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dc.contributor.advisorVincent, Charleses_ES
dc.contributor.authorParedes Leandro, Rocío Margaretes_ES
dc.date.accessioned2017-03-02T15:49:53Zes_ES
dc.date.available2017-03-02T15:49:53Zes_ES
dc.date.created2016es_ES
dc.date.issued2017-03-02es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12404/7998es_ES
dc.descriptionxv, 153 h. : il. ; 30 cmes_ES
dc.description.abstractThe handling of external operational loss data by individual banks is one of the longstanding problems in risk management theory and practice. The extant literature has not provided a method to identify the best way to combine internal and external operational loss data to calculate operational risk capital. Hence, to improve the knowledge and understanding of internal-external data combination in operational risk management, this study applied a simulation-based evaluation of well-known data combination techniques such as the scaling, the Bayesian, and the covariate-base techniques. This research considered operational losses arising from internal fraud in retail banking within a group of international banks that share data through an operational loss data exchange. One of the key elements of the simulation-based statistical evaluation was the development of a dynamic internal fraud model for operational losses in retail banking. The internal fraud model incorporated human factors such as the number of employees per branch and the ethical quality of workers. It also included the extent of risk controls set by bank managers. There were two sets of findings. First, according to the simulation-based evaluation, the scaling technique was by far the less useful for estimating the appropriate operational risk capital. The Bayesian and the covariate-based techniques performed best. The Bayesian technique was the best for higher percentiles while the covariate-based technique was the best at not so extreme quantiles. The choice of technique therefore depends on the risk appetite of the financial institution. The second set of findings relates to the model validation with hard data. Losses generated by the model in the banks across the world were associated with GDP growth and the corruption perception of the country where banks were located. In general, internal fraud losses are pro-cyclical and the corruption perception in a country positively affects the occurrence of internal fraud losses. When a country is perceived as more corrupt, retail banking in that country will feature more severe internal fraud losses. To the best of knowledge, it is the first time in the operational risk literature that this type of result is reportedes_ES
dc.description.uriTesises_ES
dc.language.isoenges_ES
dc.publisherPontificia Universidad Católica del Perúes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Perúes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/pe/es_ES
dc.sourcePontificia Universidad Católica del Perúes_ES
dc.sourceRepositorio de Tesis - PUCPes_ES
dc.subjectAdministración de riesgoses_ES
dc.subjectInstituciones financierases_ES
dc.titleAn internal fraud model for operational losses : an application to evaluate data integration techniques in operational risk management in financial institutionses_ES
dc.typeinfo:eu-repo/semantics/doctoralThesises_ES
thesis.degree.nameDoctor en Administración Estratégica de Empresases_ES
thesis.degree.levelDoctoradoes_ES
thesis.degree.grantorPontificia Universidad Católica del Perú. CENTRUMes_ES
thesis.degree.disciplineAdministración Estratégica de Empresases_ES


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