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Blog > 3 Data Considerations for Successful AI Implementation in AML

3 Data Considerations for Successful AI Implementation in AML

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Sophisticated technology solutions can assist regulated firms in dealing with the massive amounts of data they manage as part of their anti-money laundering efforts.

According to Refinitiv's Global Risk and Compliance Report for 2021, there is a strong desire to invest in new technologies: 86 percent of respondents agreed that technology has aided in the detection of financial crime, and 57 percent want to increase their tech spending, particularly on automation and digitization.

Artificial intelligence (AI) and machine learning provide significant speed, efficiency, and accuracy benefits. However, in order to work effectively, data must be prioritized, as it is a critical component of any AI strategy's success.

Data quality

Data quality can be one of the most difficult obstacles to overcome when it comes to implementing and maximizing the use of AI and machine learning-based solutions. And data quality isn't just about completeness and accuracy, though those are important as well; it's also about knowing where your data came from:

  • What country is it from?
  • What method did you use to collect it?
  • What are the ramifications of training machine learning models with that data?

When obtaining data from third-party sources, this is especially important.

It's also important to think about how you'll manage or handle your data in accordance with regulations.

If you're combining multiple data sets, you'll need to know where and how the data is stored, as well as whether it's being transferred from one jurisdiction to another.

All of this must be done while remaining within the bounds of applicable data protection laws.

Data structure

The structure of the data is a critical requirement for AI and machine learning systems. Though some of the newer tools on the market are agnostic to data type and structure, avoiding the need for a costly and time-consuming data cleansing exercise, ingestion, normalization, and the combination of structured and unstructured data must all be considered.

You must be able to specify how you want your data to be sliced and diced to the level of granularity and specificity that you require.

Combining structured and unstructured data can be a lengthy exercise, but tools like OMNIO's Customer Monitoring are available to help find the most relevant pieces of information in context within unstructured data such as adverse media.

Data completeness

One of the significant advantages of tools like OMNIO's Customer Monitoring is that they provide a single view of a customer. It is through the linking of data sets—in this case from know your customer (KYC) and transaction monitoring—that enables AI to spot signals that would otherwise be invisible to a human analyst or impossible to connect over multiple links.

This data must be complete in order for organizations to derive the most value from it.

We recognize that we have reached a critical juncture in the fight against financial crime. As an industry, we are grappling with difficult questions about effectiveness and the need to go beyond technical compliance.

It's easy to get caught up in the data, processes, and documentation (all of which are important) and forget that the real goal is to provide law enforcement with actionable information to help them disrupt and fight crime.

Why are data and machine learning useful for AML?

Machine learning is the most common type of AI used in AML solutions that use AI.

Machine learning is a branch of artificial intelligence that excels at detecting similarities and differences in large patterns of multidimensional data. Because both customer due diligence and transaction monitoring require large data sets, it's an ideal candidate for AML solutions.

Machine learning is especially useful for identifying items that rules-based solutions can't detect and for reducing the white noise associated with false positive alerts. It employs algorithms to make predictions based on data it has never seen before, allowing it to detect new financial crime typologies as they emerge, rather than just known criminal patterns.

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