The outbreak of the Covid-19 pandemic represented a revolution for credit institutions and banks all over the world. The banking market is already suffering from years of limited development by the traditional players. Those players are also coming under threat from others using innovative methodologies and technologies.
In this context, banks need to completely re-think their credit processes by enhancing the innovation of approaches which could be able to support credit decision strategies.
June 2021 was an important milestone for the regulatory framework on loan origination and monitoring: the entry of the EBA LOM guidelines took place, concluding a process that started in May 2020. The European Banking Authority (EBA) developed a package of specific guidelines on loan origination and monitoring (LOM). This followed the Council of the European Union’s Action Plan that was developed for dealing with high level of non-performing exposures.
EBA Guidelines have not failed to set out expectations for institutions data infrastructure and lending activities involving technology-enabled innovation. Italy, meanwhile, has approved the “Codice della crisi d’impresa e dell’insolvenza” (DPR (2019), in application of the law 155/2017 - D. Lgs n. 14/2019). These recent regulations, amplified in priority due to the COVID-19 emergency, come into focus with the backdrop of economic hardship in recent years.
The regulators’ expectations focus on the usage of new approaches for both credit origination and monitoring phases, through the identification of key indicators. Regulators suggest the adoption of new data paradigms and methodologies that can help to optimize credit processes. Looking at Italy, these new approaches are a path to be followed in the SME sector, that is a fundamental component of the financial institutions businesses, as the new Bankruptcy Law include new indicators estimated
by the SMEs, and therefore the financial institutions are required to adapt their data to the new regulation.
Machine Learning techniques can be applied in the calculation of risk and business indicators, in identifying those that report the highest levels of predictivity for the origination and monitoring phases. Furthermore, those techniques can be useful to enhance the data quality processes ensuring full effectiveness in the construction of databases to support benchmarking and predictive analyzes.
In this context, Reply supports its customers in all the phases related to the banking processes, from the Functional/Business phase of Regulatory Adoption, to the enhancement of the Credit Processes using Artificial Intelligence & Machine Learning techniques, to the implementation of new technologies for clients.
The ability to cover the end-to-end credit processes characterizes Reply’s commitment across various Financial Institutions in many European countries, thanks to a deep knowledge in areas such as regulatory compliance, models, processes and data governance.