Financial services has always been tip of the spear when it comes to using machine learning and artificial intelligence toolsets within their business. From fraud detection and BSA/AML compliance, to high frequency trading, many of the larger financial services have over a decade of experience and bespoke models that can deliver human out of the loop solutions to their business.
With the rise of new AI techniques like Generative AI and agentic AI frameworks, new use cases are developing to bring solutions to all aspects of the business, from IT, to marketing, sales, and human resources. This blog will discuss the different types of solutions and their relevance in Financial Services, as well as some of the technologies Cisco has released that is helping them on their journey.
A summary of traditional AI/ML and Generative AI use cases currently in use, and being explored, in financial services is below,
| Use Case | Examples | AI/ML technologies |
|---|---|---|
| Regulatory Compliance KYC/AML (Compliance) | Know your customer research and reporting, AML research and reporting | Human out of loop AI/ML Deterministic Models |
| Software Development | “What part of this code won’t scale” Copilot software writing. This is one of the most profound areas of growth given the high nature of software development in FSI. | GPT/LLM based models optimized for code, RAG, optimized IDE’s with copilot integrations. |
| Document Management (Document AI) | All Financials are based on extensive documents, categorizing, classifying, securing, and deriving meaning Faster and more informed decisions, data governance integrity and compliance Automated check deposits, in app check deposits | Human out of loop AI/ML Deterministic Models |
| NLP | Customer interaction, NLP in equity investing, Sentiment analysis | Human out of loop, AI/ML Deterministic Models , GPT potential/Assist |
| Fraud | Fraud detection, fraud reporting and compliance, reporting on AML for regulatory compliance | Human out of loop GBM, Deep learning + GBM, ML |
| Information Summarization | Wealth management solutions that provide summaries of information | Human in the loop GPT |
| Customer App enhancements | Spend analysis, natural language understanding | Multiple tools using traditional AI/ML as well as integration with newer GPT |
| Chatbot | Account information via human like interface Virtual assistants | Human out of loop Third party and internally developed Guardrails with Deterministic AI (today) |
| HR | Recruiting, screening, summarization | Third party GPT/LLM based services, on premise RAG |
| Sentiment Analysis : Stock | NLP | Human out of the loop published sentiment indexes using deterministic AI AI/ML/NLP |
| General purpose copilots | “what are the important parts of this legal document” “summarize this document“ Augmented search | Human in the loop GPT/LLM |
Cisco Solutions to help Financial Services
At Cisco I have worked with large clients on their journey with infrastructure and artificial intelligence. Cisco offers various solutions to help financial services to transform their operations, de-risk their capital expenditures, accelerate time to market, and secure their most critical asset: their customers. Cisco provides multiple solutions that can best be summarized into a few key areas:
- Accelerated Infrastructure for Artificial intelligence – AI Ready Data Centers
- AI Ops and Digital Resilience
- Security for Artificial Intelligence.
We will briefly explore each of these below and follow up in subsequent blogs on this topic.
AI Ready Data Centers for Financial Services
The different use cases for Financial Services, coupled with the inherent structured and semi structured data that underpins core FSI operations, has lead FSI to be early adopters of deterministic models that run many existing tasks at scale. Factoring compliance requirements in with the upfront investment to adopt newer technologies, two factors that influence investment decisions is the ability to de-risk the investment, and the ability to ensure agility and compliance, at scale.
Cisco’s advances, not only in the silicon, but the manageability of Compute + Storage + Network has made Cisco a market leader in converged offerings. Cisco has expanded upon this with AI Pods, Cisco Secure AI Factory, Nutanix, and Hyperfabric for AI. These products align to both Cisco Validated CVD’s and NVIDIA reference architectures for deploying AI Factories into your existing Data Center and colocation environments. Through using validated converged offerings you can de-risk your investment and ensure your deployment will be supportable and scalable, out of the box. Our partnership with NVIDIA has also allowed Cisco to be the industries only switching provider that can conform and be supported with the exacting requirements of the Spectrum-X architecture. Depending on the use case and GPU scale, Cisco can provide high scale silicon-1 based AI solutions, or NVIDIA silicon solutions, using the same operational models and APIs to reduce the barrier of adoption and ensure operational consistency.
