Response to discussion on Approach on financial technology (Fintech)
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There is need to address the informational" risks posed by Fintech activities to their customers, which are amplified by the great interconnectdness of the Fintech ecosystem. Due to their "disintermediated" nature, and in the light of a possibly aggressive marketing activity, Fintechs may assess credit scoring and/or credit risk of a counterparty in a way that does not accurately estimate the underlying probability of default. This bias may be amplified by the systemic risk that arise among Fintech borrowers, highly intereconnected with each other so that, when a counterparty fails, there may be a strong contagion effect on other, neighbouring, counterparties, that may further bias the estimate of the probability of default. To tackle this issue, and allow the devlopment of the innovative services provided by fintechs, we suggest to use "network" based models to estimate default which, using transactional/networked data naturally available for fintechs, improve the estimation of credit risk, both at the idiiosyncretic and at the systemic level. These models belong to the so-called financial network models, employed so far to measure systemic risks and contagion. Our R&D work, documented by research published in relevant finance scientific journal,s shows how it can effectively improve predictive perfomance, on real case studies that concern European Fintechs."
Question 1: Are the issues identified by the EBA and the way forward proposed in section 4.1 relevant and complete? If not, please explain why.
yesQuestion 2: Are the issues identified by the EBA and the way forward proposed in subsection 4.2.1 relevant and complete? If not, please explain why.
yes, however a further risk/opportunity should be included.There is need to address the informational" risks posed by Fintech activities to their customers, which are amplified by the great interconnectdness of the Fintech ecosystem. Due to their "disintermediated" nature, and in the light of a possibly aggressive marketing activity, Fintechs may assess credit scoring and/or credit risk of a counterparty in a way that does not accurately estimate the underlying probability of default. This bias may be amplified by the systemic risk that arise among Fintech borrowers, highly intereconnected with each other so that, when a counterparty fails, there may be a strong contagion effect on other, neighbouring, counterparties, that may further bias the estimate of the probability of default. To tackle this issue, and allow the devlopment of the innovative services provided by fintechs, we suggest to use "network" based models to estimate default which, using transactional/networked data naturally available for fintechs, improve the estimation of credit risk, both at the idiiosyncretic and at the systemic level. These models belong to the so-called financial network models, employed so far to measure systemic risks and contagion. Our R&D work, documented by research published in relevant finance scientific journal,s shows how it can effectively improve predictive perfomance, on real case studies that concern European Fintechs."