Tal Schwartz

According to a forecast made by Statista Research Department in March 2022, the global Artificial Intelligence market is expected to grow up to $126 billion by 2025. Despite the traditional nature of the banking sector, financial institutions (FIs) joined the race as well. 

Is AI a real all-in-one solution for FIs and FinTech? What drawbacks and pitfalls does the implementation of AI-powered tools hide? Read this article to open the “black box” and reveal them.

AI and its potential for growth in the financial industry

Financial institutions were rather sceptical about the adoption of Artificial Intelligence for their services’ improvement in recent years. According to Pymnts, only 5% of them reported that they use AI-powered loan management software for detecting fraud and anti-money laundering (AML) or managing credit risks in 2018. But by 2021, that figure had shown three times growth, reaching 16%.

The forecast made by Statista says that in the US alone, the AI software market is expected to grow from $4 billion in 2018 to a tremendous $50 billion by 2025. What number will it be, if we add Europe and Asia-Pacific region?

There’s no doubt that Artificial Intelligence has the potential to revolutionise the ways how work can be done and how decisions are made. But AI needs to be used and deployed responsibly with a full understanding of all challenges it arises. Artificial Intelligence must be transparent, explainable, and trusted.

AI and its application touchpoints for FIs

Artificial Intelligence and Machine Learning have rocketed to the top of the list of digital trends in a wide range of industries, including finance. Sophisticated AI-powered solutions have become the irreplaceable tool for financial service providers for the identification of suspicious patterns and automatic counter-measures implementation. 

The opportunities that Artificial Intelligence provides for efficiency improvement, revenue growth, and improved risk management and compliance are indisputable. But there are also ethical, regulatory, and security risks that financial institutions should bear in mind, if they choose to use AI.

Risks and challenges AI implementation implies

Though AI-powered solutions look very promising for future financial sector development, they should not be taken as a panacea and fit-for-all purposes tool. As Artificial Intelligence is a relatively new technology, it holds a plethora of challenges and hidden risks in it. 

Financial institutions and banks should not blindly follow the hype the technology experiences now. Let’s try to unveil the challenges that AI can cause.

Though the technology is used to perform functions quicker and more effectively than humans, it is not flawless. There are cases when it may make decisions incorrectly. To mitigate the impact of this error decision from the outset, it’s necessary to have the correct controls in place and have a service provider available for assistance.


It’s vital for businesses to have a clear vision of the sets of data used by a vendor to train, test, and deploy AI. The AI-powered system trained on inaccurate sets of data or with incomplete relevant training is likely to produce a much higher number of errors in the output. Businesses should regularly engage with the AI service vendor to understand what services are provided. FIs need to know the way decisions are made and how processes are logged and can be reviewed.


For successful technology engagement, it’s necessary for experts or teams to work with a technology provider on a daily basis to ensure issue elimination. The task to update the risk management framework for reviewing risks that AI may pose can be given to this team of experts too. Regular reviews of the issues with a vendor can significantly minimise the consequences of such issues if they occur.


The transition of functions and services to an AI-powered loan management system may cause the occurrence of an internal skills gap, resulting in the misunderstanding of what is happening to the data. This may lead to the loss of control over data for FIs. 

Financial organisations can risk losing the tools and resources for monitoring, reviewing, or explaining the decisions taken by AI.


As Artificial Intelligence systems are very complex by their nature, cybercriminals may try to use their complexity as an opportunity to access business data and insert “bad data sets” into the system. 

This method is known as “data poisoning”. It is used to influence the process of decision-making made by AI systems for getting certain benefits from it or causing damage to a company.


There are no clear regulatory requirements for AI leveraging now. Governments are only at the beginning of conducting lawsuits and regulations in the sphere.Financial institutions need to review all the changes in legislation cautiously and stay in touch with an AI provider to prevent becoming non-compliant with regulations.

Data protection

Non-compliance with laws regulating data protection can cause severe fines and reputational damage. As AI-powered systems usually process confidential and personal information, FIs should ensure that data processing is in full compliance with data protection laws.


FIs should have a clear vision of how AI was trained and what analytical tools were used for that to ensure that data is unbiased and accurate. This significantly reduces the possibility that unethical decisions taken by AI could discriminate against particular groups, either directly or indirectly.

Is AI hype or the future?

Artificial Intelligence is a bright opportunity to improve both organisational and regulatory compliance efficiency. 

At the same time, AI leveraging poses significant ethical, transparency, and security risks. They can be effectively managed by proactive boards and management. It is critical for financial institutions to ensure that proactive and efficient management is in place.

All these issues–if carefully considered from the outset–can be eliminated and provide businesses with excellent tools to use AI more effectively. The risks, worries, and hurdles described in the article can be eradicated by the careful choice of an in-depth AI partner.