Browsing by Author "Bonet, Isis"

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    Applying fuzzy scenarios for the measurement of operational risk
    (Elsevier, 2021-08-11) Bonet, Isis; Pena, Alejandro; Lochmuller, Christian; Patiño, Hector Alejandro; Chiclana, Francisco; Gongora, Mario Augusto
    Operational risk measurement assesses the probability to suffer financial losses in an organisation. The assessment of this risk is based primarily on the organisation’s internal data. However, other factors, such as external data and scenarios are also key elements in the assessment process. Scenarios enrich the data of operational risk events by simulating situations that still have not occurred and therefore are not part of the internal databases of an organisation but which might occur in the future or have already happened to other companies. Internal data scenarios often represent extreme risk events that increase the operational Value at Risk (OpVaR) and also the average loss. In general, OpVaR and the loss distribution are an important part of risk measurement and management. In this paper, a fuzzy method is proposed to add risk scenarios as a valuable data source to the data for operational risk measurement. We compare adding fuzzy scenarios with the possibility of adding non fuzzy or crisp scenarios. The results show that by adding fuzzy scenarios the tail of the aggregated loss distribution increases but that the effect on the expected average loss and on the OpVaR is lesser in its extent.
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    Flexible inverse adaptive fuzzy inference model to identify the evolution of Operational Value at Risk for improving operational risk management
    (Elsevier, 2018-02-02) Pena, Alejandro; Bonet, Isis; Lochmuller, Christian; Chiclana, Francisco; Gongora, Mario Augusto
    Operational risk was one of the most important risks in the 2008 global financial crisis. This is due to limitations of the applied models in explaining and estimating this type of risk from highly qualitative information related to failures in the operations of financial organizations. A review of research literature on this area indicates an increase in the development of models for the estimation of the operational value at risk. However, there is a lack of models that use qualitative information for estimating this type of risk. Motivated by this finding, we propose a Flexible Inverse Adaptive Fuzzy Inference Model that integrates both a novel Montecarlo sampling method for the linguistic input variables of frequency and severity that allow the characterization of a risk event, the impact of risk management matrices to estimate the loss distribution and the associated operational value at risk. The methodology follows a loss distribution approach as defined by Basel II. A benefit of the proposed model is that it works with highly qualitative risk data and it also connects the risk measurement (operational value at risk) with risk management, based on risk management matrices. This way, we mitigate limitations related to a lack of available operational risk event data when assessing operational risk. We evaluate the experimental results obtained through the proposed model by using the Index of Agreement indicator. The results provide a flexible loss distribution under different risk profiles or risk management matrices that explain the evolution of operational risk in real time.
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    A fuzzy credibility model to estimate the operational value at risk using internal and external data of risk events
    (Elsevier, 2018-06-15) Pena, Alejandro; Bonet, Isis; Lochmuller, Christian; Patino, Hector Alejandro; Chiclana, Francisco; Gongora, Mario Augusto
    Operational Risk (OpR) refers to the possibility of suffering losses resulting from inadequate or failure of processes and/or technology, inadequate behaviour of people or external events. OpR was one of the main risks that led to the 2008 global financial crisis. Limitations of the analytical models that are applied in estimating this risk surface when qualitative information, frequently associated with OpR events, is used. To determine the magnitude of OpR in financial organisations, qualitative datainnd also historical data from risk events can be used. Current research trends that focus on the development of analytical models, by using different databases, to estimate the Operational Value at Risk (OpVaR) still lack models based on qualitative information, risk management profiles and the ability to integrate different databases of OpR events. In this paper we present a fuzzy model to estimate the OpVaR of an organisation by working with two different databases that contain internal available data and external or observed data. The proposed model considers: (1) the intrinsic properties of the data as fuzzy sets related to the linguistic variables of the observed data (external) and the data from available databases (internal), and (2) a series of management profiles to mitigate the effect that external data usually causes in estimating the OpVaR of an organisation. The results obtained with the proposed model allow an organisation to estimate and determine the behaviour of the OpVaR over time by using different risk profiles. The integration of qualitative information, different risk profiles (ranging from weak to strong risk management), and internal and external databases contributes to the advancement of estimating the OpVaR in risk management.
