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The outbreak of Covid-19 represented a revolution for Credit institutions and Banks all over the world, hitting a Banking Market which is already coming down from years of limited development by the traditional players, and by a tough competition carried from other players that are 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 into force of the EBA LOM guidelines finally took place, concluding a process started last May 2020.
The European Banking Authority (EBA) developed a package of specific Guidelines on loan origination and monitoring (LOM) after the Council of the European Union’s Action Plan 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 so-called “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 a backdrop of additional hardships in the economic environment during the last years.
The regulators’ expectations focus on the usage of new approaches for both credit origination and monitoring phases, through the identification of key indicators.
The regulators suggest the adoption of a new data paradigms and methodologies which can help to optimize credit processes. Looking at Italy, these new approaches are a focal point to be followed in the SMEs 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 primarily characterizes Reply’s committment across various Financial Institutions in many European Countries, thanks to a deep knowledge in areas such as regulatory compliance, models, processes and data governance.