Artificial Intelligence Methods for Fair and Transparent Credit Risk Rating Systems

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Within operational research, analytics - the development and use of data-driven artificial intelligence techniques and methodologies to improve organizations - has had a core role supporting banking practice. This proposal moves forward in banking analytics practice by researching new artificial intelligence tools to create a fair, efficient, and transparent methodology for credit risk measurement, by leveraging the new sources of diverse data and generating integrated multimodal learning artificial intelligence models, seeking to improve the cumbersome, inefficient, and bias-prone processes currently in use when data is scarce. While these new sources of data can be promising, extreme care must be taken to not include any information that would unfairly discriminate against some sectors of the population, by including e.g. gender or race information. This information can easily be inadvertently used in machine learning models, leading to the widespread concern of unfair algorithmic bias in machine learning. While some methodologies have been put forward to create fair models, the current state of the art does not cover multimodal complex data sources, and there are no developments in credit risk management at all. Considering the previous challenges, this research will pursue the following objectives: - Objective A: To develop new methodologies to remove potential implicit or explicit biases present in the unstructured data (text, images, etc) when used to develop deep learning credit risk models. This includes the effects of gender, race, religion, and any other identity-related information that can be identified in the data. - Objective B: To construct and evaluate deep learning architectures that can process this unbiased data and can generate a prediction regarding repayment probability of a loan. - Objective C: To generate context-dependent knowledge distillation approaches to evaluate what sections of the structured traditional data and unstructured non-conventional data, covering images, text, and social networks, are being used when estimating this probability. I will provide visualizations of the outputs of the deep learning models, and support the understanding of the impact of each input in the probability of default estimation. For the operational research academic community, the project will deliver new tools to develop models for credit risk, for visualizing multimodal models, and for removing biases when their sources are known. In the regulatory space, I will propose concrete measures that can be taken to ensure fair, accountable, transparent, and ethical (FATE) credit scoring systems using multimodal data sources, thus facilitating the creation of new regulatory measures. Finally, in the banking/Fintech community, the project will provide support for ethical credit scoring systems.