Artificial Intelligence and related technologies are being rapidly adopted, bringing change to multiple industries, and fintech is no exception. Novel approaches brought up by the new wave of technology encompass multiple aspects of the industry. An analytics and AI expert, Pavel Baltabaev, currently working as a data science specialist at a leading management consulting company, is a person who understands the significance of these changes. Moreover, he brings them to life, implementing practical banking solutions based on AI and machine learning.

The McKinsey Global Institute (MGI) estimates that across the global banking sector, generative AI could add between $200 billion and $340 billion in value annually, or 2.8 to 4.7 percent of the total industry revenue. For the outside observer, “generative AI” or “Machine Learning” may sound more like mere buzzwords, but someone who has direct experience in applying the technology will see without doubt what new opportunities it brings.

Pavel Baltabaev has taken part in several projects implementing AI-based solutions in banking, and he sees several directions in which emerging technologies are affecting the banking industry, reimagining both the operation of the financial institutions themselves and their interactions with their clients and partners.  

New Level of Personalization

One of the most noticeable trends affecting the interactions between banks and their clients is the move towards a new level of personalization.

“To surpass competitors, it is not enough anymore to manually segment the audience and create a targeted offer for each segment,” comments Pavel Baltabaev. “Customers want to see offers that are tailored to their exact needs, and AI-powered personalization gives an opportunity to achieve just that.”

By analyzing users’ behavior, it becomes possible to create personalized offers for saving, investing, and budgeting, taking into account users’ goals and individual preferences. 

It would be a mistake to think that such solutions are working for the future; they can already be seen being used in practice. In 2022, Pavel Baltabaev participated in the development of an AI-powered recommendation system of this type. By implementing the Next-Product-to-Buy model, the bank was expected to gain a significant increase in fee income in a year’s prospective from its commercial clients. 

A similar type of AI model was developed by Pavel within another bank for consumer clients and targeted the deposit cross-sell. The piloting showed an impressive 600% growth in a cross-sell conversion compared to the manual segmentation approach used before. 

These are just two examples of the much broader growing trend for personalization, demanding other participants of the market to embrace new approaches and find the most effective ways to implement new tools. However, while personalization may be one of the trends most noticeable customers, the changes will affect the inner workings of the industry as well. 

Higher productivity

While it is hard to expect that AI will fully take over some human jobs in the near future, it is fully capable of becoming an effective assistant both for bank employees and customers. A powerful example is a ChatGPT-based RAG assistant, another product developed by Pavel Baltabaev. This AI assistant, empowered with access to the organization’s knowledge base and the records of past operations, allowed to gain up to a 20% increase in some bank employees’ productivity and reach a 75% level of user satisfaction. 

The role the AI-based tools play in banks’ operation will only increase in the future, and to stay competitive, companies will need to implement them in their processes. While such tools are not able to replace human employees, they allow them to perform more effectively, relying on the objective data and vast knowledge base in their decision making. 

Advanced Risk Management 

As it was already mentioned above, the applications of AI-based tools are not limited to improving bank-customer interactions. “There are other powerful trends banks need to consider,” continues Pavel Baltabaev, “and one of them is risk assessment and fraud prevention.” Modern achievements in machine learning make it possible to analyze vast amounts of data and recognize the trends that may remain unseen without these novel tools.

AI-powered credit risk systems may be able to recognize suspicious activities to protect banks and customers from fraud attempts. Moreover, they are already being used to evaluate credit scores and streamline the loan approval process in routine cases, which allows automation.

The Emerging Challenges

However, to implement such solutions, both in customer-faced systems and in the inner processes of financial institutions, one needs to find a hard balance to make them work effectively without compromising data security or violating existing regulations. To achieve this in a rapidly changing field, a professional will need a very specific skill set, combining industry knowledge with a deep understanding of computer technology.

“My education gave me a solid base of fundamental knowledge in mathematics, machine learning in computer science,” comments Pavel about his degree in the Higher School of Economics in Moscow. Later, he found his passion in applying mathematical tools to solve real-world problems and implement digital transformation and data science solutions for businesses in the finance industry.

The demand for professionals who are able to apply their computer science or data science skills to real-world problems while accounting for the specifics of a particular industry will only grow. The education system will need to adapt as well, complementing formal degrees with company-specific or internal education tracks, which help employees familiarize themselves with new tools.

It is worth noting that AI technology is still in its early stages, especially in heavily regulated segments, such as financial services. However, even with the current level of its development, it can bring changes that are impossible not to notice.

“The emerging trends present a clear picture of what is to come,” adds Pavel Baltabaev. “They represent the growing proliferation of novel AI tools and approaches that will eventually encompass all segments of the banking industry, and all businesses will have to embrace them to remain competitively viable.”

Published and up to date as of 6/27/24.