AI Ops and Digital Resilience
Cisco and Splunk are jointly deployed at the largest G-SIFI financial institutions to provide IT operational insights, as well as application performance of core platforms. Cisco has invested to integrate the acquisitions of Thousand Eyes, App Dynamics, and Splunk into a platform for AI Ops that can work with third party vendors. Cisco is the leading provider of active synthetics that can integrate with collaboration endpoints, laptop endpoints, branch devices, campus switches, WAN, Data center, and cloud, providing an end to end ability to detect and alert and respond to service disruptions, while ensuring any migrations to cloud meet baseline service level objectives. Through using Splunk to provide a unified platform for agentic ops, Cisco enables a council of experts deployment where you can harvest the domain expertise, like Nexus Dashboard, Cisco SD-Wan, Catalyst Center, and third party domain experts like VMWare and F5, in a single platform.
For more information on how Splunk is transforming Financial Services : https://www.splunk.com/en_us/solutions/industries/financial-services.html
Evolutionary to this, and built upon the data platform, is Cisco’s AI Canvas. Cisco AI Canvas is a shared, collaborative workspace for IT teams that uses generative AI to help them troubleshoot issues and automate tasks across networking, security, and cloud domains. It’s built on Cisco’s Deep Network Model and functions as a generative UI that dynamically creates dashboards and provides AI-powered agents for collaborative problem-solving and real-time action.
Security for Artificial Intelligence.
Financial institutions are not only faced with the security challenges of securing their AI deployments, but are regulated and need to be able to provide consistency of security controls and localization of data. Financials are more exposed to the risk that sensitive data can leak through prompts and responses, models can be manipulated, and regulators or litigators will expect logs. To help with this, Cisco provides Cisco AI Defense, which can provide consistency of controls and centralized guardrails across cloud and on premise environments. These guardrails help enforcing policies on which models can be used, how data flows are handled, and who can access AI resources. By addressing AI‑specific attack vectors—such as prompt injection and model misuse—while maintaining least‑privilege access and encrypted pathways, it helps financials scale AI confidently without compromising security or trust.
The value of AI Defense extends to governance and compliance, which are critical in regulated financial services. It delivers model posture and lineage tracking, approval workflows, and comprehensive audit trails, making it easier to demonstrate control over model changes, datasets, and configurations. Built‑in data protection—such as inspection, redaction, and data loss prevention—reduces the risk of exposing personally identifiable information or confidential transaction data. These capabilities support regulatory obligations around data residency, transparency, and operational risk, helping institutions shorten audit cycles and avoid compliance‑driven delays.
Operationally, AI Defense unifies visibility and control across hybrid AI pipelines, integrating with Cisco’s broader security and observability ecosystem to detect anomalies sooner and respond faster. Standardized policies and continuous monitoring improve resilience, reduce incident impact, and lower the cost of securing AI initiatives. The result is faster time‑to‑value: institutions can deploy newer Generative AI services more quickly and safely, preserve customer trust through consistent protections, and realize the benefits of AI at enterprise scale.
For more information on Cisco AI Defense, https://www.cisco.com/c/en/us/products/collateral/security/ai-defense/ai-defense-ds.html
Conclusion
Financial institutions are unique in that they are both large adopters of traditional AI/ML systems in their existing workflows, and have some of the most exhaustive regulations that need to be met in deploying generative AI tools. They exist in one of the fastest moving industries, with changes in digital currencies, low barriers to entry for upstarts with novel business strategies, a constantly shifting regulatory landscape with unique nation and state requirements, and AI tools rapidly evolving. Securely transforming existing business models requires consistency and determinacy in an ever changing landscape. Cisco is uniquely able to assist Financial services, with the experiences and lessons learned from three decades of experience in the financial services industry.
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