An Integrated Approach of BOCR Modeling Framework for Decision Tool Evaluation

Kovur, Krishna Mohan; Gedela, Ravi Kumar

doi:10.1007/978-3-030-36518-9_5

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…Quantitative Risk Assessment (QRA) [23][24][25][26][27][28][29][30][31][32][33][34][35] is a methodology for evaluating the risks associated with complex real-time business processes and applications through a systematic Int J Auto AI Mach Learn, Vol 3, Issue 1, December 2023 and comprehensive approach. The purpose of this section is to provide a detailed analysis of the modeling requirements for QRA, with a focus on critical factors that must be taken into account in order to ensure accurate and reliable results.…”

Section: Problem Formulation and Objectivementioning

confidence: 99%

Financial Risk Assessment using Machine Learning Engineering (FRAME): Scenario based Quantitative Analysis under Uncertainty

Kovur,

Gedela,

Rao

2023

Int J Auto AI Mach learn

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Risk management functions, under uncertainty, in the Banking Industry have been changing and will continue to change with the recent advancements and innovations. Embracing uncertainty and working with measurable risk becomes critical, therefore quantitative risk severity assessment is critical for sustainable financial excellence. In this paper, the authors propose Financial Risk Assessment using Machine Learning Engineering (FRAME) based on artificial intelligence (AI) and machine learning (ML), which has two significant contributions. Firstly, adoption of machine learning models for banking towards risk quantification and secondly, granularity that emphases on customized logic via multi-factor analysis modeling at different levels of abstraction connecting machine learning models. These contributions will help Financial Institutions (Fis) that will gain the most benefits and opportunities. In a nutshell, the framework analysis presented in this paper is intended as a step towards building a framework of risk modeling from qualitative to quantitative, viewed at different levels of abstraction to access risk severity in the banking applications.

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