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    A fuzzy ELECTRE structure methodology to assess big data maturity in healthcare SMEs
    (Springer, 2018-11-30) Pena, Alejandro; Bonet, Isis; Lochmuller, Christian; Tabares, Marta S.; Piedrahita, Carlos C.; Sánchez, Carmen C.; Giraldo, Liliana M.; Gongora, Mario Augusto; Chiclana, Francisco
    Advances in technology and an increase in the amount and complexity of data that is generated in healthcare have led to an indispensable revolution in this sector related to big data. Analytics of information based on multimodal clinical data sources require big data projects. When starting big data projects in the healthcare sector, it is often necessary to assess the maturity of an organization with respect to big data, i.e. its capacity in managing big data. The assessment of the maturity of an organization requires multi criteria decision making as there is no single criterion or dimension that defines the maturity level regarding big data but an entire set of them. Based on the ISO 15504, this article proposes a fuzzy ELECTRE structure methodology to assess the maturity level of small and medium-sized enterprises in the healthcare sector (SMEHs). The obtained experimental results provide evidence that this methodology helps to determine and compare maturity levels in big data management of organizations or the evolution of maturity over time. This is also useful in terms of diagnosing the readiness of an organization before starting to implement big data initiatives or technologies.
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    An Integrated Inverse Adaptive Neural Fuzzy System with Monte-Carlo Sampling Method for Operational Risk Management
    (Elsevier, 2018-01-03) Chiclana, Francisco; Gongora, Mario Augusto; Pena, Alejandro; Bonet, Isis; Lochmuller, Christian
    Operational risk refers to deficiencies in processes, systems, people or external events, which may generate losses for an organization. The Basel Committee on Banking Supervision has defined different possibilities for the measurement of operational risk, although financial institutions are allowed to develop their own models to quantify operational risk. The advanced measurement approach, which is a risk-sensitive method for measuring operational risk, is the financial institutions preferred approach, among the available ones, in the expectation of having to hold less regulatory capital for covering operational risk with this approach than with alternative approaches. The advanced measurement approach includes the loss distribution approach as one way to assess operational risk. The loss distribution approach models loss distributions for business-line-risk combinations, with the regulatory capital being calculated as the 99,9% operational value at risk, a percentile of the distribution for the next year annual loss. One of the most important issues when estimating operational value at risk is related to the structure (type of distribution) and shape (long tail) of the loss distribution. The estimation of the loss distribution, in many cases, does not allow to integrate risk management and the evolution of risk; consequently, the assessment of the effects of risk impact management on loss distribution can take a long time. For this reason, this paper proposes a flexible integrated inverse adaptive fuzzy inference model, which is characterized by a Monte-Carlo behavior, that integrates the estimation of loss distribution and different risk profiles. This new model allows to see how the management of risk of an organization can evolve over time and it effects on the loss distribution used to estimate the operational value at risk. The experimental study results, reported in this paper, show the flexibility of the model in identifying (1) the structure and shape of the fuzzy input sets that represent the frequency and severity of risk; and (2) the risk profile of an organization. Therefore, the proposed model allows organizations or financial entities to assess the evolution of their risk impact management and its effect on loss distribution and operational value at risk in real time.
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    Oil Palm Detection via Deep Transfer Learning
    (IEEE, 2020-07) Bonet, Isis; Caraffini, Fabio; Pena, Alejandro; Puerta, Alejandro; Gongora, Mario Augusto
    This article presents an intelligent system using deep learning algorithms and the transfer learning approach to detect oil palm units in multispectral photographs taken with unmanned aerial vehicles. Two main contributions come from this piece of research. First, a dataset for oil palm units detection is carefully produced and made available online. Although being tailored to the palm detection problem, the latter has general validity and can be used for any classification application. Second, we designed and evaluated a state-of-the-art detection system, which uses a convolutional neural network to extract meaningful features, and a classifier trained with the images from the proposed dataset. Results show outstanding effectiveness with an accuracy peak of 99.5% and a precision of 99.8%. Using different images for validation taken from different altitudes the model reached an accuracy of 97.5% and a precision of 98.3%. Hence, the proposed approach is highly applicable in the field of precision agriculture.
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    Validation of convolutional layers in deep learning models to identify patterns in multispectral images: Identification of palm units
    (IEEE Computer Society, 2019-07-15) Peña, Alejandro; Bonet, Isis; Manzur, Diego; Gongora, Mario Augusto; Caraffini, Fabio
    The convolutional neural networks (CNN) are considered as a particular case of the Deep Learning neural networks, and have been widely used for the extraction of features in images, audio files or text recognition. For the automatic extraction of features from multispectral images, many researchers have appealed to the use of CNN models, which integrate layers with different structures in context with the solution of a problem, which suggests quite a challenge. That is why, in this article, we propose a method to evaluate the stability in the design of convolutional layers for labeling and identification of palm cultivation units from multispectral images. The structure of the proposed convolutional layer will be given in terms of a fuzzy feature map, obtained as a result of the Cartesian product of three vegetation indices commonly used to evaluate plant vigor in this type of crops (NDVI, GNDVI, RVI), represented as compact maps (radial basis functions). The stability in the design will be given in terms of the dominance of the main diagonal that defines the structure of a convolutional layer obtained as a result of the Cartesian product of two compact maps that represent the same multispectral image.