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Artificial Intelligence and its Role in the Financial Sector

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Artificial intelligence as technological development has been around for several decades. But what has led to the rapid growth of this sector in recent years? The answer was given by engineer and businessman Gordon Moore, co-founder of Intel, who in 1965 formulated the "gospel" law for the IT industry: According to "Moore's law", every 18 months the power of processors – and as a result of computers – doubles, while the cost of manufacturing decreases. That is, a computer chip with a specific power, which cost 20,000 dollars in 1970, can be built today for less than 0.002 dollars. This has the effect of increasing the computing and storage power of computers while reducing their cost, which allows for many rapid technological advancements including artificial intelligence. In fact, according to the Stanford University report on artificial intelligence for 2019, artificial intelligence exceeds “Moore's law”, namely, its power doubles every 3-4 months instead of 18. The same report states that the time required for training an artificial intelligence network for image recognition was reduced from three hours in October 2017 to about 88 seconds in July 2019!

Artificial intelligence is therefore a branch of technology that will evolve rapidly and will radically affect many sectors. Recent technological achievements have made AI readily available as a service that can easily be accessed with the help of an AI development company.

In recent years, artificial intelligence has increasingly been applied in the financial sector, in the context of its digital transformation, for example, the project CheckNFT.iO. The boom in the adoption of artificial intelligence and machine learning technologies in the financial sector is due, on the one hand, to the rapid technological developments and the abundance of big data available to financial institutions and, on the other hand, to the pressure for greater efficiency, which comes from intense competition but also the demanding regulatory framework. At the same time, financial institutions will inevitably have to evolve to meet the needs of their customers, who are increasingly using technology to make their lives easier. The most common applications of artificial intelligence are in customer-centric functions. Especially in the banking sector, artificial intelligence is used in the processes of interaction with customers through chatbots (e.g., natural language recognition), creation of personalized financial products and services based on each customer's profile, risk management (fraud detection, credit analysis) and process optimization through the automation of repetitive tasks.

In the insurance sector, an example use of artificial intelligence is in automated invoicing, promotion, and management of customers' insurance policies, and the creation of personalized insurance products tailored to the individual needs of the customers. Indicative benefits from the application of artificial intelligence include: improving the customer experience, simplifying and automating processes by minimizing human involvement, and improving service costs, on the one hand, due to more efficient use of data and on the other due to increased ability of systems for combined analysis from both conventional data sources (transaction profiles, loan data, etc.) and non-conventional ones (social network data, etc.). In addition to improving customer service, the use of artificial intelligence technologies also involves the optimization of existing processes of organizations operating in fields, such as fraud detection, investment management, risk management, and market analysis. At the same time, the need for effective compliance of central bank-supervised financial institutions with a dynamic regulatory framework is expected to lead them to the use of artificial intelligence technologies for regulatory purposes, i.e., Regtech - Regulatory Technology. As far as supervisory principles are concerned, artificial intelligence as part of Supervisory Technology (SupTech) can be applied to procedures for detecting abnormalities in supervisory data, in-depth report analysis, market monitoring, analysis, delinquent behavior (e.g., money laundering terrorist financing, and fraud) and early warning systems for financial crises in the context of supervision. From the above, it becomes clear that the correct application of artificial intelligence can be beneficial to all parts of the financial system. Customers can enjoy better and more personalized services and access personalized financial products. Businesses can reduce their operating costs while improving the efficiency of their internal processes. Finally, supervisors can improve the effectiveness of supervision. In addition to the great opportunities presented by the use of artificial intelligence in the financial sector, new challenges are emerging:

  1. Opacity regarding the characteristics and behavior of the applied, usually complex, data processing algorithms, may lead to difficulties in understanding and controlling the processes involved and limiting their traceability, both by the organizations that adopt them, as well as and by the authorities that supervise them ("black box" phenomenon). 2. Improper design of artificial intelligence algorithms may introduce bias and discrimination in the results of its implementation. Inadequate evaluation of the results of data processing with artificial intelligence may lead organizations to make wrong decisions and consequently carry risks of reputation and compliance with the regulatory framework. 3. Vulnerabilities in artificial intelligence models or information management infrastructures may lead to emergencies of information security, cybersecurity, and data protection in general. In addition, the risks of organizations becoming dependent on third-party technology providers may increase. 4. The lack of knowledge, familiarity, and experience of the staff of each organization with the artificial intelligence systems may lead to failures or malfunctions in the management of business processes based on artificial intelligence.

At the same time, the management of an organization should not automate its basic responsibilities, while it is also necessary to have absolute transparency in the decision-making process at all levels of the organizational structure. To address the above-mentioned risks, organizations must ensure that any attempt to exploit artificial intelligence is in compliance with ethical rules, in accordance with European Union guidelines, and that the results of the application of the algorithms are explanatory and non- discriminatory. They need to have a framework for understanding, controlling, and governing these technologies, which will ensure that any critical decision made with the help of artificial intelligence is adequately documented and can, if required, be reproduced in the future. This framework should ensure the correct operation and accuracy of the algorithm results, through the continuous supervision of specialized personnel, who will have understood how they work. In addition, financial institutions should implement all those safeguards to enhance data protection.

The use of artificial intelligence is a double-edged sword and needs attention. Using artificial intelligence methods, fake news can be created, specifically deep fake (synthetic) audio or video, where someone appears to say or do things they never said or did, which can then be quickly circulated worldwide by robots with the use of social networks, leading to a misconception of public opinion and public feeling. But with the use of artificial intelligence again, fake news and deep fakes can be detected very quickly and removed from the internet. The formation of public feeling very important. As Abraham Lincoln said in his speech in 1858: "Public sentiment is everything. With public sentiment, nothing can fail; without it nothing can succeed. Consequently, he who molds public sentiment goes deeper than he who enacts statutes or pronounces decisions".

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