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SmartAgentBank
Private demonstration platform.
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Authorised access only · SmartAgentBank © 2026
⚡ SmartAgentBank — Demonstration Platform  |  All data is synthetic and for illustrative purposes only
AI-Powered Banking Intelligence Platform

The Intelligent Bank of Tomorrow
Operating Today

SmartAgentBank demonstrates how autonomous AI agents can orchestrate complex banking workflows — from CRE credit origination to RWA optimisation — by reasoning across structured data, documents, and regulatory constraints simultaneously.

Explore Architecture View Use Cases
1,000+
Synthetic Customers
4
Data Sources
2
Live Use Cases
€2.4bn
Synthetic Loan Book
12
AI Agent Modules
Four Data Sources. One Intelligent Agent.

The SmartAgentBank orchestrator ingests and reasons across four distinct knowledge bases simultaneously — replicating the full cognitive load of an experienced credit officer.

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Database 01 · Structured
Customer & Transaction Ledger
1,000 synthetic bank customers with full transaction histories, credit ratings, exposure data, industry classifications, and relationship profiles.
1,000 Customers Credit Ratings Turnover Data PD / LGD
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Database 02 · Relational
Group & Collateral Mapping
Customer group structures, connected parties, collateral registry with loan-to-value mappings, and credit facility relationships.
48 Groups Collateral Connected Parties Facility Links
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Database 03 · Unstructured
Document Repository
Customer prospect files, financial statements, property appraisals, press releases, legal documents, and credit memos — in PDF, PNG, and XLS format.
PDF Prospectuses Appraisals Financials XLS Press Releases
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Database 04 · Constraints
Policy & Strategy Engine
Bank-wide credit policy, risk appetite statement, business strategy targets, regulatory limits (CBUAE), and sector exposure caps.
Credit Policy Risk Strategy CBUAE Standard Approach Sector Limits
Live Demonstrations
Three High-Impact Use Cases

Each use case walks through a complete AI agent workflow, showing exactly how the SmartAgent orchestrates data retrieval, analysis, and decision support.

Agent Workflow
How the SmartAgent Thinks

Every query triggers a multi-step reasoning chain that mirrors an expert credit officer's analytical process.

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Grounding
Index all unstructured data in hierarchical embedded chunks. Merge with structured data via adaptive RAG. Strict citation enforcement — every claim must cite its source. No own LLM needed — all data within the bank's cloud tenant. E.g. GPT-4 via Azure OpenAI or Claude via AWS Bedrock.
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Knowledge Graph
Stores relationships, not documents. E.g. borrower → parent → exposure → collateral → related credits. This context is injected before the LLM reasons — no inference from unstructured text needed.
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Symbolic Constraints
Rules engines running entirely outside the LLM: e.g. credit policies, business & risk strategy, regulatory framework. Deterministic code — always overrides the model.
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Causal Reasoning
Only after 1, 2 and 3 the LLM comes into play — reasoning over grounded, connected, fenced context. A second independent QA agent has to validate every output before it gets injected into the process.