The letter and/or sound combinations that make up a human language are limited by the human's ability to pronounce tnese sounds° Therefore, the standard library search, which as a rule looks for all possible combinations of letters to find a word, is wasteful. Certain letters simply cannot be followed by certain other letters and a search for them is senseless. Following this same line of reasoning, letters very frequently occur in the combinations that are germane to the particular language. The growing amount of alphanumeric information presently being stored on magnetic tape presents increasingly difficult problems in both the number of tape reels used and the time necessary to search this mass of information in order to extract pertinent literature. At the present time most of this literature on tape utilizes the standard IBM 6-bit code to express alphanumeric symbols. ~t is entirely feasible to record standard English literature on tape -be it professional abstracts or novels -using only approximately two-thirds of the binary bits utilized to represent the same piece of written material in the conventional code. This can be accomplished by setting up, in a 9-bit code, the 400-odd letter combinations occurring most frequently. A 9-bit representation allows the programmer to set up as many as 512 symbols, thus leaving sufficient leeway to assign symbols to the most frequentlyused words, mathematical symbols, professional expressions, that are expected to be encountered in the literature to be recorded. In addition, these relatively short 9-bit symbols can be assigned to all key words that it may be necessary to look for later, thereby accelerating any future library search.

Information storage and retrieval

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.

Digitalisation and Big Data Mining in Banking

End-to-end neural network architecture for fraud scoring in card payments

This paper aims to explore deep learning model to learn short-term and long-term patterns from imbalanced input dataset. Data for this study are imbalanced card transactions from an Indonesia bank in period 2016–2017 with binary labels (nonfraud or fraud). From 50 features of the dataset, 30 principal components of data contribute to 87 % of the cumulative Eigenvalues. This study explores the effect of nonfraud to fraud sample ratio from 1 to 4 and three models: Convolutional Neural Network (CNN), Stacked Long Short-term Memory (SLSTM), and Hybrid of CNN-LSTM. Using Area Under the ROC Curve (AUC) as model performance, CNN achieved the highest AUC for R=1,2,3,4 followed by SLSTM and CNN-LSTM.

Learning temporal representation of transaction amount for fraudulent transaction recognition using CNN, Stacked LSTM, and CNN-LSTM

Credit risk has been a widespread and deep penetrating problem for centuries, but not until various credit derivatives and products were developed and novel technologies began radically changing the human society, have fraud detection, credit scoring and other risk management systems become so important not only to some specific firms, but to industries and governments worldwide. Frauds and unpredictable defaults cost billions of dollars each year, thus, forcing financial institutions to continuously improve their systems for loss reduction. In the past twenty years, amounts of studies have proposed the use of data mining techniques to detect frauds, score credits and manage risks, but issues such as data selection, algorithm design, and hyperparameter optimization affect the perceived ability of the proposed solutions and it is difficult for auditors and researchers to explore and figure out the highest level of general development in this area. In this survey we focus on a state of the art survey of recently developed data mining techniques for fraud detection and credit scoring. Several outstanding experiments are recorded and highlighted, and the corresponding techniques, which are mostly based on supervised learning algorithms, unsupervised learning algorithms, semisupervised algorithms, ensemble learning, transfer learning, or some hybrid ideas are explained and analysed. The goal of this paper is to provide a dense review of up-to-date techniques for fraud detection and credit scoring, a general analysis on the results achieved and upcoming challenges for further researches.

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A state of the art survey of data mining-based fraud detection and credit scoring

Online shopping and banking has increased by the growth of internet and by use of credit card. Along with this number of credit card fraud is also increased. Many modern techniques based on Artificial Intelligence, Data warehousing has evolved in detecting various credit card fraudulent transactions. We proposed a system which detect fraud in credit card transaction processing using a decision tree with combination of Luhn's algorithm and Hunt's algorithm. Luhn’s algorithm is used to validate the card number. Address matching rule checks whether the Billing Address and Shipping Address match or not. This check does not guarantee whether a transaction is fraud or genuine. But if the two addresses match, the transaction can be classified as genuine with a high probability. Else, the transaction is labelled as suspect. A customer usually carries out similar types of transactions in terms of amount, which can be visualized as part of a cluster. Since a fraudster is likely to differ from the customer’s account, his transactions can be detected as exceptions to the cluster – a process known as outlier detection. General Terms Credit card fraud, online Transaction, Electronic Commerce

/pdf/a-novel-idea-for-credit-card-fraud-detection-using-decision-2s6lsvyk5e.pdf

A Novel Idea for Credit Card Fraud Detection using Decision Tree

An approach towards Information retrieval process security issues & accuracy of the retrieved data from the user perspective, are the major concerns of this paper. Implanted work includes demonstration of the retrieval modes, process flow of information retrieval system and risk analysis of the retrieved data. Much work has been done to highlight the accuracy problem in the retrieved content where an accuracy formula focuses on the calculation of the accuracy percentage in order to generate an transparent approach towards the information retrieval process.

/pdf/security-issues-and-accuracy-concerns-in-information-2fn6zubryt.pdf

Ketan N. Jain

Papers

A Novel Idea for Credit Card Fraud Detection using Decision Tree

Security Issues and Accuracy Concerns in Information Retrieval Process