Artificial intelligence (AI) is one among the rapidly advancing technological innovations that has a potential to contribute to a digital transformation of the financial services industry through cutting costs, elevating human and systemic efficiency, upgrading user experience, promoting loyalty and facilitating higher returns. The Gartner research and business consulting company states that the fundamental concepts of AI, learning and deep learning, will become an essential part of our lives within the time-span of five years. This has fueled the interest and enthusiasm of major and minor organizations and companies to adopt these technologies, in order to keep up with the latest trends.

It is necessary to explain that AI is based on a range of fundamental underlying technologies such as natural language processing, computer vision, machine learning, deep learning, neural networks etc. All of the latter are put in collaboration within a cloud-based environment, where large amounts of data are stored and processed. This combination of interlinked technologies allows for instant AI response and interactions. Machine learning, for instance, processes data that can then be built on using cognitive techniques, resembling the actual ways humans use underlying data in daily interactions. This technology can explore and develop the capabilities of humans and machines, expanding the range of possibilities far beyond the one they have separately from one another.

When it comes to banking, AI has full potential and power to change the nature of the banking industry, as we are currently shifting towards a self-improving system that is based on learning and where all actors are able to communicate with one another in a real-time mode. This will facilitate the improvement of trust-based relationships and give customers an opportunity to enjoy a more meaningful and accurate interaction.

The main goal is to find and effective application for the AI potential and ensure the efficient use of all its existing capabilities. Process automation and data mining that ensure operational efficiency and risk management have been the primary aims.

At the early stages of its development, AI was viewed as a useful extension to data analytics systems. Researchers believed that the machine-learning component of AI was capable of creating a better was for processing of middle – and back-office data, while reducing the possibility for errors through minimizing human intervention. The main goal was to optimize and automate the processes that required vast resources for sufficient accuracy, and it is here that the AI is most likely to replace humans. One of the best examples if JPMorgan where AI was implemented for the optimization of trade execution process, simultaneously rising effectiveness indicators. At the same time, Citi used supervised machine-learning capabilities in the pricing requests framework that its traders are involved in.

Compliance and risk management procedures have also benefited greatly from unsupervised machine-learning. In more simple words, AI has found an application in identifying the needle in the haystack. After data and documents are scanned, the technology takes action to fulfil the task. The activity is largely based on a set of laws and regulations, as well as specific parameters.

For instance, HSBC uses AI in its money laundering, fraud and terrorist-funding detection procedures. The adoption of latest technological innovation in similar processes has acquired the name regtech (regulatory technology). It allows for an increased automation of regulatory procedures, which ensures higher compliance with international standards and rules, and is thus promoted by many regulatory authorities. The latter aim for the machine learning and natural language processing to be used for capturing and global regulatory data on automatic and continuous basis. As a consequence, a data map of international regulatory intelligence information is created and can then be projected into the regulatory processes of financial institutions. Such technology would enable banks to identify the relevant regulatory basis for businesses of different nature across various jurisdictions, giving managers and regulators more certainty and transparency.

In terms of mass data processing and handling automation is truly valuable, but even more excitement arises around the front-line services, where AI is paired with human intervention. Talking of this in simple terms, companies and organizations aim to enhance user experience through virtual assistants, chatbots, robo-advisors, as well as other instruments that heavily depend on the use of AI. This is what most banks and other institutions in the financial industry are focused on. It is common for them to use chat- and voicebots to communicate with customers and resolve basic issues without a need for actual humans to be involved.

Besides, machine learning can find application in risk management. For instance, it could help identify individuals at risk of defaulting on a loan or credit card payment. This will potentially enable the bank to take the necessary measures before the amount of debt is unsustainably high. AI can also generate better insights that may result in improved recommendation, as well as targeted cross-selling of products and services. For example, a robo-advisor can offer the customer a personalized and tailored advice on financial matters, based on the profile of the particular individual. Profiling procedure occurs with the help of machine learning to analyze structured and unstructured data and then direct it through algorithmic sorting in order to make a personalized solution.

However, we should always remember about the limitations that will continue to exist in the field. The role of humans is still the leading one, as they control and direct the application of AI. The main goal behind the technology is to help, not replace people.