• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1051-1054
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• 1993

The International Atomic Energy Agency's Statute in Article III.A.5 allows it“to establish and administer safeguards designed to ensure that special fissionable and other materials, services, equipment, facilities and information made available by the Agency or at its request or under its supervision or control are not used in such a way as to further any military purpose; and to apply safeguards, at the request of the parties, to any bilateral or multilateral arrangement, or at the request of a State, to any of that State's activities in the field of atomic energy”. Safeguards are essentially a technical means of verifying the fulfilment of political obligations undertaken by States and given a legal force in international agreements relating to the peaceful uses of nuclear energy. The main political objectives are: to assure the international community that States are complying with their non-proliferation and other peaceful undertakings; and to deter (a) the diversion of afeguarded nuclear materials to the production of nuclear explosives or for military purposes and (b) the misuse of safeguarded facilities with the aim of producing unsafeguarded nuclear material. It is clear that no international safeguards system can physically prevent diversion. The IAEA safeguards system is basically a verification measure designed to provide assurance in those cases in which diversion has not occurred. Verification is accomplished by two basic means: material accountancy and containment and surveillance measures. Nuclear material accountancy is the fundamental IAEA safeguards mechanism, while containment and surveillance serve as important complementary measures. Material accountancy refers to a collection of measurements and other determinations which enable the State and the Agency to maintain a current picture of the location and movement of nuclear material into and out of material balance areas, i. e. areas where all material entering or leaving is measurab e. A containment measure is one that is designed by taking advantage of structural characteristics, such as containers, tanks or pipes, etc. To establish the physical integrity of an area or item by preventing the undetected movement of nuclear material or equipment. Such measures involve the application of tamper-indicating or surveillance devices. Surveillance refers to both human and instrumental observation aimed at indicating the movement of nuclear material. The verification process consists of three over-lapping elements: (a) Provision by the State of information such as - design information describing nuclear installations; - accounting reports listing nuclear material inventories, receipts and shipments; - documents amplifying and clarifying reports, as applicable; - notification of international transfers of nuclear material. (b) Collection by the IAEA of information through inspection activities such as - verification of design information - examination of records and repo ts - measurement of nuclear material - examination of containment and surveillance measures - follow-up activities in case of unusual findings. (c) Evaluation of the information provided by the State and of that collected by inspectors to determine the completeness, accuracy and validity of the information provided by the State and to resolve any anomalies and discrepancies. To design an effective verification system, one must identify possible ways and means by which nuclear material could be diverted from peaceful uses, including means to conceal such diversions. These theoretical ways and means, which have become known as diversion strategies, are used as one of the basic inputs for the development of safeguards procedures, equipment and instrumentation. For analysis of implementation strategy purposes, it is assumed that non-compliance cannot be excluded a priori and that consequently there is a low but non-zero probability that a diversion could be attempted in all safeguards ituations. An important element of diversion strategies is the identification of various possible diversion paths; the amount, type and location of nuclear material involved, the physical route and conversion of the material that may take place, rate of removal and concealment methods, as appropriate. With regard to the physical route and conversion of nuclear material the following main categories may be considered: - unreported removal of nuclear material from an installation or during transit - unreported introduction of nuclear material into an installation - unreported transfer of nuclear material from one material balance area to another - unreported production of nuclear material, e. g. enrichment of uranium or production of plutonium - undeclared uses of the material within the installation. With respect to the amount of nuclear material that might be diverted in a given time (the diversion rate), the continuum between the following two limiting cases is cons dered: - one significant quantity or more in a short time, often known as abrupt diversion; and - one significant quantity or more per year, for example, by accumulation of smaller amounts each time to add up to a significant quantity over a period of one year, often called protracted diversion. Concealment methods may include: - restriction of access of inspectors - falsification of records, reports and other material balance areas - replacement of nuclear material, e. g. use of dummy objects - falsification of measurements or of their evaluation - interference with IAEA installed equipment.As a result of diversion and its concealment or other actions, anomalies will occur. All reasonable diversion routes, scenarios/strategies and concealment methods have to be taken into account in designing safeguards implementation strategies so as to provide sufficient opportunities for the IAEA to observe such anomalies. The safeguards approach for each facility will make a different use of these procedures, equipment and instrumentation according to the various diversion strategies which could be applicable to that facility and according to the detection and inspection goals which are applied. Postulated pathways sets of scenarios comprise those elements of diversion strategies which might be carried out at a facility or across a State's fuel cycle with declared or undeclared activities. All such factors, however, contain a degree of fuzziness that need a human judgment to make the ultimate conclusion that all material is being used for peaceful purposes. Safeguards has been traditionally based on verification of declared material and facilities using material accountancy as a fundamental measure. The strength of material accountancy is based on the fact that it allows to detect any diversion independent of the diversion route taken. Material accountancy detects a diversion after it actually happened and thus is powerless to physically prevent it and can only deter by the risk of early detection any contemplation by State authorities to carry out a diversion. Recently the IAEA has been faced with new challenges. To deal with these, various measures are being reconsidered to strengthen the safeguards system such as enhanced assessment of the completeness of the State's initial declaration of nuclear material and installations under its jurisdiction enhanced monitoring and analysis of open information and analysis of open information that may indicate inconsistencies with the State's safeguards obligations. Precise information vital for such enhanced assessments and analyses is normally not available or, if available, difficult and expensive collection of information would be necessary. Above all, realistic appraisal of truth needs sound human judgment.