Interview with Ara Azaryan on the New Science of AI-Driven Credit

The days of static credit limits and monthly portfolio reviews are officially over. As artificial intelligence permeates the finance function, its most profound impact may be in the high-stakes world of credit management. But how do these "advanced applications" translate into real-world protection against bad debt and acceleration of cash flow?

To explore the frontier of autonomous credit, we spoke with Ara Azaryan, leading financial technology expert. With multi-year experience in the financial sector of Armenia—including leadership roles at major financial institutions like VTB Bank and Ardshinbank—Azaryan offers a pragmatic view on how AI is transforming credit from a reactive control function into a predictive growth enabler.

Rita Danielyan: Mr. Azaryan, for decades, credit management has been a conservative, rule-based discipline. Why is it suddenly at the center of the AI revolution in finance?

Ara Azaryan: Because the cost of being slow has become too high. Traditional credit management is like using a rearview mirror. You assess a customer's health based on last year's financial statements, set a limit, and then hope for the best. But in a volatile economy, a customer's health can deteriorate in 30 days, not 12 months.

AI is revolutionizing this field because it introduces dynamism. It allows us to move from static, periodic reviews to continuous, real-time assessment. For a CFO, this isn't just about efficiency; it's about protecting margins and seizing opportunities. If you can accurately assess risk in real-time, you can confidently extend credit to a growing customer today, rather than waiting for a committee meeting next quarter.

Rita Danielyan: What is your expert opinion on "Autonomous Credit Risk Assessment." That sounds like a machine taking over a job traditionally done by seasoned analysts. How does that actually work in practice without introducing massive risk?

Ara Azaryan: It's crucial to understand that autonomy doesn't mean absence of oversight. Think of it as a highly intelligent triage system.

When a credit application comes in, an autonomous system doesn't just run a credit check; it ingests a universe of data. It looks at payment history from your ERP, external credit bureau scores, macroeconomic trends affecting that customer's industry, and even unstructured data like news feeds.

Based on that, it makes a decision instantly. For 80% of straightforward applications, it can approve within pre-set boundaries without a human touching it. The remaining 20%—the complex cases, the outliers, the applications that fall outside the risk appetite—are flagged and routed to your senior analysts with a complete data dossier. It eliminates the bottleneck of manual processing while ensuring that human judgment is reserved for the decisions that truly need it.

Rita Danielyan: What you think about "Generative AI for Credit Analytics". How does that differ from the predictive models banks have used for years?

Ara Azaryan: That's a great question because the term "generative" is often misunderstood. In this context, it's not about creating text; it's about creating scenarios.

Traditional predictive models are good at answering "what is likely to happen?" based on past data. Generative AI-powered analytics can answer "what could happen?" It can simulate thousands of potential scenarios. For example, if interest rates rise by another 50 basis points, or if a key customer in the supply chain goes bankrupt, how would our specific portfolio react?

It allows a CFO to stress-test their receivables portfolio against macroeconomic shocks they haven't even seen yet. It generates the insights needed to make proactive, strategic decisions—like tightening credit in a specific sector before a downturn hits—rather than just reacting to losses after the fact.

Rita Danielyan: One of the most intriguing points is the use of Cognitive Automation to interpret unstructured data. Why is this such a critical capability for modern credit departments?

Ara Azaryan: Because the vast majority of valuable data is locked in formats machines can't read. Think about it: a customer's payment comes in with a PDF remittance advice. A contract has special payment terms buried on page 15. A dispute email mentions a specific invoice number.

Historically, a human had to open these documents, read them, and manually key the data into the system. That is slow, expensive, and error-prone. Cognitive automation, using technologies like natural language processing, acts like a digital clerk that can read, understand, and extract that data instantly.

For credit management, this is a game-changer. It means you can assess a customer's true payment behavior by reading the narrative on their remittances, or you can automatically validate a dispute by cross-referencing it with the delivery terms in their contract. It closes the data gap that has always existed between the physical transaction and the financial record.

Rita Danielyan: Let's talk about "Dynamic Credit Limit Adjustments." This is a powerful concept, but it also sounds risky. How do you ensure that a machine doesn't get too aggressive in cutting off a good customer or too lenient with a bad one?

Ara Azaryan: The key is the "human-defined guardrails" I mentioned earlier. The AI doesn't operate in a vacuum; it operates within a framework set by the finance leader.

You define the rules of engagement. You might say, "We never want credit exposure to a single customer to exceed $1 million without a human review." Or, "We will never drop a customer's limit by more than 20% in a single day based on a real-time signal."

Within those boundaries, the AI optimizes continuously. It sees a customer is paying slower, perhaps due to seasonal issues, and recommends a temporary, modest reduction. Or it sees a long-standing client has just received a major funding round and suggests increasing their line to capture more of their business. It’s about continuous optimization, not erratic swings. It’s a co-pilot, not an autopilot.

Rita Danielyan: Mr. Azaryan from your experience I would like to know your opinion on "Early Warning Systems." How sophisticated are these today? Can they really predict a bankruptcy before traditional signals?

Ara Azaryan: Absolutely. They are far more sophisticated than the simple "over 90 days late" flags of the past. Modern early warning systems are like a health monitor for your receivables.

They look at leading indicators, not just lagging ones. They might detect that a customer who always paid on the 5th of the month has started paying on the 10th. Or that their interactions with your support team have become tense. Or that there are negative news articles about their supply chain. The AI correlates these disparate, seemingly minor signals and says, "This account now has a 35% probability of default in the next 60 days."

This gives the finance team a crucial window—maybe 30 to 60 days—to proactively engage. They can call the customer, adjust payment terms, or secure a personal guarantee before the account ever becomes a collections problem. That's the difference between protecting cash flow and writing off bad debt.

Rita Danielyan: Finally, you have a "Real-time Credit Monitoring Dashboard." For a busy CFO, is this just another screen to look at, or does it fundamentally change how they manage the business?

Ara Azaryan: It changes everything, because it changes the conversation. Instead of a monthly report that tells you what happened last month, a real-time dashboard gives you a live view of your risk landscape.

A CFO can see, in real time, that exposure to a specific industry is rising, or that a major customer's credit score is flashing yellow. They can then ask their team, "What's our strategy here?" It transforms credit management from a historical reporting function into a forward-looking strategic command center. It allows the CFO to steer the company away from risk and toward profitable growth with confidence, because they have complete, instantaneous visibility. That is the ultimate goal of this technology.

BY Rita Danielyan