AI will become the most defining technology of the new banking and financial services of the future.


Undoubtedly, Artificial Intelligence (AI) is the most revolutionary concept that can take the field of computing to the next level. The integration and application of AI in a variety of procedures will extend human senses and capabilities and automate undesirable tasks. For financial institutions, represents an opportunity to entirely improve efficiency, customer service, risk management and fraud detections.

And as Roberto Ferrari, Managing Director, CheBanca!, puts it: “AI will become the most defining technology of the new banking and financial services of the future.”

Which Technologies should banks be focusing on?

There are a number of factors that bring Artificial Intelligence (AI) into the spotlight. The convergence of advanced analytics, with a robust set of data and extremely low costs of data storage are creating a new era of data processing. The connection of those three elements allows to leverage vast amount of data and apply advanced analytics in order to create truly meaningful intelligence that in return can inform better customer experiences and much better risk controls.

However, the question is not which AI technologies are important, but what can be done with such tools. As Ken Dodelin, Vice President of Digital Product Management at Capital One, states: “Leveraging AI technology is only worthwhile if you can connect the dots between why it will make someone’s life better and how it will actually go about accomplishing that goal.”

In the financial field, the area of focus of Artificial Intelligence (AI) is detecting in the machine and deep learning, the “conversational AI” and at the simpler end of AI spectrum, robotic automation techniques.

As, Julia Krauwer, Artificial Intelligence Expert at ABN AMRO, shares: “Deep learning is a technology that is loosely inspired by the way the human brain works, and is very well suited for learning from big volumes of unstructured data, such as images, voice and even text. Its ability to detect non-linear relationships could help banks move from using linear, expert-based models with set variables to models that expose relationships humans could not even have thought of”.

The other way that AI can be implemented is the conversational AI. “Conversational AI involves utilizing natural language processing (NLP) and natural language understanding (NLU) as a means for banks to communicate with individuals concerning their banking needs”, as Abe CEO Rob Guilfoyle explains.

What are the key applications of AI in financial services?

Nobody can answer precisely about the range that AI can capture in the industry. Is an entirely extensive area that will redefine the whole financial industry in the long-term, just after machine and deep learning are mature and applicable enough.

However, observers incline towards a wider application area of AI. Harnessing data and analytics could be used as a credible guide to enhance customer interactions and create experiences that add value and make it easier to succeed. Also, there is a potential to essentially improve customer experience.

As lan McIntyre, Senior Managing Director, Global Banking, Accenture stresses, “That is the goal for financial services: personalisation, and AI is one of the catalysts for banks to move away from thinking about advice as something that happens occasionally to thinking of it as something that happens in real-time, in the moment, in context, with a strong degree of personalisation. This is all part of banks moving towards making our lives better”.

Furthermore, the usage of AI in areas such as fraud management can not only improve speed and decrease costs, but at times, these technological advantages can make even better decisions. The usage of machine learning techniques to the area of fraud prevention sees a great potential and in the future may extend to other risk areas such as customer onboarding. In other words, AI has great potential to modernize and simplify customer onboarding, because the usage of machine learning will help accurately identify documents which have been uploaded using commodity technology (scanners, low-res smartphones e.t.c.).

The challenges: Technology

The most obvious technology challenge is that technological solutions do not work very well till now and they still have some way to go.

Language seems to be the most important barrier to commercial AI, because it isn’t identified yet how to get a computer to completely understand human beings. Data is another blogger and as ABN Amro’s Krauwer says, “AI is not a plug-and-play solution”. For most bank-specific purposes, you will need large quantities of data and a great amount of effort to train the models that lead to intelligence”. The collection, process and analysis of data is a very demanding task because the majority of them are fragmented. In order to take advantage of these data a crucial success factor is data quality and knowing how to use them.

In addition, legacy challenges are at the center of collection data procedures. It is very important to have good control and governance because in order to succeed a true end-to-end experience involves to interface with old legacy systems.

As vendor community plays an important part in expending the financial industry’s successful integration of AI, banks face the challenge of effective partnering with the supplier community on order to speed their progress. Basically, the real challenge of this cooperation is that vendor community needs to identify and respond to the real need of the financial services industry around AI and present a more coherent picture of what is an offer, and how it fits together, to really help banks progress.

The challenges: People & Culture

It is clear that banks should adopt the appropriate culture to integrate and use successful AI solutions. What they should consider is that digital comes first and this rethinking should pass at their designing processes and organizational structure.

It is important for banks to create an innovation culture, where digital teams can explore and test AI technologies and by extend bring value to customers and themselves. Business models should be aligned with these changes and finding or training the right people to do that, definitely would be a challenge for the banking sector.

Although that digital integration will change the in-store environment and procedures, financial institutions should keep a balance on their interaction with customers and AI-based services. Also, the fact that the new AI-led services will held huge amount of information that they gather through transactions, banks should be careful not to make use them to make inappropriate offers.

The risks of not engaging

Financial institutions that do keep up with the times and integrate AI technologies could eventually loose contact with their clients and loose the opportunity to offer excellence in customer service.

As, Julia Krauwer, Artificial Intelligence Expert at ABN AMRO points: “A risk banks face if they don’t engage is missing out on an opportunity to service clients in a more personalised, frictionless and maybe even more proactive way”.

Furthermore, the risk of staying behind the competition is highest and the institutions that lack from vision and courage will face the risk of losing new business opportunities.