Excited about AI in financial crime? Get your data ready first

This article is for financial crime professionals exploring how to make gains with AI in financial crime, and why strong data infrastructure is essential to making it work.

Key takeaways

• AI in financial crime holds promise if your data is clean, connected, and real-time.
• Most AI models underperform without structured, accessible data.
• Trust is essential — in your systems, data, and decision-making.
• You need strong data infrastructure before adopting AI tools.
• Salv Bridge enables intelligence sharing that makes both AI and human decision-making faster and more accurate.

There’s plenty of speculation and high hopes about what AI could do for financial crime teams.

Sharper detection, less manual work, and lower costs are all appealing. But the reality so far has been mixed.

Without access to well-structured data, even the most sophisticated AI will fall short, explains Taavi Tamkivi, CEO and Co-founder of Salv.

“One major problem is always data: access to it, managing it in real time, applying decisions in real time.”

His insight comes as financial institutions face increasingly sophisticated fraud (sometimes powered by AI) and rising regulatory pressure. Many are discovering that the foundation for effective financial crime prevention isn’t more powerful algorithms. It’s better data and crucially trust that makes data sharing possible.

Why AI in financial crime starts with data, not models

Running AI on fragmented, low-quality data doesn’t lead to smarter decisions — it leads to frustration. When organisations adopt AI without addressing data access and quality, they end up with underperforming models.

Data pipelines are the priority. Without them, real-time decision-making is very difficult — no matter how advanced the technology is.

“Usually the first problem is that companies can’t process their data,” says Taavi.
“Especially now, with instant and faster payments, the infrastructure hasn’t caught up.”

This is where many financial institutions find themselves. Realising that, while AI has its place, it can’t compensate for deficiencies in the underlying data infrastructure.

For many, the journey toward truly effective crime prevention starts with improving how data is gathered, structured, and shared.

Why clean, structured data is the foundation for AI in financial crime

While our platform does have AI capabilities, we focus more on ensuring the data infrastructure is in place. This is our philosophy at Salv.

We favour systems that can structure data consistently — and secure channels that make it easy to share intelligence between institutions, like Salv Bridge.

It’s designed for rapid, structured exchanges of intelligence — not raw data — so teams can flag suspicious activity fast, and do it securely. This collaborative approach breaks down silos between different financial crime teams.

Broader patterns can be spotted that are often invisible from one company’s perspective. Taavi compares this with blockchain-based systems, where each transaction is recorded on a ledger visible to the entire network.

“In crypto, transaction data is open, making it easier to detect suspicious patterns.
In traditional banking, that openness doesn’t exist — and criminals know it.”

Salv helps bridge that gap. Our platform enables institutions to collaborate securely without breaching EU data privacy laws. The result? Better intelligence and more financial crime uncovered. Once your data is clean, connected, and real time, AI becomes a logical next step.

Why trust and transparency are essential for AI in financial crime

Alongside good data, there’s another essential ingredient: trust. Whether it’s between institutions, with regulators, or with the data at our disposal — trust is the invisible glue that makes financial crime prevention possible.

As such, trust also underpins AI adoption itself. In areas like sanctions and fraud, regulators expect you to fully explain your tech stack. Black-box models that can’t justify their decisions pose a risk.

That’s why Salv uses AI to support, not replace, human judgment. It’s there to help validate decisions. Trust makes decision-making possible. And it makes AI decisions explainable, defensible, and dependable.

Build the right foundations for AI in financial crime

AI in financial crime holds real promise. But without the right foundations, that promise is impossible to reach.

It starts with better data. Data that’s structured, accessible, and shareable in real time. On top of that, we need trust — in the data, where it comes from, and the decisions being made.

To make AI work its best, we need to build the technical and human infrastructure to get the best of both worlds.

For more hot takes from Taavi, follow him on LinkedIn, where he posts about the latest happenings in financial crime.

Frequently Asked Questions

Q: What is AI in financial crime?
A: AI in financial crime refers to the use of artificial intelligence to detect, investigate, and prevent fraudulent transactions and money laundering — often by automating decision-making, improving pattern recognition, and reducing false positives.

Q: Why does AI in financial crime depend on data quality?
A: AI models can only perform well if the underlying data is clean, consistent, and accessible in real time. Poor data leads to poor predictions — no matter how advanced the model.

Q: What makes AI adoption risky in financial crime prevention?
A: Black-box models that can’t justify decisions may conflict with regulatory requirements. Explainability and trust are essential for using AI in regulated environments like banking.