Regulators need to ensure the transparency of rules and criteria used to judge the admissibility of decision algorithms employed by financial institutions, to avoid possible negative impact on the industry such as discrimination among market players. Thus, it is important that regulators and policy-makers have conceptual tools and research at their disposal to make quick and motivated decisions on how to regulate the use of data science techniques.
During this Action, working group 2 (WG2) will develop prototypes to demonstrate the application of quantitative methods to improve transparency for so-called “black box” models. The WG will also publish policy papers to suggest new regulation and guidelines for industry. Our objective is to lower, to the extent possible, the barriers to use more advanced methods.
In addition, our work will also address the issues of limited data and small-sample problems that arise in situations when the events of interest occur infrequently (e.g., defaults, fraud, etc.), providing solutions that will augment existing methods used in the financial industry. Furthermore, the WG will employ methods drawn from econometrics and statistics to transparently quantify and, to the extent possible, alleviate the impact of this problem on inference and prediction for financial decision making.