Bank Secrecy Act - Anti-Money Laundering Compliance

Using neural networks to detect money laundering

Upgrade Compliance With Skymind Neural Networks

Speed and Complexity Are Overwhelming Most Banks. Only The Newest Deep-Learning Techniques Will Help You Keep Up.

Superhuman Accuracy

Deep learning algorithms are surpassing human performance on key data analysis problems.

Enterprise Support

Skymind offers the only commercially supported open-source AI toolkit for enterprise.

Real Use Cases

Deep learning is providing ground-breaking results in fraud and anomaly detection.

Financial institutions are devoting more and more resources to monitor transactions with technology. These institutions have ramped up hiring staff that can work with those monitoring systems, as well as contractors to support them. Under regulatory pressure, significant investments have also been made toward testing, deployment, hosting and training.

Deep learning and advanced predictive analytics offer a powerful solution for the AML problems faced by large financial institutions. BSA AML is going to be transformed by algorithms that can learn how to surface anomalous patterns of transactions, and cluster suspicious accounts.

Current AML technology runs financial transactions through a set of filters, or hard-coded and discrete rules, and sends “alerts” when a transaction triggers one or more rule; for example, cash transactions of more than $10,000.

The problem with systems based on hard coded rules is that they do not evolve in the face of changing data, which means that they are likely to erode in value over time, as money-laundering strategies adapt to institutional measures. They are a crude means to surface money laundering, and they flag many innocent transactions, or false positives.

False positives are incredibly costly for institutions' financial crimes analysts, who are forced to spend much of their time chasing down false leads. That time would be better spent in a more targeted fashion and a purer subset of transactions likely to represent crimes. That comes down to the quality of the filter imposed on those transactions, or how they are flagged.

Distributed deep learning not only promises more accurate models for detecting money laundering and other financial crimes, it is able to learn from new data automatically on massively parallel hardware. Systems that learn rapidly, without human intervention and error: that is the process and the promise of deep neural networks.

Optimized to run distributed on the corporate production stack integrated with Hadoop or Spark, deep learning’s ability to learn robust models from massive amounts of data, and return results almost immediately once deployed, gives compliance departments a new tool in the constant arms race.

Large institutions spend hundreds of millions of dollars per year fighting financial crimes. Much of that budget is misspent on legacy analytics and a few closed-source vendors. Current systems are encumbered with technical debt and legacy systems such as data storage in closed-source relational databases and lack of elastic resourcing for their models and pipelines.

Open-source deep-learning allows those institutions to empower their internal teams to create, customize and adapt Anti-Money Laundering Solutions with the help of deep-learning experts at Skymind.

Financial services has wholeheartedly embraced open-source, starting with Linux as an operating system, continuing through the big data ecosystem of Hadoop and Spark, and ending with the analytics necessary to take actions. Skymind’s deep learning offers record-breaking analytics tools to the open-source enterprise.

In the vanguard of the AI revolution, a few companies are moving now to apply the most accurate techniques to their costliest and most challenging problems. They are the companies embracing deep learning to cut risk and modernize compliance.

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