21,900 research outputs found

    An Overview of the Use of Neural Networks for Data Mining Tasks

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    In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

    Artificial Intelligence in Banking Industry: A Review on Fraud Detection, Credit Management, and Document Processing

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    AI is likely to alter the banking industry during the next several years. It is progressively being utilized by banks for analyzing and executing credit applications and examining vast volumes of data. This helps to avoid fraud and enables resource-heavy, repetitive procedures and client operations to be automated without any sacrifice in quality. This study reviews how the three most promising AI applications can make the banking sector robust and efficient. Specifically, we review AI fraud detection and prevention, AI credit management, and intelligent document processing. Since the majority of transactions have become digital, there is a great need for enhanced fraud detection algorithms and fraud prevention systems in banking. We argued that the conventional strategy for identifying bank fraud may be inadequate to combat complex fraudulent activity. Instead, artificial intelligence algorithms might be very useful.  Credit management is time-consuming and expensive in terms of resources. Furthermore, because of the number of phases involved, these processes need a significant amount of work involving many laborious tasks. Banks can assess new clients for credit services, calculate loan amounts and pricing, and decrease the risk of fraud by using strong AA/ML models to assess these large and varied data sets in real-time. Documents perform critical functions in the financial system and have a substantial influence on day-to-day operations. Currently, a large percentage of this data is preserved in email messages, online forms, PDFs, scanned images, and other digital formats. Using such a massive dataset is a difficult undertaking for any bank. We discuss how the artificial intelligence techniques that automatically pull critical data from all documents received by the bank, regardless of format, and feed it to the bank's existing portals/systems while maintaining consistency

    An academic review: applications of data mining techniques in finance industry

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    With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

    Advanced analytical methods for fraud detection: a systematic literature review

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    The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic

    Engineering a Ruled-Based Software Solution for Credit Rating and Worthiness Assessment in Financial Operations

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    In loan provision, the central worry is whether the borrower will default or payback. A good number of institutions world-wide have gone into distress owing to bad debt arising from inability to recover borrowed funds. Credit Rating is a technique that is widely used to evaluate applications tendered for credit, identify prospective borrowers, and manage existing credit accounts. This work is aimed at the development of a system capable of evaluating the credit worthiness of fund-seeking bank customers and other borrowers towards repayment capabilities of loan facility availed to them in due time. The method carefully examines who qualifies for a loan based on certain rules consisting of Payment History, Credit Owed, Credit Available, Age of Account, Crime Records, Medical Records, Amount to be borrowed. and other factors. Percentage weights for assessment of each of these factors were proposed including threshold percentage above which credit is predicted adequate to be given. This factor creates a sort of satisfaction and level-playing field for correct assessment of lending risk

    A Review of Algorithms for Credit Risk Analysis

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    The interest collected by the main borrowers is collected to pay back the principal borrowed from the depositary bank. In financial risk management, credit risk assessment is becoming a significant sector. For the credit risk assessment of client data sets, many credit risk analysis methods are used. The assessment of the credit risk datasets leads to the choice to cancel the customer\u27s loan or to dismiss the customer\u27s request is a challenging task involving a profound assessment of the information set or client information. In this paper, we survey diverse automatic credit risk analysis methods used for credit risk assessment. Data mining approach, as the most often used approach for credit risk analysis was described with the focus to various algorithms, such as neural networks. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</p

    Data Mining Techniques for Fraud Detection

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    The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision tree-based algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance. Naïve Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models. Keywords: Data Mining, Decision Tree, Bayesian Network, ROC Curve, Confusion Matri
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