Composite AI: The Foundation for Mission-Critical AI in Banking
In banking, AI is no longer being evaluated simply on what it can generate. It’s being deployed to make decisions, automate processes and support activities where accuracy, explainability and control are non-negotiable.
“According to the 2026 Gartner CIO and Technology Executive Survey, ‘the number of banks and investment firms deploying AI agents is set to double, with 41% of banks and 38% of investment service firms planning deployments in the next 12 months.”
But mission-critical AI solutions for banking cannot be built around a single AI technology. Large language models have transformed what AI can understand and generate. Yet they are not designed to be the sole foundation for complex, regulated business processes. These processes require domain knowledge, deterministic rules, contextual understanding, reliable evidence, explainability and efficient execution.
That is why Composite AI is becoming the foundation for mission-critical AI. It combines complementary AI techniques so that organizations can apply the right form of intelligence to each part of a complex business problem. In banking, this provides a practical way to build AI that can make sophisticated decisions while meeting the requirements for accuracy, transparency and control.
Why the Architecture Behind AI Matters
Banking AI operates in an environment where mistakes can have significant consequences. Decisions can affect customers, trigger regulatory obligations or determine whether potentially suspicious activity requires further investigation.
In these environments, AI needs to do more than produce a plausible answer. It needs to understand business context, apply relevant rules and policies, work with reliable information and provide evidence for its conclusions. It also needs to deliver these capabilities efficiently enough to create sustainable business value at scale.
That makes the architecture behind AI fundamental, not incidental. As Gartner notes, banking CIOs need to build a “robust, trusted foundation” in which AI agents can execute complex, auditable workflows with autonomy and compliance.
Expert.ai’s Neurosymbolic, Composite AI Approach
Composite AI provides a foundation for combining complementary AI techniques to address increasingly sophisticated reasoning, decision making and business processes.
Expert.ai brings this approach to enterprise AI through its semantic control and neurosymbolic AI Mesh architecture, which orchestrates knowledge graphs, deterministic linguistics, business rules and language models, using each according to the requirements of the task.
Instead of asking a large language model to handle every step, the AI Mesh determines where different capabilities can deliver the greatest value. Symbolic capabilities can classify content, identify entities, apply business policies and retrieve relevant evidence. Language models are used selectively when generating or more advanced language understanding adds value. The results can then be checked against their supporting evidence.
The result is an approach to AI that is more controlled, explainable, traceable and efficient, while reducing unnecessary reliance on large language models and helping organizations manage the cost and complexity of AI at scale.
The principle is simple: Mission-critical AI should use the right intelligence for each task, rather than expecting one technology to do everything.
Built for Where Accuracy and Explainability Matter
This approach is valuable wherever organizations need AI to work with complex information and make decisions where accuracy and explainability are critical.
Expert.ai’s AI solutions for banking apply Composite AI to financial crime, risk and compliance use cases including perpetual KYC, dynamic risk understanding, regulatory change management, adverse-media screening, sanctions and PEP screening, and identifying material customer changes. It also supports complaint management, credit underwriting, intelligent document processing and customer engagement.
The aim is to help organizations handle complex knowledge-intensive work faster, make better-informed decisions and extend the capabilities of their teams.
Expert.ai’s platform is already in production at 10 top banks in Italy and with global AML organizations. Customers report more than 90% screening accuracy, a 90% reduction in AML false positives, up to 70% time savings per alert in KYC screening and a 40% improvement in customer satisfaction.
AI Solutions for Banking: The Foundation for Mission-Critical AI
Gartner’s Coolest Vendor Innovations in Agentic AI for Banking, Part 1* identifies the foundational capabilities needed to support agentic AI across the enterprise, including data quality, workflow automation and explainability. Expert.ai is one of five “coolest vendors” featured in the report.
We believe this recognition underlines an important principle for organizations deploying AI in regulated environments: one AI technique cannot do everything well.
As AI takes on more complex business processes, organizations need to combine the technologies that are best suited to each task. This is the role of Composite AI in building AI solutions for banking and the approach Expert.ai brings to mission-critical enterprise AI.
*Gartner Coolest Vendor Innovations in Agentic AI for Banking, Part 1, Van To, Derek Frost, Sudarshana Bhattacharya, Debbie Buckland, Peter Ryan, 26 August 2026
Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research and advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to its research, including any warranties of merchantability or fitness for a particular purpose. GARTNER is a trademark of Gartner, Inc. and/or its affiliates.
