Artificial intelligence

Artificial Intelligence In Banking Industry: Conversion To Genuine Benefits

Today, it seems, hardly a week goes by without mentioning another company which joins the ranks of those, who have already launched their own virtual assistants – from news networks to stores. In the banking sector, traditional call centers are being replaced by AI assistants. However, despite the obviousness of this trend, the introduction of AI technologies may have a comparable financial effect in other fields of application, experts in the field of banking say.

Artificial intelligence may be something fundamentally new for many industries – but not for the banking sector: according to analysts at Accenture, who prepared the Banking Technology Vision 2018 report: Building the Future-Ready Bank, banks could be considered as “veterans” in terms of the history of using these technologies: the first neural networks for automatic lending appeared in this industry more than 20 years ago.

At the same time, the AI is evolving now in an unprecedented way, and the interim results of this transformation are expected to be truly impressive: according to the report, 79% of the banking sector representatives anticipate that in the next two years, technologies will advance to such an extent that artificial intelligence could work in financial organizations equally with living people as an employee and reliable advisor.

Industry experts are already estimating the benefits that large-scale introduction of AI technologies will bring to companies in the sector. For instance, Autonomous Research, a US independent analytical agency that conducts research in the field of financial technologies, offers the following estimate: in the United States alone, artificial intelligence will provide banks and financial institutions with savings of 22% until 2030, or more than $1 trillion.

The components of this impressive figure demonstrate in which areas AI is expected to be the most effective. From the report of Augmented Finance & Machine Intelligence it follows that in the front-office the amount of savings will be about $490 billion – almost half of the aforesaid sum (due to a reduction in the number of specialists in cash operations, security personnel, retail banking network and other staff). Another $350 billion will be saved by using the AI in the middle-office (technologies of customer identification and verification (KYC / AML) as well as other forms of data processing). Finally, AI application in the back-office – regulatory and accounting units – will reduce the expenses of banks by another $200 billion.

In the meantime, one can hardly find a country with banking sector smoothly adopting artificial intelligence without facing any structural challenges. Russia is no exception. In November 2018, the Russian rating agency Expert RA in cooperation with the Center for Financial Technologies conducted a study on “Artificial Intelligence in the Banking Sector” with participation of Russian market leaders in the application of AI and machine learning technologies.

The results of the study showed that the use of AI technologies in the Russian banking industry is hampered by the scattered nature of data and information systems. Moreover, analysts say, even after solving the problem, banks are more likely to experience an acute shortage of specialists capable of processing this data.

Sergei Putyatinsky, Deputy Chairman of the Management Board of Credit Bank of Moscow, shared his opinion on the process of introducing AI and machine learning (the bank ranked “above average” in terms of the use of these technologies according to Expert RA): “We are pragmatic in implementing “hype” technologies. The investigation of technology begins with a limited pilot project, which allows evaluating its utility, creating internal competencies. Where it is possible, we use free software. Each project is calculated in terms of recoupment, and only in this case the decision on its implementation is made. Our top-priority directions in adoption of these technologies are: working with full-text documents, making credit decisions, working with overdue debts, financial monitoring, and while doing so we try not to redo existing solutions, but to search for still manual areas and to automate them by using new technologies.”

The problem of training qualified personnel impels banks to enter the educational solutions market more actively: for example, since spring 2018, CBOM has been implementing the IB Universe internship program, offering students and graduates to gain practical experience in investment business (IB) in a number of areas, including information technology.

The goal of such training programs offered by banks seems obvious: in that case heads of departments and specialists can act as mentors, transferring their knowledge and competencies and, in the long run, “raising” new experts in the workplace, reducing the shortage of staff who, among other things, can be “in tune” with a trend set by AI technologies and machine learning.

Of course, the availability of qualified specialists cannot be viewed as an end in itself, nor the funds released through the introduction of AI can be examined from the position of economy for the sake of economy, no matter how huge the amounts might be. It is important to keep in mind that the active use of AI technologies in the coming years potentially may become a decisive argument in the competitive struggle of banks for mass segments not only in Russia, but throughout the world.

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