Artificial intelligence is becoming a competitive tool for lenders looking to grow small business portfolios, improve risk selection, and reduce fraud and manual underwriting costs. This opportunity is particularly significant in segments where traditional credit models may overlook viable businesses because they rely heavily on limited financial histories, collateral, or conventional borrowing models.
Saugat Nayak is a data science and risk analytics expert with over 15 years of experience in financial technology, telecommunications and consulting. His work focuses on machine learning-driven credit risk, real-time fraud detection, behavioral analytics, and artificial intelligence decision systems deployed in manufacturing environments. He has helped explore how alternative data and dynamic behavioral signals can give lenders a more up-to-date view of business performance while supporting faster, more consistent decisions.
In this interview, Nayak discusses how financial institutions can use AI to identify creditworthy small businesses that legacy models might miss, reduce false positives in fraud detection, and successfully move risk models from research to day-to-day operations. It also explains why data quality, explainability, model monitoring and cross-functional adoption are important for institutions seeking to expand responsible lending while increasing efficiency and capturing untapped market opportunities.
1. Traditional credit scoring has been the mainstay of lending for decades. why you
Do you believe these models often fail when valuing small and minority-owned businesses?
Traditional credit scoring was developed in a different era, for a different borrower profile. This
based primarily on credit history, debt-to-income ratios, and collateral, mostly preferred metrics.
established enterprises with long financial experiences. The problem is that very small and
Minority-owned businesses simply don’t fit this mold because the credit risks are poor, but
because they have historically operated outside the systems that generated those signals.
Instead of taking over, a going-out business owner reinvested cash flow
credit, or building a customer base through community networks will not look good on FICO.
based model. That doesn’t mean they don’t deserve credit, but it does mean the model isn’t asking
the right questions.
2. You’ve spent most of your career developing risk models powered by artificial intelligence. How is the car?
Are learning and behavioral analytics changing the way lenders evaluate credit?
The shift I see is from static snapshots to dynamic instances. Traditional models look
location of the borrower. Machine learning allows you to see how they behave and how they behave
a behavioral signal is more predictive than a credit score.
In my work building risk models for SME lending, I have seen how variables such as cash flow.
consistency, transaction speed, seasonal patterns, and even supplier payment behavior
tell a richer story about the health of the business than just the balance sheet. Behavior
analytics adds another layer and it shows how borrowers interact with financial products
notes anomalies or, equally important, patterns that indicate risk over time
a reliability that traditional computing would completely miss.
What excites me the most is that these models get better with data. How diverse
The borrower population I train with, the more nuanced and fair the model becomes.
3. Many small businesses struggle to access financing despite healthy operations. How
Can alternative data help create a more complete picture of the borrower’s financial health?
Alternative data fills the gaps left by traditional underwriting. When I work
I look at signals like credit risk models for small business lending, point of sale transaction data,
online review sentiment such as payroll consistency, inventory turnover, and even power of attorney
occupational health. These are not exotic data sources, but traces of each transaction
business naturally lags behind.
For a restaurant, consistent weekend revenue increases and stable supplier payments speak volumes to me
more about vitality than two years of tax returns. The challenge is creating models that can
synthesize these signals responsibly, making sure the data is relevant, not just correlated.
4. AI has the potential to improve lending decisions, but it also raises fairness concerns
and bias. What steps should financial institutions take to ensure these systems remain in place?
transparent and fair?
This is something I think deeply about in my work, especially with these things in mind
models have a direct impact. The first step is to acknowledge that bias in AI is not visible
nowhere but inherited from biased historical data. If my training data represents decades
discriminatory lending, my model will learn these patterns unless I actively intervene.
In practice, this means establishing fairness constraints directly for model development
process, don’t treat them as an afterthought. This means testing model outputs
demographic segments before deployment, not just overall accuracy metrics. And it means that
When investing in explainability, lenders must be able to explain to the borrower why they are refusing.
plain language that is both a regulatory expectation and a fundamental issue of fairness.
Explainable AI isn’t just a compatibility checkbox; This is what makes these systems reliable
rather than reinforcing exceptions, it is actually sufficient to widen access.
5. As financial services evolve, fraud prevention becomes increasingly important
digitize. How can AI help organizations detect fraud while retaining positive customers?
experience?
The tension between fraud detection and customer experience is real, and it’s one I work with
directly. The traditional approach is to note anything that seems unusual and put it away
While it creates a sharp sense of friction for legal customers through a manual review queue
sophisticated fraudsters find solutions.
What AI enables is more precise targeting. By establishing basic behavioral profiles
Individual users can distinguish between a customer who travels and models
unusual purchases against the fraudster who took over the account. This means accuracy
fewer false positives, which means fewer good clients are blocked at worst
moment. The main thing is that the device fingerprint,
geolocation consistency, session behavior, so the system makes decisions based on full
The picture is not an anomaly.
6. As financial institutions adopt more advanced AI systems, what are some of the biggest ones?
Implementation challenges you see when moving these models from research to reality
world production environments?
It’s the gap between a model running on a laptop and a model running in production
where most AI initiatives fail silently. I have seen this pattern many times. The data in the study is clean,
latency is negligible and outliers can be processed manually. None of this in production
luxuries are available.
The most common problems I see are data pipeline fragility, model drift, and organizational problems
preparation. The model developed on the basis of last year’s data begins to degrade at the moment of market change
and economic conditions in SME lending can change rapidly. Holding building monitoring systems
early drift and rapid response pipeline reengineering is as important as the model itself.
Another challenge is getting people to underwriters, compliance teams, and product managers
truly trusting and acting on model results requires an investment in communication and
an education that most ML teams don’t appreciate.
7. Looking ahead, how do you see AI reshaping access to entrepreneurs and capital?
underserved communities in the next five to ten years?
I’m really optimistic about this, with some important caveats. The next decade will see that
alternative data and real-time underwriting are becoming the standard practice rather than the competition
differentiators. This structural change will significantly expand the pool of small banks
especially businesses in communities chronically underserved by traditional lending.
What reassures me is that the economic incentive coincides with the social incentive. there is
Hundreds of billions of dollars of untapped credit opportunities in the SME segment. Creditors
Which builds sophisticated enough models to identify creditworthy borrowers
Misses are not just good. They enter the market where their competitors have left behind.
Alignment of profit and purpose leads to real, lasting change.
8. What advice would you give to financial institutions looking to use AI?
to improve operational efficiency, but also to expand responsible lending and better serve micro-small
business owners?
Start with the problem you’re actually trying to solve, not the technology. I have seen the institutions
they’re investing heavily in AI infrastructure before they’ve defined exactly what success looks like to them
specific borrower population and risk appetite. This leads to models that are technically
impressive but operationally insignificant.
Organizations that do this well are those that treat AI as an ongoing capability, not a stand-alone one.
time project. They invest in data quality, monitor model performance
downstream, and they build cross-functional teams where data scientists work alongside credit
officers and compliance starts from day one. Responsible lending is not a limitation for artificial intelligence, quite the opposite
is a design principle. The best models I’ve built have been models with justice.
explainability and accuracy were considered equally important from the first line of code.
