Analysis of banking data using machine learning

Patil, Poonam; Dharwadkar, Nagaraj V.

doi:10.1109/i-smac.2017.8058305

Cited by 23 publications

(14 citation statements)

References 5 publications

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“…It shows that the quality of the prediction significantly increased when using the correlation-based feature selection of bag-of-words [19]. e supervised artificial neural network algorithm is implemented for classification purpose in customer retention and fraud detection [20].…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach to Evaluate the Performance of Rural Bank

Jing

Zhang

2021

Complexity

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In the current performance evaluation works of commercial banks, most of the researches only focus on the relationship between a single characteristic and performance and lack a comprehensive analysis of characteristics. On the other hand, they mainly focus on causal inference and lack systematic quantitative conclusions from the perspective of prediction. This paper is the first to comprehensively investigate the predictability of multidimensional features on commercial bank performance using boosting regression tree. The dimensionality in the financial-related fields is relatively high. There are not only observable price data, financial fundamentals data, etc., but also many unobservable undisclosed data and undisclosed events; more sources of income cannot be explained by existing models. Aiming at the characteristics of commercial bank data, this paper proposes an adaptively reduced step size gradient boosting regression tree algorithm for bank performance evaluation. In this method, a random subsample sampling is performed before training each regression tree. The adaptive reduction step size is used to replace the reduction step size setting of the original algorithm, which overcomes the shortcomings of low accuracy and poor generalization ability of the existing regression decision tree model. Compared to the BIRCH algorithm for classification of existing data, our proposed gradient boosting regression tree algorithm with adaptively reduced step size obtains better classification results. This paper empirically uses data from rural banks in 30 provinces in China to classify the different characteristics of rural banks’ performance in order to better evaluate their performance.

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Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach to Evaluate the Performance of Rural Bank

Jing

Zhang

2021

Complexity

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“…In 2017, Patil and Dharwadkar [6] produce a prediction and classification model for two datasets of bank customer's data. They used the Artificial Neural Network (ANN) in this model then weighted the results.…”

Section: Literature Surveymentioning

confidence: 99%

Improve Profiling Bank Customer’s Behavior Using Machine Learning

Dawood¹,

Elfakhrany²,

Maghraby³

2019

IEEE Access

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In the banking industry, credit card evolution is a noticeable occurrence. Each banking system includes a huge dataset for customer's transactions of their credit cards. Therefore, banks would be in need of customer profiling. Profiling bank customer's cognize the issuer's decisions about whom to give banking facilities and what a credit limit to provide. It also helps the issuers get a better understanding of their potential and current customers. In previous research, Customer profiling mainly depends on transaction data or demographic data, but in this research, we merge both data in order to get a more accurate result and minimize the risk. By finding the best technique, it leads to improvement in accuracy and helps banks to get higher profitability by customer satisfaction through a focus on the valuable customer (companies) which consider as the main engine in the bank's profitability. This study aims at using k-mean, improved k-mean, fuzzy c-means and neural networks. The used dataset is labeled and creating a ýnew label as a target for neural network classification is the main aspect of this study, which helps to reduce the clustering execution time and get the best accuracy results. Finally, by comparing the accuracy ratio it shows that the neural network ýis the best clustering technique. INDEX TERMS Profiling, banking, machine learning, k-mean, fuzzy c-mean, neural network classifier.

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“…Patil et al [19] implemented a supervised artificial neural network with back propagation algorithm for the purpose of classifying transactions for fraud detection. Experimental evaluation was made on an old dataset of applications for credit loans, which seems to be unrelated to the task of fraud detection in bank transactions.…”

Section: Fraud Discovery Approachesmentioning

confidence: 99%

Fraud Detection in Payments Transactions: Overview of Existing Approaches and Usage for Instant Payments

Diadiushkin

Sandkuhl

Maiatin

2019

CSIMQ

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Financial industries are undergoing a digital transformation of their products, services, overall business models. Part of this digitalization in banking aims at automating most of the manual work in payment handling and integrating the workflows of involved service providers. The focus of the work presented in this paper is on fraud discovery and steps to fully automate it. Fraud discovery in financial transactions has become an important priority for banks. Fraud is increasing significantly with the expansion of modern technology and global communication, which results in substantial damages for the banks. Instant payment (IP) transactions cause new challenges for fraud detection due to the requirement of short processing time. The paper investigates the possibility to use artificial intelligence in IP fraud detection. The main contributions of our work are (a) an analysis of problem relevance from business and literature perspective, (b) a proposal for technological support for using AI in fraud detection of instant payment transactions, and (c) a feasibility study of selected fraud detection approaches.

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Analysis of banking data using machine learning

Cited by 23 publications

References 5 publications

A Machine Learning Approach to Evaluate the Performance of Rural Bank

A Machine Learning Approach to Evaluate the Performance of Rural Bank

Improve Profiling Bank Customer’s Behavior Using Machine Learning

Fraud Detection in Payments Transactions: Overview of Existing Approaches and Usage for Instant Payments

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