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Leonardo Badea (BNR): The activity of financial institutions enters the “digital era”

The European Conference on Financial Services – ECFS 2020, organized by the Financial Studies Institute (ISF) and the Centre for Association of Insurance Arbitration and Mediation Center – ACAMA was held today in Brasov. Present at the conference, Leonardo Badea, the vice-governor of the Romanian National Bank (BNR), talked about the digitalization of the financial sector, consumer experience, fintechs, machine learning, among other talking points.

Financial services digitalization brings with it a number of challenges. First of all, it must lead to the improvement of the consumer experience, this becoming the central objective of all the activities and compartments of the company: prospecting, advising and selling, trading, post-trading and administration. In addition, in order to better understand consumer needs, benefit from business opportunities and reduce costs, it is necessary to fructify the available data as effectively as possible.

Also, in order for the digitization to provide the expected benefits, the operational model must be redefined, which involves identifying and implementing the most appropriate solution for the consumer, between the speed of response to the consumer’s requests, conferred by the digitalization, and the comfort of human interaction, for counseling purposes, in the case of more complex products, or in the event of dealing with certain difficulties or complaints.

In order to be able to adapt to digitalization, the redefinition of the company is needed. This involves transformation at multiple levels: strategy, financing principles, human resources qualifications and competencies, adaptability, organizational culture.

FinTech solutions are already being used to automate financial services, in risk management, and also for reducing costs. By using smartphones, it is possible to transfer money, grant loans, provide services for retail banking or for investment, perform crypto-currency transactions or access financial counseling websites. Robot advisers, a category of automated financial advisors, who offer financial counseling with moderate to minimal human intervention, provide financial advice based on mathematical algorithms and, they can, therefore, offer a cheap alternative to traditional financial counseling.

The results of a study applied on a sample of 27,000 consumers, from 27 countries, active in the digital technologies field, showed that 96% of them declared they had heard of at least one FinTech service for payments or money transfers. Globally, the adoption rate of FinTech services increased by up to 64%.

Concepts such as machine learning and artificial intelligence are concepts that nowadays are increasingly applied in a whole range of fields, including the financial sector. Machine learning algorithms build up a mathematical model based on sample data, with the purpose of issuing predictions or decisions without being explicitly programmed to perform the task.

Following the 2008-2009 crisis, financial institutions have to apply a greater number of regulations and supervisory measures, leading to the need for more detailed and frequent data reporting on a whole range of aspects of their business models and of their balance sheets. Thus, large amounts of reporting data are obtained, which must be well defined and structured, aggregated within groups and delivered on time to supervisors. Therefore, machine learning contributes to the improvement of large database management.

An advantage of the implementation of machine learning is related to its capacity of analyzing a large number of data, at the same time offering an intense granularity and a detailed predictive analysis. Financial institutions have an obvious need for strong analytical tools, in order to be able to manage large amounts of data of different types, from different sources and of different formats, while maintaining and even improving the granularity of the analysis.

Problems arise from the fact that, in financial institutions, complex data of adequate quality are not always available. In addition, the strong capacity of predictability and granularity of the analysis of different approaches can be obtained with the risk of increasing the complexity of the model and of the inability of explaining the mechanism. Thus, the whole process becomes difficult to be audited or analyzed from the compliance point of view. However, there are also adapted approaches to machine learning, which are based on simplified nonlinear analysis.

Machine learning is based on correlations identified in data samples, in order to obtain predictions outside the samples. The disadvantage is related to its limited ability to provide an understanding of the analyzed connections. Thus, when we do not know what it is behind the correlations, we do not know what could be the cause of their possible deficiency.

Some examples of machine learning implementations are financial risk management, especially credit risk management, detection of credit card frauds, supervising the conduct of traders in the trading process and market abuse.

The five largest technology companies, Google, Apple, Microsoft, Amazon and Facebook, which now own about 20% of the S&P500 capitalization, are strengthening their dominance over more and more sectors (including the financial sector) and are increasingly putting their mark on the economy (in terms of number of employees, investments, expenses and purchases of services and semi-finished products), having the advantage of the huge amount of information that they collect, store and analyze.

We are living in a complex world, where daily processes are increasingly based on artificial intelligence.

The consumers of financial products and services must be protected, informed and financially literate.

In this new reality, the conduct of these technological giants is significant, in order for us to be able to talk about a society in which we like to live in, as Robert J. Shiller exposed in the book “Finance and the good society.”

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