Data Mining Approach for Direct Marketing of Banking Products with Profit/Cost Analysis

Mitik, Merve; Korkmaz, Ozan; Karagöz, Pınar; Toroslu, İsmail Hakkı; Yucel, Ferhat

doi:10.1007/s12626-017-0002-5

Cited by 20 publications

(14 citation statements)

References 9 publications

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“…Apart from the continuous exploration of the above Portuguese direct marketing dataset, Shih et al [65] presented a target marketing model for commercial banks for the personal loan service, and the experiment was conducted with the data from a bank in Taiwan. With the direct marketing data set of a Turkish bank, Mitik et al [66,67] proposed a two step hybrid system and achieved promising accuracy and a huge increase in the overall profit/cost ratio. Another regionally focused research by Wang and Petrounias [68] analyzed the relationships between demographic characteristics and mobile banking in China with big data collected through questionnaires.…”

Section: Customer Development and Customizationmentioning

confidence: 99%

Digitalisation and Big Data Mining in Banking

Hassani

Huang

Silva

2018

BDCC

116

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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

116

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“…Modifying an ANN to make it cost-sensitive is a promising approach to enhance its performance and mitigate the effects of imbalanced data distribution. Cost-sensitive methods preserve the quality of original datasets, in contrast to other methods (e.g., pre-processing the original dataset by re-sampling techniques to adjust the skewed distribution of classes) which may degrade the quality of the data [29]. In practice, bank telemarketing data are highly imbalanced (e.g., 11% of the contacted clients in a marketing campaign may be interested to accept an offer).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, contacting uninterested clients is an incurred cost with marginal returns. It is a matter of fact that wrong prejudgments on client intentions (e.g., willing or not willing to accept an offer) have unequal consequent costs [29,30]. A marketing manager would expect a relatively higher cost of not contacting a potential client who is willing to invest than contacting an uninterested client.…”

Section: Introductionmentioning

confidence: 99%

Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

et al. 2020

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The banking industry has been seeking novel ways to leverage database marketing efficiency. However, the nature of bank marketing data hindered the researchers in the process of finding a reliable analytical scheme. Various studies have attempted to improve the performance of Artificial Neural Networks in predicting clients’ intentions but did not resolve the issue of imbalanced data. This research aims at improving the performance of predicting the willingness of bank clients to apply for a term deposit in highly imbalanced datasets. It proposes enhanced Artificial Neural Network models (i.e., cost-sensitive) to mitigate the dramatic effects of highly imbalanced data, without distorting the original data samples. The generated models are evaluated, validated, and consequently compared to different machine-learning models. A real-world telemarketing dataset from a Portuguese bank is used in all the experiments. The best prediction model achieved 79% of geometric mean, and misclassification errors were minimized to 0.192, 0.229 of Type I & Type II Errors, respectively. In summary, an interesting Meta-Cost method improved the performance of the prediction model without imposing significant processing overhead or altering original data samples.

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“…Recall is the percentage of the restaurant that the predicted restaurant among the restaurants which is user actually visited, as shown in equation (7). F-measure is calculated as equation (8) with Precision and Recall.…”

Section: Methodsmentioning

confidence: 99%

“…Banks or financial institutions can propose appropriate financial products by analyzing the transaction history or account data of customers. Stock market can be predicted by combining social data such as SNS or news [8,9]. Stores can identify purchase patterns by analyzing the purchase history of their customers.…”

Section: Introductionmentioning

confidence: 99%

Rating and Comments Mining Using TF-IDF and SO-PMI for Improved Priority Ratings

Kim¹,

Moon²

2019

KSII TIIS

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Data mining technology is frequently used in identifying the intention of users over a variety of information contexts. Since relevant terms are mainly hidden in text data, it is necessary to extract them. Quantification is required in order to interpret user preference in association with other structured data. This paper proposes rating and comments mining to identify user priority and obtain improved ratings. Structured data (location and rating) and unstructured data (comments) are collected and priority is derived by analyzing statistics and employing TF-IDF. In addition, the improved ratings are generated by applying priority categories based on materialized ratings through Sentiment-Oriented Point-wise Mutual Information (SO-PMI)-based emotion analysis. In this paper, an experiment was carried out by collecting ratings and comments on "place" and by applying them. We confirmed that the proposed mining method is 1.2 times better than the conventional methods that do not reflect priorities and that the performance is improved to almost 2 times when the number to be predicted is small.

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Data Mining Approach for Direct Marketing of Banking Products with Profit/Cost Analysis

Cited by 20 publications

References 9 publications

Digitalisation and Big Data Mining in Banking

Digitalisation and Big Data Mining in Banking

Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

Rating and Comments Mining Using TF-IDF and SO-PMI for Improved Priority Ratings

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