Machine Learning Platform for Banks

Challenges

Customers are expecting faster transactions, decisions, and customer support from their banks. Several challenges prevent executive, product, and engineering teams from meeting those demands faster.

Lack of AI/ML talent
There are not enough people with experience in developing and deploying ML solutions for banks.
Regulatory compliance
Strict internal and regulatory policies prohibit deployment of untested, unhardened solutions.
Point solutions
It is not clear how ML can be used at banks to add significant value to customers and stakeholders.
Infrastructure
Common ML solutions are often not compatible with hybrid, Java-based, secured infrastructures of banks.

Solution

Only the newest Machine Learning capabilities will help banks keep meet the demand as soon as possible. Skymind is a platform for developing and shipping ML capabilities such as fraud detection, customer service bots, and automated analytics into a bank’s applications.

Ship ML-powered applications faster

Empower engineering teams to use the language to build ML functionality for Java applications in enterprise environments, using the language and environments they already know.

Reduce operational overhead

Eliminate dependencies and interoperability issues between data science and engineering teams. Get enterprise-grade model reliability, performance, and scaling without maintenance, all on the JVM stack.

Automate business processes based on structured or unstructured data

Build ML-powered capabilities into existing banking workflows using streaming or stored data from ERP and CRM systems, data lakes, Spark, Kafka, and elsewhere. Improve model performance over time with quality monitoring, A/B testing, and winner selection.

Make data science work useful

Engineers can integrate and deploy Python-based models developed by data science research teams into the bank’s JVM environments and applications, or build their own models in a tracked and collaborative environment.

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Perspective: Machine Learning in the Banking Industry

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.

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.

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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