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Use signal to detect and prevent threats

Unlock insights from unstructured text to maximize regulatory compliance and protect against fraud
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The history of banking is a history of mitigating risk

Deep learning models more accurately identify inherent risks and propagate these insights as signals that banks can operationalize in downstream workflows. From fraud protection to customer loyalty, these models protect the signal-oriented universal bank against a revolving door of regulatory and antagonistic threats.

Leveraging deep learning to conquer risk

The evolution of banking is irrevocably tied to the development of novel methods to quantify and manage risk. Advances in communication and data processing technologies help banks surmount risks and satisfy customers’ changing needs.

We trace the evolution of banking through the ever-changing manner in which technology-enabled analytics techniques help mitigate risk.

Deep learning

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Protect people and payments

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Put data to work

03

Maximize compliance

Why it matters

Bad actors and threats of fraud are an ever-increasing concern in the global digital banking sphere. According to the Federal Trade Commission, consumer reports of fraud including identity theft and imposter scams rose 20% between 2020 and 2021 to 5.7 million instances. To protect customers and funds and maintain compliance with evolving regulatory policy, the signal-oriented universal bank is equipped with ready-built data solutions that maximize compliance and adapt to a changing financial landscape.

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Protect people and payments from threats of fraud

Flag suspicious payments faster and with more accuracy by enhancing the universe of structured and unstructured data with a payments network graph. Connecting digital payment authorization data with the context of channel interactions data improves the ability to detect fraud, proactively alert customers, and shut down suspicious activity.

Leverage unsupervised learning to adhere to anti-money laundering policy

Since a critical consideration for successful adherence to anti-money laundering (AML) policy is the generation of suspicious activity reports (SAR) and alerts, banks must choose data solutions that adapt with circumstances. AML models are more adaptable than traditional rules engine approaches so your models evolve with ever-changing threats and emerging policy.
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Maximize regulatory compliance to protect brand and bottom line

In 2020, institutions and individuals incurred over $10 billion worth of fines for compliance failures. Without proactive risk controls, banks put their financial future and their reputation on the line. Maximize regulatory compliance with ready-to-deploy approaches to address AML, SAR, and know your customer (KYC) with deep learning models.

SambaNova helps you simplify the process of creating “language-as-a-service” signals to detect threats and protect interests in the universal bank.

Bad actors and threats of fraud are an ever-increasing concern in the global digital banking sphere. To protect customers and funds and maintain compliance with evolving regulatory policy, the signal-oriented universal bank is equipped with ready-built data solutions that maximize compliance and adapt to a changing financial landscape.
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Discover how to detect threats and maximize compliance with world-record accuracy