a summary for all interested parties that are facing the challenges of big data.

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Section: Customer Development and Customizationmentioning

confidence: 99%

Digitalisation and Big Data Mining in Banking

Hassani

Huang

Silva

2018

BDCC

117

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“…They used a regression model to process and select important features that affect business failure prediction and then, a probabilistic neural network model was adopted to achieve high probability of success for data classification. To reduce the risk of noise in learning processes of algorithms, Lahmiri () proposed a two – step prediction approach with an application on bank telemarketing problem. The study shows that two – step system is robust to nonlinear and noisy data and it is suitable to make fast and easy prediction for large datasets.…”

Section: Proposed Technique: Data Mining Based Outlier Detectionmentioning

confidence: 99%

How to detect illegal corporate insider trading? A data mining approach for detecting suspicious insider transactions

Esen

Bilgiç

Basdas³

2019

Intell Sys Acc Fin Mgmt

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Summary Only in the U.S. Stock Exchanges, the daily average trading volume is about 7 billion shares. This vast amount of trading shows the necessity of understanding the hidden insights in the data sets. In this study, a data mining technique, clustering based outlier analysis is applied to detect suspicious insider transactions. 1,244,815 transactions of 61,780 insiders are analysed, which are acquired from Thomson Financial, covering a period of January 2010–April 2017. In order to detect outliers, similar transactions are grouped into the same clusters by using a two‐step clustering based outlier detection technique, which is an integration of k‐means and hierarchical clustering. Then, it is shown that outlying transactions earn higher abnormal returns than non‐outlying transactions by using event study methodology.

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“…The purpose of the current work is to study the effectiveness of various ensemble learning and classification systems in the context of financial data classification; especially in corporate bankruptcy prediction and credit scoring which have previously received a large attention (Abdou & Poiton, ; Çelik, ; Davalos, Leng, Feroz, & Cao, ; Figini, Savona, & Vezzoli, ; Lahmiri, ; Lahmiri, ; Lahmiri & Bekiros, ; Lahmiri & Gagnon, ; Mendes, Cardoso, Mário, Martinez, & Ferreira, ; Peat & Jones, ; Pendharkar, ; Quek, Zhou, & Lee, ; Savona & Vezzoli, ; Sun, ; Trinkle & Baldwin, ).…”

Section: Introductionmentioning

confidence: 99%

Performance assessment of ensemble learning systems in financial data classification

Lahmiri

Bekiros

Giakoumelou

et al. 2020

Intell Sys Acc Fin Mgmt

Self Cite

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Summary Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.

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A two‐step system for direct bank telemarketing outcome classification

Cited by 19 publications

References 48 publications

Digitalisation and Big Data Mining in Banking

Digitalisation and Big Data Mining in Banking

How to detect illegal corporate insider trading? A data mining approach for detecting suspicious insider transactions

Performance assessment of ensemble learning systems in financial data classification

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