Building AI Infrastructure for Real-Time Financial Crime Prevention

The next challenge in financial crime prevention is not simply detecting fraud more accurately. It is building infrastructure capable of operating at the speed of modern financial systems.

Real-time payments, AI-assisted fraud tactics, and rising compliance expectations are exposing the limits of legacy AML architectures built for slower transaction environments. Financial institutions now face pressure to process growing volumes of transactional data, make explainable decisions, and maintain operational efficiency without indefinitely expanding compliance overhead.

That shift is accelerating investment in AI infrastructure designed for continuous monitoring, adaptive risk analysis, and scalable decision-making. For Madhu Nadig, CTO of Flagright, the larger transformation is architectural: financial crime systems are evolving from static compliance tools into real-time operational infrastructure.

Why are legacy AML systems struggling in the AI era?

Most financial institutions are facing a tough reality. Fraud detection tools are getting smarter every few months, but the AML systems behind them were built for a different era.

We have seen this gap grow: banks promote new AI fraud prevention, but their main compliance systems still rely on rules from years ago, created by teams who are no longer there. This mismatch is more than a tech issue. It is an urgent problem.

The fundamental issue is that legacy systems were built to catch what they already knew. Static, heuristic-based rules work well when fraud is present. The main problem is that old systems were designed to spot familiar threats. Fixed, rule-based methods worked when fraud changed slowly. That is no longer true.

Fraud patterns now change in weeks, since criminals use the same AI tools that compliance teams are still waiting to try. A rules engine cannot keep up with attackers who adapt every day. It only catches old crimes while new ones slip by.

Real-time payment rails like SEPA, UPI, and Fed have now become the global norm; batch processing is not just inefficient, it is irrelevant. By the time the system catches the transaction, the money has moved, the account is empty, and the synthetic identity that opened it three weeks ago has already been dissolved.

We asked Madhu G Nadig:

When you first started building Flagright, what made you feel the existing AML and fraud stack was fundamentally broken?

Madhu G Nadig:
Financial institutions have always struggled with fragmentation, and that’s exactly where Flagright was born. Three core insights shaped our vision from the beginning. 

The first was recognizing that most financial institutions lack a singular operating system, a unified pane of glass that consolidates everything needed to combat financial crime. Instead, organizations typically rely on multiple siloed tools and disconnected data systems. Centralizing all financial crime operations, we believed, would drive enormous gains in both operational efficiency and effectiveness.

The second one is that, as generative AI adoption increases, fraud and financial crime are no longer a human against human problem. Attackers were the first to use generative AI to create synthetic entities like deepfake KYC sessions. Social engineering was widely adopted. So, the nature of the threat fundamentally changed. And globally, across all institutions and even regulators, there has been a push to adopt real-time payments. There are real-time payments in Europe (e.g., SEPA), in India, and in the US.

Historically, AML financial crime systems have always been post-processing and batch-based, whereas Flagright started out as a real-time, native system. We were built for real-time payments. These were the three key insights that led to the formation of Flagright.

Many fintech leaders still discuss AI risk in theory. Based on what you are seeing firsthand, how has generative AI already changed the fraud landscape?

Nadig:
Generative AI has fundamentally changed the fraud landscape by making attacks faster, cheaper, and far more scalable.

On the threat side, as I mentioned briefly earlier, the attack surface has widened significantly due to generative AI. You can generate synthetic entities. It’s very easy to generate a fake document. It’s easy to perform account takeovers. Social engineering has never been this easy.

Historically, we’ve seen fraud and AML typologies evolve over months or maybe even years. Now we’re seeing them evolve in weeks because it’s so easy for external agents to test their schemes and iterate more quickly. First-party fraud and authorized push payments are the fastest-growing categories and aren’t caught by traditional external fraud controls.

Now coming to how Flagright helps with this, there are three things here.

First is consolidating the stack and adopting AI native architecture while investing in explainability from day one. Flagright allows organisations to unify fraud and AML because siloed systems create blind spots.

The second key point is real time. Batch processing is obsolete for most fraud use cases, especially card acquiring, A2A payments, and real time payments. Decisions need to happen in hundreds of milliseconds, and that’s how Flagright operates by default.

The third is hybrid detection. Traditionally, most financial crime detection relied on static, heuristic based rules. With Flagright, we’re increasingly seeing AI driven adaptive systems being more effective while remaining equally explainable as well.

It feels like the conversation around AI in compliance has shifted massively in the last two years. What are customers suddenly caring about now that they barely asked about before?

