Autonomous AI data monitoring for a new era of AML strategy

Anti-Money Laundering (AML) is no longer all about checking boxes and submitting a high volume of Suspicious Activity Reports. It’s about having a sophisticated system for identifying what makes activity suspicious. These AML systems increasingly rely on vast amounts of data, and the quality of that data can be the difference between catching suspicious activity and letting a fraudulent transaction pass by undetected.

It’s no wonder that forward-looking financial institutions are moving toward Self-Driving Data. With the new capabilities offered by a coordinated system of autonomous AI agents, AML teams can leverage a digital workforce that oversees every aspect of their data’s health around the clock. That means less time tracking down data issues and more time focused on case management.

New paradigms in AML methodology mean more data than ever before

Best practices around Anti-Money Laundering are changing. In the United States, FinCEN has been signaling a shift away from defensive filing, with an emphasis on program effectiveness and reduced expectations around Suspicious Activity Report (SAR) volume. EU regulators are also emphasizing clearer and more defensible AML programs.

While there will always be a place for threshold-based AML decisions, the trend is toward actionable insights that cut through the noise. After all, criminal activity is becoming more complex over time: The number of money laundering cases is rising, and the amount of AML typologies is growing. Novel pathways for criminal activity signal a need for comprehensive detection programs where nothing slips through the cracks.

What “comprehensive detection programs” means in practice is larger datasets and more involved processing. It’s like packing for a trip. If you decide what to bring based only on the temperature it’ll be at noon on Tuesday, you might find yourself in a snowstorm wearing shorts. Just like your trip planning becomes more effective when you have access to a robust weather report, your AML strategy is stronger when it’s based on multiple markers taken together. To a large extent, the more data, the better the insights.

One bad apple can spoil the data bunch

There is one catch, however. Only healthy, high-quality data is useful. Missing, incomplete, or corrupted data isn’t just useless, it’s actively harmful.

An order-of-magnitude error that lists a $100,000 customer transaction as $1,000 could lead to an AML false negative. Stale sanctions lists could also mean unintended oversights. Corrupted segment data could cause false positives and lead to unnecessary delays in customer transactions. In recordkeeping, missing or corrupted data could even bring regulatory scrutiny or fines.

High-quality data forms the foundation for more advanced AML models and increases trust for day-to-day case management tasks. Low-quality data can lead to faulty decisions and the additional time expense of fixing data issues.

Legacy data monitoring systems can’t keep up with modern needs

The scale and complexity of today’s data needs have grown beyond what legacy data monitoring systems can handle. Trying to monitor modern data with observability-only monitoring or manual checks has several glaring downsides:

• Data availability doesn’t equal data validity
  Observability-based monitoring systems equate data availability with data validity. But being able to access a table isn’t the same as knowing the data inside is healthy. Metadata checks are great for efficient oversight of thousands of tables at once, but they aren’t sufficient for critical datasets.
• Manual rules don’t scale
  Each new onboarded dataset requires manual rule configuration and tweaking. A half-hour configuration job on one table is fine when you have ten tables, but not when you’re working with enterprise-scale data.
• “Unknown unknowns” aren’t covered
  You can only build manual rules for failure scenarios you can predict. If you don’t anticipate a novel issue (like missing data in a specific, previously-complete segment), you can’t protect against it.
• Rigid rules don’t respect fluid data
  Manual rules need to be updated every time the expected behavior changes. Unfortunately, enterprise data tends to fluctuate over time. That can mean adjusting a manual rule’s expected-value window for each peak and valley in the business cycle.

Put together, these shortcomings can send data governance teams into a backward-looking doom loop.
First, they spend time firefighting a novel issue (often after it’s affected downstream tables and workflows). Then, they write manual rules to ensure that the issue is caught sooner next time. After they’ve spent time configuring the rule, an alert comes in about a different problem, and the cycle starts over again. To break the loop, top teams are moving away from manual checks and towards autonomous AI data monitoring.

How to choose an autonomous AI data monitoring system to empower trustworthy AML predictions

When data governance teams reach for autonomous AI data monitoring, they’re hoping to relieve the day-to-day burden of manual rules and gain proactive, automated AML insights. But how do you figure out which tool will actually accomplish those goals?

First, consider “table stakes” features, basic requirements for any monitoring system. A system with just these capabilities will already help catch issues like complex drifts in customer profile information.

