Finance & Fraud

Using neural networks to automatically detect and stop fraud

Fight Fraud and Monitor Risk With Skymind Neural Networks

Faster Transactions and Trades Threaten to Overwhelm Compliance. 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 services companies are drowning in data. Banks have to process many millions of transactions per day, which means their compliance departments may have to issue hundreds or even thousands of authorizations per second. They may have only a few milliseconds to judge whether a given transaction is fraudulent or not.

Many of them are forced sample only a tiny percentage of total transactions as they learn to model fraud. Massive downsampling means that these banks are not taking into account 90% of the transactions flowing through their system every day, and in doing so, they lose valuable information about the evolution of modern fraud.

These same institutions face an immobile necessity: they need to guarantee their customers and stakeholders 99.99% uptime. Their system can’t be fragile, and it can’t take a break for updates. It needs to learn robustly and automatically.

More efficient machine-learning is crucial to process transactions for efficiently and to learn from a wider array of data.

Distributed deep learning not only promises more accurate models for detecting fraud and other anomalies, 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 of deep, artificial 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 financial institutions spend hundreds of millions of dollars per year fighting risks like fraud. Much of that budget is misspent on legacy analytics and a few closed-source vendors.
Open-source deep-learning allows those institutions to empower their internal teams to create, customize and adapt Anti-Fraud 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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