IJERT-Comparison on Banking Dataset-Marketing Targets using Power BI (original) (raw)
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The present global economic crisis makes it difficult for banks to attract customers. Therefore, marketing is seen to be a useful technique for the banking industry to get clients interested in a term deposit. In banks, telemarketing is a commonly used kind of direct marketing. Customers seldom react favorably, therefore data prediction models may assist in identifying the most probable potential clients. Data mining helps direct marketing efforts succeed by foretelling which leads will sign up for term deposits. In this study, we used machine learning to the benchmark dataset of banking institutions' direct marketing campaigns to create an accurate classifier to forecast which consumer would accept a long-term deposit offer. Our research reveals the remarkable influence that machine learning methods may have on the outcome of a telemarketing campaign. Data preparation and model assessment are the two main phases. In the first phase, data must be cleaned by removing duplicate records and determining if missing values should be kept or removed, data visualization, and utilizing the response coding approach to encode category characteristics using label and one-hot encoding. The dataset is originally split into training and testing but the dataset is unbalanced so we needed to consider that while training so we used the balanced class weight approach and 10-fold cross-validation to solve the imbalanced class problem. The Random Forest algorithm is used for training and testing and a perfect classifier is achieved. The proposed system outperformed all the state-of-the-art techniques and achieved perfect classification.
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Hybrid Datamining Approaches to Predict Success of Bank Telemarketing
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Telemarketing is a kind of straightforward marketing in which salesman requests the consumer either face to face or telephone request and influence him to purchase the product. Telemarketing achieves most prevalence in the 20th century and still increasing it. Now, the phone has been broadly accepted. It is valued efficient and holds the consumers up to date. In the Banking area, marketing is the backbone to exchange its goods or service. Business promotion and marketing is frequently based on an exhaustive understanding of actual information about the market and the real client demands for the productive bank manner. We recommend a data mining (DM) method to foretell the achievement of telemarketing requests for contracting long-term bank deposits. A local Portuguese bank was labeled, with data gathered from 2011 to 2016, thus involving the effects of the current economic crisis. We examined a comprehensive set of 11 features associated with bank consumer, goods and social-economic characteristics. We also discuss four DM forms with the hybrid model: Naïve Bayes (NB), Decision Trees (DTs), Perceptron Neural Network (NN) and Support Vector Machine (SVM). The four types were tested and compared with proposed hybrid classification methods (Perceptron Neural Network + Decision Tree) on an evaluation set, and we are splitting data into training and testing sets using cross-validation method. The proposed hybrid classification technique presented the best results (Precision 99% and ROC = 97%).
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Payment data is one of the most valuable assets that retail banks can leverage as the major competitive advantage with respect to new entrants such as Fintech companies or giant internet companies. In marketing, the value behind data relates to the power of encoding customer preferences: the better you know your customer, the better your marketing strategy. In this paper, we present a B2B2C lead generation application based on payment transaction data within the online banking system. In this approach, the bank is an intermediary between its private customers and merchants. The bank uses its competence in Machine Learning driven marketing to build a lead generation application that helps merchants run data driven campaigns through the banking channels to reach retail customers. The bank’s retail customers trade the utility hidden in its payment transaction data for special offers and discounts offered by merchants. During the entire process banks protects the privacy of the retail c...
Banking and Financial Analytics -An Emerging Big Opportunity Based on Online Big Data
International Journal of Case Studies in Business, IT, and Education (IJCSBE), 2020
Business analytics refers to the skills, technology, methods of continuous iterative discovery,and study of past business results. In the banking industry, business analytics can be utilized tothe extent that basic banking reporting can be improved with the help of descriptive analytics,predictive analytics, and prescriptive analytics utilizing significant technical developments andthe use of big data currently available. The application of business analytics to banking andfinance, for both organizations and professionals, is crucial, profitable, and extremelyrewarding. Using advanced machine learning technology, combined with analytics, supportsbanks to research a great deal on customer behavior and preferences, allowing banks tocontinuously learn and fine tune analytical models to optimize products and services and minimize the cost of offering products across different channels. Cloud-based analytics platforms provide flexibility and elasticity for banks to work at high speed with large dataworkloads and to gain business value more quickly. In this paper, the major business analyticscomponents - descriptive analytics, predictive analytics, and prescriptive analytics areaddressed and their applications in various functions of banks for optimum decision-making aswell as for activities such as fraud detection, application screening, custom acquisition andretention, awareness of customer purchasing habits, effective cross selling of different bankingproducts and services, payment collection mechanism, better cash/liquidity planning,marketing optimization, consumer lifetime value, management of customer reviews, etc areanalyzed. The effects of these analytics on the banking and financial industry sector'scompetitive and innovative capabilities are also discussed.
Applied Sciences
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 misclassif...