Tom Byrne is General Manager of commercial lending at nCino.
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Artificial intelligence is no longer a future concept in finance.
One area where this shift is most visible is commercial lending. From onboarding to risk assessment, AI is moving deeper into processes once defined by paperwork and long lead times. The promise is faster approvals, smarter decisions, and more time for bankers to focus on relationships.
But there are still questions — especially about fairness, transparency, and what it really takes to unlock the value of data.
In this interview, we hear from Tom Byrne, General Manager of Commercial Lending at nCino, who brings experience from both traditional banking and fintech. Today, he focuses on how commercial banks can use data and intelligent automation to improve lending decisions — and deliver better service.
The conversation touches on everything from explainable AI to what commercial bankers will be doing in the next years. Byrne also makes one thing clear: using AI in a meaningful way is about making existing data useful.
You can read the full interview below!
R: Can you share a bit about your career journey and how you transitioned in the role of General Manager, EMEA & International Onboarding – Product and Engineering at nCino?
T: Prior to joining nCino, I worked in relationship management and then delivery at Lloyds Banking Group, where I managed the implementation of a variety of digital transformation projects across the commercial bank.
I joined nCino in 2017, working first as a Delivery Lead before becoming Head of Product for EMEA. I have held the position of General Manager, EMEA – Product and Engineering since 2021.
I’ve recently shifted my scope to onboarding where I focus on Client Lifecycle Management opportunities at financial institutions across the EMEA region – enhancing onboarding processes within the nCino Platform.
In practice, this looks like equipping institutions with the processes, data & intelligence automation, and connectivity to streamline their onboarding across both digital and human channels, changing how they manage critical activities for new & existing clients.
R: Having worked in both traditional banking and fintech, what are the biggest differences you’ve observed in how technology is shaping commercial lending?
T: Traditional banks are relationship-based, focusing on bringing value to their customers and helping them achieve their financial goals. Before the age of digital transformation, tools of the trade were checkbooks. Now, banks have invested heavily in digital front ends that make it easier for customers to bank on the go. However, banks still struggle to bring these same operational inefficiencies and manual processes to the back-end.
This is where fintech plays a major role. Technology was first focused on addressing the need for digitized data storage and interaction, which is where you get the term ‘cloud banking’.
Now, using the workflows established on cloud infrastructure, fintech is enhancing banks’ data using AI and data intelligence. This next evolution is making it easier for loan officers to review the vast amounts of data captured when onboarding a customer, collating it into easy-to-interpret analysis.
This makes existing processes more efficient, provides insights into steps that originally required manual research, and gives valuable time back to banks to focus on their customers.
R: AI is transforming many aspects of financial services. Based on your experience, what are the most significant changes AI has brought to commercial lending in recent years?
T: AI is rapidly changing many aspects of commercial lending. The extent to which AI has enabled lenders to provide a high degree of personalization to their clients is one of the biggest changes.
By equipping employees with the tools they need to address a customer’s unique goals and circumstances, AI is making the time to approval faster, while providing sophisticated solutions to customers – further augmenting the customer experience.
AI tools are also being deployed to improve processes like credit assessment, fraud detection, and compliance, reducing the potential for human error and providing greater certainty for customers.
At nCino, we are uniquely positioned to bring AI innovation to the market in a game-changing way by helping institutions unlock their data to drive value. Given the breadth of the platform, we see so many opportunities to create automation and embed intelligence in processes.
R: Bias in AI-driven lending models is a growing concern. How do you approach ensuring fairness and transparency when integrating AI into lending decisions?
T: This is something that we continuously think about at nCino. The best way to remove bias is to adopt explainable AI models, which are key to preventing unfair lending practices and building trust with borrowers.
When used correctly, AI integration can potentially increase fairness in lending decisions through a variety of mechanisms. For one, AI can analyse alternative data types, such as online transactions, to assess the credit risks of borrowers who are often disadvantaged due to low credit scores or a lack of credit history.
Through its advanced predictive analytic capabilities, AI can forecast borrowers’ future financial struggles, allowing lenders to proactively offer support, mitigating potential defaults. In the same way, AI can help lenders see opportunities with existing clients to expand their business with the institution.
R: As AI takes over administrative and operational tasks, how do you see the role of commercial bankers evolving in the coming years?
T: As AI is increasingly deployed to fulfill administrative tasks, we see it as an augmentation to the role of commercial bankers. This will allow employees to become more focused on their customers and strengthen these relationships.
As AI is deployed for more manual, time-consuming tasks, I think we’ll see an increase in the number of customers banks engage and an increase in customer satisfaction. Additionally, I think employees will become deeply specialised, with AI-driven insights guiding employees to where their expertise is truly needed.
There are four core areas that I think AI will improve operations at commercial banks:
- Intelligent solutions: Pulling from the vast amount of data banks collect, intelligent, AI-powered solutions can build and customize products to fit the specific needs and future growth plans of each borrower.
- Smarter risk assessment: AI can analyse vast amounts of financial and non-traditional data (e.g. news articles, social media) to create more accurate and holistic credit profiles. This leads to smarter loan pricing and reduces risk.
- Fraud detection: AI can detect fraudulent applications and suspicious activity in real time, protecting lenders from potential financial losses.
- Improved efficiency and automation: AI can automate tasks like document analysis, verification or generation, significantly reducing processing time and manual effort, allowing more time for relationship building that was previously used for manual process.
R: What are some of the biggest challenges you’ve faced in implementing AI-powered solutions in lending, and how have you overcome them?
T: Data drives the banking industry, and as banking has become more digitised, the amount of data that banks have has grown exponentially. However, managing that data and making sure it’s usable can be a challenge.
When used with clean data, AI can provide a holistic snapshot of the customer, enabling greater customer insights that have the potential to reduce credit losses, decrease monitoring costs, and improve productivity.
Aligning front- and back-offices with clean data can significantly increase efficiencies for employees and enhance the customer experience. But these efficiency gains can’t be achieved if institutions ask, 'how do I get more data’ when they should be asking ‘how can I create value from the data I already have?’.
When looking at the challenges that we’ve helped our customers overcome, the first step to unlocking the data is understanding it. By showing them how to better use their data through intelligent automation, they open the door to better analysis, smarter solutions, and more time to build relationships with their customers.
R: Looking ahead, what emerging trends or innovations in AI do you believe will have the most significant impact on the future of commercial lending?
T: As AI evolves from predictive and generative models, agentic solutions will become increasingly leveraged and intelligent automation will transform complex multi-click tasks into simple one-click solutions.
An increasing demand for digital solutions shows how consumers are no longer content with one-size-fits-all services. To stay competitive, financial institutions will increasingly focus on relationship management.
