Marius Silvasan is CEO of eCapital, a fast-growing fintech firm transforming financing for small to mid-size companies.
AI has been heavily hyped since the launch of ChatGPT. I’ve seen some of the biggest AI enthusiasts make bullish claims that AI will change everything, spark a massive economic boom and even replace most human workers. However, I’ve also seen some investors and tech industry commentators share concerns that too many big companies are betting too heavily on AI without demonstrating enough specific use cases and real-world business value. Already in 2025, I’m noticing a shift: Businesses are beginning to demand a return on investment (ROI) for their use of AI.
In reality, AI is already delivering an ROI to many businesses, and the financial services industry is among those leading the way in adopting AI to save money and boost productivity. An Accenture report found that generative AI for banking could drive between 22% and 30% productivity increases for early adopters in the next three years.
For the financial services industry, it’s become clear the AI genie will not go back in the bottle. This is especially true for small to medium-sized business (SMB) lending. Here are the ways AI is bringing business value to SMB lenders and their customers, along with some of my predictions and a look at the challenges lenders need to consider.
How AI Is Impacting Lending
Supporting Accessibility And Efficiency
Banks and lenders are already developing profitable use cases for AI tools. For example, some companies, my own included, use AI with the goal of making SMB financing more accessible and on-demand. We use AI to help assess risk, detect fraud and anomalies and more efficiently provide capital.
I expect lenders’ use of AI will continue to play out this year as companies explore ways to enhance their embedded finance and digital lending platforms so SMBs can unlock access to capital. This is in stark contrast to manual loan processes that are often cumbersome and time-consuming. Using AI for lending can help banks and financial technology firms develop faster, more efficient ways of doing business, with a more seamless customer experience for borrowers.
Augmenting Jobs And Automating Tasks
The work of underwriting and issuing loans for SMBs requires a lot of repetitive, manual business processes. AI can be used to augment or, in some cases, automate these processes to create a more effective and streamlined experience. In fact, research from Cornerstone Advisors and Zest AI found that 80% of respondents believe AI can improve lending processes. Moreover, Accenture’s aforementioned research found that 73% of U.S. bank employees’ work time could be affected by AI—39% with automation and 34% with augmentation.
That doesn’t mean I think AI will replace all humans or lead to massive reductions in head count at banks or lending companies. Instead, lenders can use AI to help do their work faster, more accurately and at lower cost. When it comes to SMB lending, we’re already seeing use cases of how AI can help evaluate credit applications, set up loan pricing and make loan offers.
Driving Toward A Fully Digital Lending Experience
I expect to see increasingly digital lending solutions powered by AI and embedded finance in the coming years, with platforms evolving to support lending offers, invoicing and financing. Machines may handle everything from generating offers and setting up pricing to managing and collecting payments.
As early adopters, nonbank lenders using this tech can gain an edge over banks that have been slower to adopt AI. Using AI to automate processes, personalize offers and streamline loan approvals can help nonbank lenders improve efficiencies and enhance the customer experience. This year, I expect to see lenders aiming to create more seamless, digital experiences for SMBs seeking funding, with AI leading the charge.
Managing Challenges And Risks Of AI
AI is transforming financial services. However, these innovations bring challenges, particularly with regulatory compliance and data integrity.
While there is a federal framework for banking regulations, states may impose additional rules, which can create variability. Lenders must ensure AI tools are built with an understanding of these rules, including anti-money laundering, know-your-customer and fair lending laws.
Data integrity is also a concern, with issues like outdated data, bias, inconsistency and the need for secure financial information. Despite AI’s ability to offer valuable insights, ensuring data integrity and human oversight is essential, particularly for high-stakes decisions.
Looking Ahead: Transforming The Job Landscape
Some people worry that AI might lead to a loss of good-paying jobs. I think it’s too early to predict, but I don’t believe that AI will be a net destroyer of jobs. In my view, AI could drive a shift toward higher-value, higher-paid “AI-augmentable” jobs that can be based in the U.S., such as managing or supervising AI. Instead of a machine being responsible for the quality assurance of people’s work, we might need more people to supervise the quality assurance work of a machine and AI interfaces; fine-tune AI models; or otherwise provide discretion, context and creativity in ways that only humans can do.
If AI does replace any jobs in the short term, I believe most of those jobs will likely be business process outsourcing roles that conduct repetitive, manual tasks. As AI makes it possible to automate more of these tasks, some of these roles might no longer be needed.
Ultimately, I believe AI in business lending could become an economic bright spot this year, as it is the next logical step in improving SMBs’ access to capital. Since SMBs are the lifeblood of the economy, the impact could be profound.
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