A data mining application to deposit pricing: Main determinants and prediction models

Batmaz, İnci; Danışoğlu, Seza; Yazıcı, Ceyda; Kartal-Koç, Elçin

doi:10.1016/j.asoc.2017.07.047

Cited by 15 publications

(7 citation statements)

References 50 publications

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“…Other advanced supports [76][77][78][79][80][81][82][83][84] classification (NN, DT, SVM), k-mean clustering Nigeria [77], Turkey [78,81], Canada [80], ASEAN [82], Islamic banks [83], BRICS [84], US [79] Branch strategy, bank efficiency evaluation, deposit pricing, early warning of failing bank.…”

Section: Customer Development and Customizationmentioning

confidence: 99%

“…A less mainstream application by Batmaz et al [78] focused on the DM application for deposit pricing and identifying its main determinants. This research was conducted on the customer level data set of a commercial bank in Turkey, and beneficial conclusions for strategic deposit pricing were achieved, contrary to existing evidence obtained from macro-level bank data.…”

Section: Other Advanced Supportsmentioning

confidence: 99%

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Digitalisation and Big Data Mining in Banking

Hassani

Huang

Silva

2018

BDCC

115

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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%

Section: Other Advanced Supportsmentioning

confidence: 99%

Digitalisation and Big Data Mining in Banking

Hassani

Huang

Silva

2018

BDCC

115

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“…There are a number of techniques that can be used in the process of data mining and machine learning. Some of these methods include but are not limited to:  Decision trees [34] : These are tree flow-chart like structures which contains nodes which may have child nodes [2]. Essentially, decision trees use the tree-like structure to represent the attributes and possible outcomes [35].…”

Section: Data Mining and Machine Learning Methodsmentioning

confidence: 99%

“… Random Forest: Random forests are based on decision tree classifiers and are also known as Classification and Regression Trees (CART) [36]. Random forests use a number of decision trees to improve a prediction models performance [34]. In the collection of decision trees, each tree is created by first selecting a small group of independent variables randomly to split on at each node and the calculating the best split [6].…”

Section: Equation 1: Bayesian Theorem Algorithmmentioning

confidence: 99%

Design and Development of an optimal algorithm to assign applicants to suitable teaching positions

Chishimba

Kunda

2018

zictjournal

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Resource allocation has always been an area of interest in the area of computing. Areas such as machine learning provide many solutions to the problem of resource allocation. The issue addressed in this study is the issue of optimal allocation of applicants (teachers) to positions in schools where their area of specialization will be better applied. We develop an algorithm that is able to allocate applicants to schools based on the applicant qualifications and the school's needs. We use the principles of resource allocation and machine learning in order to create an application to allocate applicants to schools where their qualifications are most suited. Methods used include classification techniques in machine learning, regression and similarity comparison. For the identification of subjects an applicant in proficient in, various machine learning algorithms are tested to determine which machine learning algorithm was best. The actual process of identifying which applicant qualifies for a school position is also tested against sequential assignment if applicants to schools. The results of this was the algorithm based assignment of applicants to schools which produced more accurate assignment of applicants to schools than the sequential assignment of applicants. The aim of this algorithm is to provide a solution to that automatically identifies the needs (subjects) of a school, determine which needs have a higher priority, identify the qualifications of the applicants and assign the applicants to the school according to the school's needs and the applicant's qualifications.

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“…Thus, the dynamic generations of behavior resulted from this nonlinearity on the P2P rental platform, and identification of key determinants’ influence on room pricing becomes more complex. Nonparametric approaches are a better alternative in the Airbnb listing data sets for the existence of nonlinearity and/or buried multicollinearity, and the nonlinear, nonparametric approaches do not require any stringent assumptions concerning the form of the data distribution of the variables involved in the models (Batmaz et al, 2017). MARS and RF are able to fit complex and nonlinear association between the dependent variable (room pricing) and determinants (listing attributes; Friedman, 1991; C.…”

Section: Theoretical Background and Literature Surveymentioning

confidence: 99%

What Airbnb Host Listings Influence Peer-to-Peer Tourist Accommodation Price?

Chattopadhyay

Mitra

2020

Journal of Hospitality & Tourism Research

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Recognizing that the pricing strategy of the newly emerging online shared accommodation industry would be different from that of the traditional hotel industry, this study attempted to identify the variables that are the main determinants of the peer-to-peer tourist accommodation price. Using a data set of Airbnb accommodation listings for Toronto, the study established a relationship between room pricing and various listing variables and identified a reduced number of listing attributes that influence the room price significantly. Focusing on a reduced number of important variables, Airbnb hosts can not only increase average profit but would also give tourists a better rental experience. Along with traditional multiple regressions approach, the study also applied two different approaches and found that the analysis of hedonic pricing using nonlinear and nonparametric approaches is quite promising.

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A data mining application to deposit pricing: Main determinants and prediction models

Cited by 15 publications

References 50 publications

Digitalisation and Big Data Mining in Banking

Digitalisation and Big Data Mining in Banking

Design and Development of an optimal algorithm to assign applicants to suitable teaching positions

What Airbnb Host Listings Influence Peer-to-Peer Tourist Accommodation Price?

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