Abrar Huq
Co-founder and Chief Revenue Officer

In today’s climate, financial institutions are focused on the balance sheet. Banks of all sizes are looking for ways to drive down operational costs while keeping up with the high demand for client service. Over the past few decades, technology has been a powerful catalyst for achieving these goals. Large-scale transformations have helped create differentiated service and strengthened risk management capabilities while improving capital efficiency — the holy trinity of the fintech value proposition.

This value has primarily been reserved for the largest and most complex institutions that have invested billions in time, money and resources to implement massive transformations. In contrast, community banks tend to be much leaner by design — budgets are tighter, technical capabilities are often less available and the people managing change need to ensure the core business runs smoothly in the foreground. This is one of the reasons why community banks have traditionally had less access to innovation than their larger peers.

Then came ChatGPT. OpenAI put large language models into the hands of the world, kicking off one of the fastest moving technical progressions in history by democratizing  productivity. Community bankers should be gearing up to capture this value.

Artificial intelligence (AI) can help propel the manual and outdated operational processes that exist in every bank. The capabilities of core systems will continue to expand, while new areas for efficiency will come to the fore. One of the highest-value areas for this is unstructured data, which lives outside of a database or spreadsheet. It is estimated that over 80% of data in an enterprise is unstructured, and less than 1% of all data is used in decision-making.

One of the most relevant repositories for unstructured data is documentation, which contains the most critical terms and figures to do business. Documentation is one of the biggest pain points of nearly every operational process in a bank. AI-powered software can now offer these institutions the ability to inject automation into the formulation, negotiation, execution and management of their documentation. The data that has been landlocked in documents that can now be fed back into core processes and decision-making.

With the current shifting regulatory environment, compliance is top of mind for every banker. Ever-changing regulatory requirements are challenging to keep up with, and even slight missteps can be very costly. An AI-enabled compliance toolset can help to substantially improve risk management and regulatory adherence. AI will lower the cost of advanced pattern recognition, which is especially relevant in data-rich scenarios like fraud detection and credit risk assessment.

From a compliance standpoint, AI can help identify gaps in regulatory requirements and automate critical tasks, reducing operational errors and risks that come with manual oversight.

AI can also help banks streamline the implementation of new platforms and technologies. With software, AI is already significantly helping to reduce the cost of personalization, resulting in pre-baked solutions that are close to, if not fully, out-of-the-box ready. For example, no-code interfaces are quickly becoming table stakes, allowing users to intuitively configure and customize enterprise-grade solutions without the help of advanced technical skillsets. Additionally, the effort for systems integration will continue to decrease as AI automates more of the grunt work.

AI could also help to transform change management and user adoption processes. The combination of intuitive interfaces, AI-assisted user training and the ability to rapidly show value will lower both the time and risk of implementing new software. This is where the playing field will really level as access to powerful new tools will no longer be contingent on daunting investments and disruptive resource allocation.

The hype around AI is real. Since the rise of ChatGPT, many have prophesized its potential impact. Only now, as more tangible applications come to market, are we starting to see the trends start to crystalize. While AI is a very powerful tool, it is not a magic button. Community bankers must be strategic in how and where to deploy AI, always putting business value first. By taking a thoughtful approach to AI, community banks can ensure they get the most from this technology and be more competitive both now and in the future.


Abrar Huq

Co-founder and Chief Revenue Officer

Abrar Huq is co-founder and chief revenue officer of Arteria AI, and award-winning leader in enterprise digital documentation, leveraging artificial intelligence and a data-first approach to make documentation processes and management frictionless for financial institutions.