FDE FinTech Track — Enterprise & Trainee Hub

Applied AI Agents in FinTech & Forward Deployed Engineering (FDE)

Moving beyond fragile prototype chatbots into auditable, deterministic, stateful AI agent workflows. Designed for cross-functional GCC pairs (Finance Specialists + Software Engineers) shipping compliant banking and finance systems.

15 Hrs

Applied Curriculum

7 Modules

Enterprise Systems

Dual-Model

Producer-Evaluator

GraphRAG

Relational Risk Maps

FDE FinTech Course Modules

Module 1 • Foundations 2.0 Hrs

The Agentic FinTech Revolution

State machines vs chat loops, function calling schemas, and orchestration via n8n & FastAPI.

Module 2 • Context Engineering 2.0 Hrs

Context Engineering & Financial Data

Token budgeting, ERP schema injection, and eliminating hallucinations with deterministic JSON contracts.

Module 3 • Relational Risk 2.5 Hrs

Relational Risk & GraphRAG

Why vector embeddings fail on financial graphs; multi-hop entity resolution for shell company fraud rings.

Module 4 • Financial Crime 2.5 Hrs

AML, SAR & Financial Crime Automation

Suspicious Activity Report (SAR) evidence dossiers, multi-agent investigations, and human sign-off gates.

Module 5 • Security 2.5 Hrs

Adversarial Document Security

Indirect prompt injections inside invoice PDFs/emails, vendor bank account poisoning, and perimeter defense.

Module 6 • Governance 2.0 Hrs

Dual-Model Guardrail Evaluators

Producer-Evaluator architecture for real-time hallucination scoring and automated policy blocks.

Module 7 • Capstone 1.5 Hrs

Enterprise Blueprint & Capstone Defense

Live production roadmaps, RBI/SOX compliance review, and cross-functional 100-pt capstone evaluation.

MODULE 1 • FOUNDATIONS

The Agentic FinTech Revolution

2.0 Hours

Shifts the engineering and finance mindset from single-turn chat interactions to stateful agent execution loops with explicit function-calling contracts and transaction rollback capabilities.

Core Engineering Deliverables:

Orchestrate multi-step finance agent state machines with defined entry/exit states.
Implement Pydantic V2 schemas for deterministic API tool-call routing.
Construct webhook listener pipelines for automated invoice and trade event triggers.

agentic_state_machine.py

from pydantic import BaseModel, Field

class InvoiceExtractContract(BaseModel):
    vendor_tax_id: str = Field(..., description="GSTIN or Federal EIN")
    invoice_amount: float = Field(..., gt=0)
    bank_account: str = Field(..., regex=r"^[A-Z0-9]{8,24}$")
    requires_approval: bool = Field(default=False)

15-Hour FDE Course Schedule & Gantt Timeline

Divided across 2 Intensive Days (or 4 Evening Cohort Sessions) with paired Finance + Tech execution.

Session & Module	Duration	Topic & Technical Focus	Hands-On GCC Lab Milestone
M1: Agentic FinTech	2.0 Hrs	State Machines, Agentic Loops & Function Schemas	Building n8n / FastAPI webhook listener for trade events.
M2: Context Engineering	2.0 Hrs	Token Budgets & Core ERP API Ingestion	Structured GL prompt injection with Pydantic JSON contracts.
M3: GraphRAG	2.5 Hrs	Multi-Hop Entity Graphs & Relational Risk	Extracting 3-hop shell company networks using Neo4j / NetworkX.
M4: AML & SAR Automation	2.5 Hrs	Transaction Dossiers & Human Sign-off Gates	Automated SAR evidence compilation with human approval gate.
M5: Adversarial Invoices	2.5 Hrs	Indirect Prompt Injection & PDF Poisoning	Simulated malicious PDF attack & ERP master cross-validation defense.
M6: Dual-Model Evaluators	2.0 Hrs	Producer-Evaluator Governance Pattern	Deploying real-time policy evaluation filter before ERP commit.
M7: Capstone Defense	1.5 Hrs	1-Page Enterprise Blueprint & Live Run	Live defense before Course Director panel judged on 100-pt rubric.

⚡ Live FDE Microservice API Runner

Test the live production FastAPI template (fde_microservice_starter.py) with client task dispatching and semantic caching.

Client ID

Workflow Name

Execution Priority

Endpoint Execution Response

// Click "Execute FDE Task Endpoint" to simulate FastAPI request/response with semantic caching...

labs/fde_microservice_starter.py

@app.post("/api/v1/execute", response_model=EnterpriseTaskResponse)
def execute_task(request: EnterpriseTaskRequest):
    cache_key = f"{request.client_id}:{request.workflow_name}"
    if cache_key in SEMANTIC_CACHE:
        return EnterpriseTaskResponse(cached=True, status="SUCCESS", ...)
    # Execute deterministic validation & agent evaluation
    return EnterpriseTaskResponse(cached=False, status="SUCCESS", ...)

🎯 100-Point FDE Capstone Rubric

Assessed by the Course Director panel on live working systems. Minimum pass: 60 pts. Security & Guardrails minimum: 15/25.

20 Pts

1. Business Value & Problem Fit

Clear As-Is vs To-Be process mapping. Solves high-friction finance bottleneck with realistic ROI.

20 Pts

2. Context & Data Architecture

ERP/Core banking schema integration, token budgeting, and strict Pydantic JSON contracts.

25 Pts

3. Security, Guardrails & Defense

Multi-layer defense against prompt injections in invoices, PII masking, and Dual-Model Evaluator.

20 Pts

4. Governance & Human-in-the-Loop

Explicit escalation triggers, named human approval gates for high-value transactions, audit logging.

15 Pts

5. Cross-Functional Defense

Finance defends accounting logic & compliance; Tech defends API pipeline & reliability.

State Machine vs Chat Loop Architecture

Why enterprise FinTech agents require deterministic finite state machines instead of open-ended conversational prompts:

Bounded States: Document Intake → Schema Validation → Risk Check → Review Gate → ERP Commit.
Rollback Guarantees: Any failed validator reverts ledger state without partial writes.
Audit Snapshotting: Every transition logs model parameters, input tokens, and human approver IDs.

Dual-Model Producer-Evaluator Pattern

A self-grading LLM suffers from confirmation bias. The Producer-Evaluator pattern isolates generation from safety auditing:

[Raw Invoice] ──> [Producer LLM (Extraction)]
                         │
                         ▼ (Draft JSON)
                [Evaluator LLM (Policy Guardrail)]
                ├── Checks Master Vendor Table
                ├── Verifies Bank Account Match
                └── Flags Invisible Injections
                         │
                 ┌───────┴───────┐
                 ▼               ▼
           [PASS / Commit]  [FAIL / Alert Audit]