Nadig:
With the impact of AI, the key concern for institutions today is auditability. Auditability is nonnegotiable because every AI-assisted decision needs a reproducible reasoning trail. There needs to be an immutable, explainable audit log that can be presented to regulators.

Two years ago, auditability requirements mostly focused on how somebody within the system operated. There was no strong push for system level auditability. Now, with AI systems, there’s a major push for the system itself to maintain decision level audit trails.

The second key point is that institutions increasingly want agentic workflows to execute their own SOPs, their standard operating procedures.

Two years ago, most AI applications used proprietary algorithms to determine whether something was a false positive or required enhanced due diligence.

Now institutions want the agent workflow to execute their own procedures rather than rely entirely on external logic.

Hallucination controls are also becoming critical, grounded retrieval against institutional data, cited narratives, and tight guardrails on what the agent can or cannot do.

Everyone talks about AI efficiency, but where are customers actually seeing measurable operational gains inside compliance teams?

Nadig:
The biggest impact is on operational effectiveness and operational efficiency.

Once protection systems flag something, it needs to be investigated. Usually, an analyst has to manually review individual alerts, transactions, and cases.

Without Flagright, analysts typically need to log in to four or five systems, cross-reference data manually, and review every checklist item themselves. A simple alert could take 10 to 15 minutes.

Flagright centralizes all data in a single pane, so everything is in one place and easy to investigate.

But the real efficiency booster is our agentic investigations.

Before a human even looks at an alert, the system pre-investigates it. Customers can decide whether they want a human in the loop, where an analyst approves the investigation, or whether low-risk alerts can be fully autonomous.

Instead of a team of 10 people struggling to resolve 20,000 alerts, the AI takes around 80% of the workload, allowing analysts to focus on high-risk, complex cases.

What fintech CTOs are still underestimating about AI infrastructure?

Most fintech boardrooms are no longer debating whether to use AI, but many still struggle with how to manage it responsibly at scale. In fact, 75% of fintech AI projects never make it to production, and data quality problems cause 43% of failures in financial services.

Many CTOs still see AI as something to add onto their current systems, instead of building their architecture around it from the start. The difference between showing off an AI demo and actually running a compliant, real-time AI system across millions of transactions is much bigger than most leaders expect.

The challenge of making AI explainable is even more serious. The EU AI Act requires high-risk AI systems, like those in financial services, to meet tough standards for explainability, documentation, and human oversight.

Still, most companies use black-box models that can quickly flag fraud but can’t explain their decisions to regulators or customers. At CTO Magazine, we’ve shown that AI fraud prevention is now a key strategy, not just a support tool. If institutions can’t explain their AI decisions, they risk both regulatory trouble and losing customer trust.

The infrastructure gap is just as important. Scaling AI is mainly about people and processes, not just technology, and most fintech teams don’t have enough resources for governance, model monitoring, or managing the audit trail.

JPMorgan Chase cut fraud alerts by 50% with their AI fraud detection system, mainly because they built explainability and auditability into the system from the start. This is the standard that CTOs should aim for.

Nadig, who works right at this intersection, shares a more practical perspective:

One thing that rarely gets discussed publicly is the waste in fintech infrastructure. From what you are seeing, where is the biggest inefficiency today?

Nadig:
In terms of infrastructure itself, there’s definitely a lot of redundant compute.

What I typically see in the market, especially among competitors, is that to support both real-time and batch processing, they operate separate systems. They have separate real-time and batch systems.

That creates redundant compute and redundant data storage. They’re essentially running two systems masquerading as one.

At Flagright, we take a different approach. We have a single system that is real-time by default but also supports batch processing. So we have one consolidated compute layer and one consolidated data storage layer that handles both.

If you were advising a fintech CTO right now, where would you tell them to put serious investment over the next 24 months?

Nadig:
If it’s a fintech I’m advising, I would definitely recommend investing in AI automation on the back office side, not just AML compliance and fraud operations, but across the entire operational stack.

Back office operations are definitely the best place to invest first, followed by developer productivity. Data infrastructure and the other areas follow.

Once companies scale beyond the startup phase, leadership challenges also change. What became much clearer to you after scaling engineering teams?

Nadig:
I think the importance of engineering culture becomes extremely clear as the company scales.

Flagright has always had a strong engineering culture because both my co-founder, Baran Ozkan, and I come from engineering backgrounds. We worked at organizations such as Palantir and Amazon Web Services, which are known for engineering excellence.

As the company scales, it becomes even more important to enforce engineering excellence and operational excellence because we provide a critical service to customers.

Customers rely on us to protect transactions and onboard users securely, so maintaining strong expectations and a strong engineering culture is critical. The default state of anything is entropy, so you need to continuously reinforce the culture to keep it intentional.

