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Banks As A Front End Of Digitalisation And Automation

ML and digitalisation processes are able to improve ORM and create new risks at the same time

What can operational risk gain from the implementation of machine learning in the near future?

Operational risk could gain from ML in several ways. The most obvious and straightforward is cost-saving and efficiency increase of the function. ML-models substitute manual operations in collection and handling of risk data. From online identification and classification of simple events in accounting and back office systems to tagging new events from different text data and unstructured video data using NLP models and computer vision. The ML techniques already have a vast implementation in fraud and anti-money laundering detection. Furthermore, they could be used for development of predictive models.

How digitalisation and automation have been changing banking? Do these processes reduce human errors or create more change-management risks?

Financial industry is at the front end of digitalisation and automation. Banking services have been migrating to the digital world at a rapid pace. Banks now are more like IT-companies than old-style credit institutions. And on one hand we see that machines and algorithms replace humans in banking. It allows to eliminate human mistakes in certain areas, to provide online banking services and to reduce cost in mass operations. On the other hand, we see a significant rise of new risks: cyber security, model and technological risks. Still, at the very core of those risks are human errors. Errors, like opening a phishing message, wrong IT-codes or mistakes in model logic can now lead to devastating losses. In this respect digitalisation reduces the number of human errors and at the same time makes them more destructive and difficult for identification.

We are pleased to have Sergey on board of the 16th Annual Banking Operational Risk Management Summit in February 2022, where he will be sharing his insights on Sberbank Experience Concerning ML Models in Operational Risk.


Sergey Alenkin is a Head of Operational Risk at Sberbank – the largest bank in Russia and one of the largest financial institutions in Europe. He has 15+ years of experience in the banking industry, mainly in the field of Finance and Risk management. Married and has three children, he loves to spend holidays with his family discovering new windsurfing spots all over the world.

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