• Coverage for unknown unknowns
  Any data monitoring system worth working with should be able to find novel and complex data issues without any manual input. Checks shouldn’t be deterministically defined. Instead, they should look for changes and unusual activity across your entire table. As a bonus, because of this broad scope and fluid criteria, a good system for surfacing unknown unknowns can actually help uncover new AML typologies!
• Seasonality awareness
  If your data has seasonal shifts, your data monitoring system needs to account for them automatically. If the system sees you’re approaching a busy shopping season, it should take that into consideration before issuing an alert for high transaction volume. Seasonality awareness keeps prediction intervals narrow and accurately positioned, without manual intervention.
• Ecosystem compatibility
  The most feature-rich monitoring system won’t help you if it can’t connect to all of your data warehouses or data lakes. Also desirable is a tool that connects to your broader ecosystem, integrating with data catalogs, issue resolution tools, communication platforms, and anywhere else your team works.

Firms looking to upgrade their AML predictions may find it useful to also look for the supplementary features below. These features support advanced AML techniques and allow SMEs to iterate on AML strategies faster, without engineering support.

• Monitoring for non-tabular data
  Data sources like PDFs, text documents, and logs often go unchecked, since they’re difficult to systematize. If your vendor supports monitoring for non-tabular data, that opens up opportunities like generating sentiment alerts for text-based news reports. This functionality makes sentiment analysis or adverse media screening more repeatable, sustainable, and trustworthy.
• Segment analysis
  Deviation from peer behavior is a major AML typology, so segment analysis capabilities are valuable in an automated AI data monitoring system catering to the financial services industry. Consider choosing a monitoring system that can notify you if any segment or entity is an outlier.
• Low-friction custom check creation
  With legacy systems, non-technical users are often left waiting for overstretched data engineers to create custom checks . Low-code and no-code custom checks are a worthwhile addition to a data monitoring system, reducing engineering workloads and unblocking non-technical users.

Now that Self-Driving Data has arrived, the most advanced autonomous AI data monitoring systems are offering even more benefits for companies investing in robust AML strategies. These quality-of-life features further democratize data governance and accelerate monitoring cycles, providing “always on” oversight for your data. We’ll cover a few specific benefits below, but this is by no means an exhaustive list.

• Direct chat with your data
  Siloed information is now a thing of the past. Anyone, including non-technical SMEs, should be able to use natural language to chat directly with their datasets. AI data agents should help users explore the history and contents of a table, understand the root cause of an issue, and investigate interesting metrics in the data.
• Check configuration through natural-language prompts
  Not only should AI data agents help non-technical users set up existing types of checks, users should be able to ask an agent to create a fully custom check. This makes granular data monitoring more approachable than ever, helping SMEs truly feel like part of the data governance team.
• Bespoke, no-effort documentation
  Documentation is typically an area where everyone agrees it’s important, but no one can find the time to work on it. AI data agents should do the legwork for you, using context awareness and table history to generate useful, bespoke documentation. Team members should be able to upload supplementary resources and information, or adjust the documentation if needed.

From seasonality awareness to automated segment analysis and autonomous 24/7 insights, the right data monitoring platform can save your enterprise time and improve AML predictions. With a platform like Anomalo, users can get set up in minutes and experience all of the benefits of robust data monitoring and analytics at their fingertips. And now, Anomalo’s agentic AI further democratizes access, allowing AML professionals to generate new insights with natural language prompts.

How Discover promoted data trust with Anomalo

Discover knew that their enterprise data was only useful if it was complete, accurate, and timely. More data presented an opportunity for new insights, but they needed to ensure that data-driven decisions were trustworthy.

Legacy, deterministic data quality monitoring wasn’t feasible at scale for the amount of data Discover was using. By their estimation, implementing manual-rule-based data monitoring for their entire data warehouse would take 25 years. Instead of spending decades building that system, Discover leveraged Anomalo’s scalable monitoring to reach data confidence in a small fraction of the time.

Anomalo delivered reliable monitoring for tables of all widths and depths, with the same configuration time for a 10-column table as a 1,000-column table. Also compelling were a modern self-service UI, complex alerting capabilities, and a robust list of integrations. These features empowered Discover to democratize data governance and trust their mission-critical tables, ensuring those new insights were backed by healthy data.

What’s next?

Anti-Money Laundering programs now require more data, and they require that data to be verifiably trustworthy. With a longstanding background as a leader in enterprise data monitoring (we literally wrote the book on it), Anomalo is ready to support enterprise AML programs with our state-of-the-art autonomous AI data monitoring platform.

AI data agents are standing by to help your team trust your data and refine your AML predictions. If you’re ready to meet them, visit our Anomalo for Financial Services page to start learning more.