Using self-organizing maps for analyzing credit rating and financial ratio data

Shih, Jen-Ying

doi:10.1109/apbitm.2011.5996303

Cited by 5 publications

(2 citation statements)

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“…A growing hierarchical self-organizing map (GHSOM) is an extension of SOM. It is an unsupervised neural network for clustering, which was applied for the financial problems [68,71]. Huang et al [36] extended its property of topology to systematically identify the spatial relationships of dichotomous cases.…”

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confidence: 99%

Intelligent Fraud Detection in Financial Statements Using Machine Learning and Data Mining: A Systematic Literature Review

Ashtiani

Raahemi

2022

IEEE Access

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Fraudulent financial statements (FFS) are the results of manipulating financial elements by overvaluing incomes, assets, sales, and profits while underrating expenses, debts, or losses. To identify such fraudulent statements, traditional methods, including manual auditing and inspections, are costly, imprecise, and time-consuming. Intelligent methods can significantly help auditors in analyzing a large number of financial statements. In this study, we systematically review and synthesize the existing literature on intelligent fraud detection in corporate financial statements. In particular, the focus of this review is on exploring machine learning and data mining methods, as well as the various datasets that are studied for detecting financial fraud. We adopted the Kitchenham methodology as a well-defined protocol to extract, synthesize, and report the results. Accordingly, 47 articles were selected, synthesized, and analyzed. We present the key issues, gaps, and limitations in the area of fraud detection in financial statements and suggest areas for future research. Since supervised algorithms were employed more than unsupervised approaches like clustering, the future research should focus on unsupervised, semi-supervised, as well as bio-inspired and evolutionary heuristic methods for anomaly (fraud) detection. In terms of datasets, it is envisaged that future research making use of textual and audio data. While imposing new challenges, this unstructured data deserves further study as it can show interesting results for intelligent fraud detection.

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Section: ) Technique Typesmentioning

confidence: 99%

Intelligent Fraud Detection in Financial Statements Using Machine Learning and Data Mining: A Systematic Literature Review

Ashtiani

Raahemi

2022

IEEE Access

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“…Heuristics based on the domain information can be applied to cluster data where K-Means algorithm produces a large number of outliers (Shashidhar and Varadarajan, 2011). SelfOrganizing Map is an important neural network based technique employed for clustering and has been applied Science Publications JCS for problems in banking domain (Shih, 2011). Concept formation is a closely related process to clustering and is used to learn summaries from data.…”

Section: Cluster Analysis and Concept Formationmentioning

confidence: 99%

Data Mining in Banking and Its Applications-a Review

Pulakkazhy

Balan

2013

Journal of Computer Science

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Banking systems collect huge amounts of data on day to day basis, be it customer information, transaction details, risk profiles, credit card details, limit and collateral details, compliance and Anti Money Laundering (AML) related information, trade finance data, SWIFT and telex messages. Thousands of decisions are taken in a bank daily. These decisions include credit decisions, default decisions, relationship start up, investment decisions, AML and Illegal financing related. One needs to depend on various reports and drill down tools provided by the banking systems to arrive at these critical decisions. But this is a manual process and is error prone and time consuming due to large volume of transactional and historical data. Interesting patterns and knowledge can be mined from this huge volume of data that in turn can be used for this decision making process. This article explores and reviews various data mining techniques that can be applied in banking areas. It provides an overview of data mining techniques and procedures. It also provides an insight into how these techniques can be used in banking areas to make the decision making process easier and productive.

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