How Does a Unified Data Platform Improve Financial Crime Compliance?

For a long time, financial crime prevention has been a massive issue for banks. This is because of several often complex navigation around rules and regulations, such as AML, KYC, CTF, and SAR. However, a bigger issue has emerged. As more and more transactions happen and financial crime networks get smarter, the bigger problem is the way banks handle data.

In the recent conversation on the Don’t Panic It’s Just Data podcast, host Herb Blecher, Research Director, Data and Analytics, Enterprise Management Associates (EMA), sat down with guest Manish Andhy, Financial Services AI & Industry Executive at Teradata.

They talked about how criminal enterprises are changing fast, like the internet, while compliance systems are still using old methods. Andhy tells Blecher, "The difference between how fast criminals can change and how slow compliance systems are is a big weakness."

More than $3 trillion in money moves through the global financial system every year, but only a small part of it is caught. Banks have spent a lot of time and money building systems to stop money laundering. Many of them still have separate systems for onboarding, transaction monitoring, investigations and risk management.

The strange thing is that big financial institutions have already spent a lot of money to solve this problem. They built data warehouses to get rid of systems and data lakes to put all the information in one place. In fact, they often created the same separate systems they were trying to get rid of.

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What are the Key Concerns for Compliance Teams in Data?

Compliance teams, because they are worried about following the rules and avoiding risk, often take data from systems and make their own copies for their own use. Over time, these copies become systems with their own data, rules and versions of what is true about customers.

This costs a lot of money. According to Andhy, investigators spend up to 60 per cent of their time looking into alerts, “which is like putting together a puzzle with pieces that should already be connected.” Meanwhile, old systems that use rules to monitor transactions keep making a lot of alarms, which increases the workload without catching the threat actors..

The result is a problem in big companies, but it is happening in a high-stakes area – duplicated systems, inconsistent data quality and increasing complexity.

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What’s Teradata’s Strategy to Stop Financial Crime?

Teradata believes that to stop crime, banks need to rethink how they handle data. Instead of making separate systems for financial crime, they should use a unified platform with reusable "data products". This idea is similar to what other companies are doing with data management, but it is specific to banking.

In this system, the data is kept in one place. Managed as a single source of truth. Different users, like developers, investigators, auditors and compliance officers, can see the data from angles like looking at a Rubik's Cube from different sides. Each team sees what they need without making a copy of the cube.

This may sound like a difference, but it addresses a bigger challenge with big AI systems. These systems need data to work well. If they are trained on datasets or conflicting customer records, they will have the same inconsistencies.

This is becoming more important as banks start using AI in their workflows. For example, making activity reports is a lot of work. Analysts have to write descriptions that answer who, what, when, where, why and how.

Generative AI can help with this by making drafts from transaction histories, notes, emails and documents. AI also introduces a problem. Banks want to automate, but regulators want stronger controls.

Recent guidelines from regulators show this tension. While they are starting to acknowledge the use of AI, they are not sure how to govern it. The burden is on institutions to create their own rules.

This brings us back to the data. Andhy tells Blecher that data products are not just useful for operations but for controlling AI. They provide context and reduce the risk of inconsistent outputs.

For a time, compliance has been seen as a necessary cost but separate from making money. If banks create a unified data foundation, the same intelligence used to catch suspicious transactions could also help with customer segmentation, fraud detection and business analytics.

This changes the way we think about compliance. The question isn’t whether banks should modernise their compliance systems but it’s whether compliance can become a way for banks to get smarter about their business.

Because in a world where criminal organisations are always changing, having infrastructure is not just inefficient. It is also a weakness.

Takeaways

• Over $3 trillion of illicit funds moves through the global financial system annually.
• Only about 1% of illicit funds are detected or intercepted.
• AML programs have grown in a fragmented, siloed way across enterprises.
• The pace of change in compliance has historically been slow.
• Traditional rule-based systems generate enormous amounts of false positive alerts.
• Data silos persist due to organisational and technology issues.
• Data products allow for a unified view of data without creating new silos.
• Generative AI can automate parts of the compliance process.
• Financial institutions must treat data as a strategic asset.
• Compliance can inform broader business decisions beyond regulatory obligations.

Chapters

• 00:00 Introduction to Financial Crime and Data Analytics
• 05:06 Challenges in Financial Crime Compliance
• 10:05 Data Silos and Their Impact
• 15:12 The Concept of Data Products
• 19:57 Modernising Financial Crime Strategies
• 24:59 Conclusion and Key Takeaways

For more information on financial crime and how financial institutions should manage their data securely and compliantly, follow Teradata across its official channels: Teradata 

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