Julia Gutierrez

Julia Gutierrez
Director of Education, Compliance Alliance

AI – The benefits and challenges for financial institutions

Artificial intelligence is the future and it’s filled with risks and rewards.”

The technologies of artificial intelligence (AI) are becoming an integral part of the world in which we live. These technologies are being deployed across a plethora of fields, ranging from simple devices, such as cell phones to more complex technologies such as autonomous vehicles or the diagnosis of diseases. 

AI is even rearing its technological head into the field of banking. It is a constantly evolving technology that many industries are jumping into while others are slowly being pushed into it in their efforts to thrive. For banks, it’s critical to embrace the advancements of the future, but also to consider the security and regulatory requirements and the overall risk to the organization and its customers. 

What is AI?

AI is a term that commonly refers to the various technological capabilities that allow for the analysis of data and the identification of patterns to make decisions and impact outcomes. Some examples of these AI-type activities or branches include machine learning, natural language processing, robotics process automation and speech and object recognition. 

Machine learning is a branch of AI and computer science that focuses on the use of algorithms and data to imitate human learning patterns while gradually improving accuracy. With machine learning, the system learns and improves as new data is made available. 

Another branch of computer science and AI is natural language processing. This branch of AI enables computers to process human language — received through text and spoken words — and to understand its meaning and intent. It basically allows a computer system to understand the semantics of conversational language. 

The AI branch of robotics process automation, also known as software robotics, involves the use of applications and systems to perform human-like tasks. It uses intelligent automation technologies and rule-based software to perform business process activities at a more efficient volume, reducing the need for human resources or involvement in the task. 

Finally, the AI branch of speech recognition enables a system to identify and process human speech in written format. Speech recognition may also be referred to as automatic speech recognition, computer speech recognition or speech-to-text. This AI technology is often confused with voice recognition, which focuses on identifying an individual user’s voice. However, speech recognition focuses on translating speech from verbal to textual. Each of these artificial intelligence branches are utilized throughout financial institutions and countless other industries around the world. 

The benefits of AI

AI is used in various fields and applications, ranging from online shopping to advertising and machine translation, enabling cross-language communication to improve the overall operations and cost efficiency of financial institutions. The use of AI technologies in financial institutions can drastically reduce operational costs while significantly increasing productivity. 

With its broad range of uses, AI can potentially aid financial institutions in reducing costs associated with products and services, and it can enhance the overall customer experience as it bridges the gap between customer convenience and relationships. AI can benefit a financial institution’s lending process as it can expand credit access, assist in financing decisions, decrease underwriting times and costs and enhance both the borrower and lender experience. 

AI can be beneficial in other areas within financial institutions, such as identity validation and real-time anti-fraud monitoring. The opportunities and benefits seem to be endless. But there have to be challenges, right?

AI challenges 

AI isn’t perfect. Like any other enhanced technology, AI comes with its own set of risks and challenges. Some of those risks and challenges include system integration and skill gaps. With system integration, the data behind AI is equally as critical as the technology itself. In order for the utilization of AI to be beneficial and effective, the data quality and quantity need to be accurate. This involves organizing data and preparing for integration. This means that financial institutions with a core processor will have to coordinate between their core systems and their AI technologies. This can often be a complex and costly undertaking that is financially burdensome, especially for small financial institutions and community banks. 

Financial institutions may also run into a more complicated integration process if their core processors and AI solution vendors are competitors of the same or similar products and services. This challenge often leads to increased fees and costs for integration. Even if financial institutions are able to work out all the kinks related to system integration, there is always the challenge of obtaining expertly trained staff who are knowledgeable in building and deploying AI solutions. The rapid advancement and use of AI technologies have led to a shortage of skilled AI experts in the broader labor force. While this is a challenge that is expected to improve in the future, it presently leaves financial institutions competing with large tech companies such as Apple or IBM when recruiting for AI talent. 

An even more challenging area associated with artificial intelligence and financial institutions is meeting compliance expectations for technologies that are surrounded by so much regulatory uncertainty. 

Financial institutions are expected to identify and manage all risks related to artificial intelligence and how it is used within the organization. It’s not enough for financial institutions to simply employ the technologies of AI — rather, they are expected to understand the data or inputs that drive the outcomes. 

Financial institutions are expected to ensure that all data used within the various branches of AI aligns with regulatory compliance requirements. For example, if the machine learning branch of AI is used in the decision-making process for credit, the bank should understand and be prepared to explain what the contributing factors were that the AI system used to make that decision (i.e., what data was input to receive the outcome or decision). 

It is critical that financial institutions are not only able to understand and explain this process, but also that all the data used within the AI system meets regulatory requirements. This means ensuring that the AI system isn’t using information that may violate consumer or fair lending laws. 

Financial institutions that are utilizing AI should have processes in place that allow for the identification of risk — both new and emerging — as well as controls for managing that risk. Because of the rapidly evolving technologies of AI, there is always the challenge of changes in risk level or even unidentified risk development. 

Financial institutions need to be prepared to rise to the occasion when it comes to meeting those regulatory and risk challenges, whether that be through an increased frequency of monitoring and reviewing established controls or contracting with external vendors to conduct robust third-party risk management. 

The use of AI technologies within financial institutions has captured the interest of regulators and policymakers alike. Key concerns are always the safety and soundness of financial institutions and consumer protections. While AI is constantly growing and advancing, many of the banking laws and regulations currently on the books are still a little behind the times, leaving some areas of regulatory uncertainty. Nevertheless, regulators acknowledge the benefits of AI and support responsible innovation by financial institutions. 

In 2021, the agencies (CFPB, OCC, FDIC and Federal Reserve Board) issued RFI’s (requests for information) on the use of artificial intelligence by financial institutions. In 2022, the OCC issued supervisory expectations for how banks should manage the risks associated with AI. And in April 2023, a joint statement was issued by the agencies on the enforcement efforts against discrimination and bias in automated systems. The 2023 statement outlines some of the challenges of AI and serves as a reminder that financial institutions must embrace responsible innovation.


For financial institutions to thrive in the industry and remain relevant in the market, they must continue to be forward-thinking and responsible in their innovation efforts. Financial institutions must engage in the balancing act of supporting new and innovative technologies for their consumers while also acknowledging and managing the risks and challenges of such growth. It is imperative that we fully understand the technologies that our institutions rely on for their operation and that we remain abreast of any arising issues in the regulatory world.

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