Looking back at the last wave of AI adoption, was there a technical decision that looked smart at the time but later felt unnecessary?

Nadig:
Yes. We were one of the first anti-financial crime vendors to have AI in production back in 2022 with GPT 3. One of the key lessons we learned quickly was not to solve problems that future model improvements would solve automatically.

At that time, GPT-3 had context-size limitations and hallucination risks, so we built a lot of infrastructure to manage them.

Then GPT 4 came out, and much of that work became irrelevant.

So the takeaway for us was that if something is a temporary deficiency in the model itself, it’s often better to plan around future improvements rather than build heavy scaffolding that will later become obsolete.

In regulated fintech environments, product speed and security requirements constantly clash. How do you make those calls internally?

Nadig:
Our philosophy at Flagright is to have smart defaults while making everything configurable.

We operate in more than 30 countries and serve customers ranging from tier one banks to very early-stage startups. So, the system needs to work easily out of the box while still allowing sophisticated institutions to configure everything according to their needs. When product direction and customer requirements don’t fully align, we ask ourselves whether something should become configurable for future customers as well.

We almost never custom-code something specifically for one customer. Instead, we build capabilities into the platform in a configurable way. And these decisions are always collaborative. Engineering, design, customer success, and often the customers themselves are involved in the process.

Many infrastructure decisions look perfect during evaluations but fail in production. Was there a moment like that for you?

Nadig:
Yes, definitely. One example was Spark.

At Flagright, we built our own proprietary data aggregation and rule engine architecture using NoSQL databases.

As we scaled, we explored whether a more commoditized solution like Spark could support our distributed computing needs more efficiently.

On paper, Spark made a lot of sense. We ran POCs, and initially, the results looked promising. But when we moved toward rollout, it simply didn’t meet the performance bar we required because of eventual consistency issues.

Our internal proprietary system ended up outperforming Spark across all the key benchmarks we cared about. So we decided to continue investing in our internal architecture instead of moving entirely to the Spark model.

What is one AI trend you think is overhyped right now, and one fintech innovation more people should be paying attention to?

Nadig:
One overhyped AI trend is the idea that programmers will be fully replaced.

AI definitely makes engineers more efficient. It can write tests, generate scaffold code, and significantly improve productivity.

But building complex systems still requires context-driven thinking, and current LLMs are nowhere close to fully replacing engineers. I think the broader narrative around the large-scale replacement of entry-level jobs is also more hype than reality at this point.

On the fintech side, stablecoins are one of the most important innovations, especially for cross-border transactions. I suspect that within five years, stablecoins will take over a large share of remittance and large transaction-based payment services.

AI infrastructure in fintech: Where the industry is headed?

Financial institutions spend over $206 billion each year on financial crime compliance. The AML software market is expected to almost triple in the next ten years. Still, fraud losses keep growing, compliance teams are stretched thin, and investigations are too slow for today’s real-time economy.

So, what’s causing this gap?

Flagright co-founder Nadig says the industry’s main weakness isn’t a lack of AI, but rather fragmented infrastructure.

Most institutions still use separate fraud tools, isolated AML systems, and batch-based setups designed for older payment methods. AI can help with detection, but it can’t solve these operational gaps by itself.

This is important because fraud has changed. Generative AI has made attacks faster and more advanced, from fake identities and deepfake KYC to large-scale scams and account takeovers. Schemes that used to take years to develop now change in just weeks.

Meanwhile, financial systems now support real-time payments, but many compliance systems haven’t kept up.

This has created a growing gap, with institutions trying to fight AI-driven fraud using systems built for slower, after-the-fact analysis.

From we’ve observed so far in the industry, the next big advantage in fintech won’t be about who uses the most AI. Instead, it will come from those who build infrastructure that can support AI under real regulatory pressure, like unified data, clear decision-making systems, and real-time processing that can handle complexity without breaking down.

For CTOs, that shifts the conversation entirely. The central challenge is no longer model capability. The question is whether the systems underlying those models are resilient enough to keep pace with the speed of modern financial crime.

About Flagright

Flagright is an AI-native platform for AML compliance and fraud. It serves banks, fintechs, payment providers, neobanks, brokerages, and crypto companies.

The platform brings together transaction-driven, AI-driven investigations, checks, risk scoring, case management, and regulatory filing in one place across more than 30 countries and helps financial institutions centralize fraud and AML operations while reducing manual investigation workloads.

Its real-time infrastructure and auditable AI agents are designed to help compliance teams move faster without sacrificing explainability or regulatory readiness.