The signal-oriented bank of the future

Unlock the value trapped in your unstructured data

Your most valuable data asset

To improve every aspect of your bank’s business at scale – optimize the customer experience to improve customer retention, streamline operational efficiency to reduce costs, and reduce credit and regulatory risk – you need a plan for unstructured data.

The most information-rich data in a banking organization, unstructured data makes up 80% of the total landscape. Speech and text is the most valuable as it is the one instance that customers tell the bank what they want and need. But this powerful data, including sentiment from a customer call, reasoning behind customer emails, and technical product details from an internal knowledge database, often goes to waste because traditional systems are not equipped to interpret it.

Deep learning empowered by modern AI tools like foundation models help banking organizations unlock the insights within unstructured data and evolve from being simply data-driven to turning data into value and becoming signal-oriented.

SambaNova helps you simplify the process of creating “language-as-a-service” signals to unlock insights within your customer data


Use strong signals to enhance customer journey

In today’s digitized banking system, consumers are far less likely to remain loyal when they experience dissatisfaction and distrust. Preventing churn and delivering great experiences requires strong onboarding (Know Your Customer) and a signal-oriented contact center powered by enriched and workflow-ready data flows.


Banks cannot identify customer intent in unstructured data such as voice recordings, call transcripts, emails, and chat messages. Analysis is performed utilizing indirect data and measures mostly through feedback and surveys.


By understanding language context, Large Language Models can unlock critical insights and context of customer interactions across multiple channels. This enables banks to proactively determine the intent of a customer’s call.


Proactively understanding a customer’s intent enables banks to accelerate customer task resolution, improving customer satisfaction and reducing churn while simultaneously reducing average handle time and customer wait time.

“Artificial intelligence really changes how we look at business requirements and business potential. We often say that companies like us have a huge amount of data but what we really need is not data, it is information and insights. Artificial intelligence speeds up how we turn data into business insights.”

— Péter Csányi

Board Member & Chief Digital Officer

Assess and mitigate new and existing risks

According to McKinsey, efforts to mitigate payments fraud, including mitigation expenses and foregone revenue, can account for 0.75% of a bank’s total revenue. According to public sources, this could represent more than a billion dollars per year. By augmenting payment transaction data with digital transaction data, banks can begin to see hidden relationships and alert customers to the first signs of fraud.


As more payments are authorized on digital channels, more opportunities arise for credentials to become compromised and for bad actors to opportunistically steal account information or impersonate authorizing parties.


Banks can develop an enhanced knowledge graph to ensure the validity of customer contact information in banking channels. Fraud is identified by fusing payments data with unstructured data generated by inbound communications.


Enhancing the context of digital payment authorization with channel interactions data in a payments network graph improves fraud detection and supports efforts to proactively alert customers to suspicious transactions.


Turn signal into success in Capital Markets

Most asset optimization models utilize well-defined and structured market data but are unable to incorporate unstructured data from sources such as SEC filings, earnings reports, news, social media, and other publicly available data. Processing these unstructured documents quickly is key to creating a differentiated trading strategy.


Because of the complexity and inconsistent format of these documents, processing them requires manual work as well as domain expertise. Significant errors during evaluation or reduced evaluation coverage become likely.


Large language models can extract key insights at scale from complex documents such as 10-K filings, earning reports, and social media feeds, allowing banks to incorporate these insights strategically ahead of their competition.


Asset managers and banks can improve the speed, accuracy, and competitiveness of their trading decisions by incorporating previously inaccessible inputs into models, delivering improved returns and increased profitability.

Learn how SambaNova Suite is helping enterprises unlock the value trapped in their unstructured data