Integrating RAG into Predictive Intelligence for QR Code Payments

The Paradigm Shift: From Retrieval to Prediction

Most developers view Retrieval-Augmented Generation (RAG) as a tool for answering questions: "What is our refund policy?" or "Show me the transaction history." In high-frequency domains like QR Code Payments, RAG can be integrated into a Predictive Intelligence Workflow. Here, retrieval doesn't just fetch facts; it fetches contextual patterns, historical anomalies, and dynamic risk rules that feed into a predictive model. The LLM then acts not as a chatbot, but as a reasoning engine that synthesizes retrieved data to predict outcomes (e.g., fraud probability, merchant churn, settlement delay) before they happen.

The Core Difference

Feature	Pure QA Workflow	Predictive Intelligence Workflow
Goal	Answer user queries	Predict future states/events
Retrieval Target	Static documents (PDFs, FAQs)	Dynamic vectors (historical patterns, risk scores, real-time rules)
Output	Text response	Structured prediction (Score, Probability, Action)
Latency Sensitivity	Moderate (seconds)	Critical (milliseconds)
State Management	Session-based	Transaction-based + Long-term memory

Real-Time Unique Use Case: "Dynamic Fraud & Settlement Prediction for Merchant QR Codes"

Scenario

Merchant Profile: "Spice Garden," a mid-sized restaurant in Delhi using a static QR code for UPI payments.

Business Problem:

1. Fraud Risk: A sudden spike in small-value transactions from new users at 2 AM could indicate "testing" for carding attacks or money laundering.

2. Settlement Cash Flow: The merchant needs to know if tomorrow’s settlement will be delayed due to high-risk flags triggered by today’s unusual pattern.

3. Customer Churn: If legitimate customers face repeated failed scans due to network issues, they might switch competitors.

Traditional Approach: Rule-based engines flag transactions after they occur. They cannot predict settlement impact or churn risk based on unstructured context (e.g., local festival spikes, weather-related footfall changes).

RAG-Powered Predictive Solution: During each QR scan event, a multi-agent LangGraph system:

1. Retrieves Historical Patterns: Fetches similar transaction clusters from a vector DB (e.g., "Similar spikes occurred during Diwali 2025").

2. Retrieves Dynamic Risk Rules: Fetches real-time regulatory thresholds from a compliance knowledge base.

3. Predicts Outcomes: Uses an LLM to reason over retrieved patterns + real-time data to predict:

   • Fraud Probability Score (0-1)

   • Settlement Delay Risk (High/Medium/Low)

   • Customer Friction Score (Likelihood of drop-off)

4. Triggers Pre-emptive Actions: Auto-holds suspicious funds, sends proactive alerts to the merchant, or optimizes routing for faster settlement.

System Architecture

Step-by-Step Implementation

Prerequisites

pip install langgraph langchain langchain-openai chromadb psycopg2-binary redis pydantic numpy pandas

Step 1: Define State Schema and Data Models

from typing import TypedDict, List, Optional, Dict, Anyfrom pydantic import BaseModel, Field
from datetime import datetime
import uuid

class QRScanEvent(BaseModel):
    """Represents a single QR scan transaction."""
    event_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
    merchant_id: str
    user_id: str
    amount: float
    timestamp: datetime
    location_lat: Optional[float] = None
    location_lon: Optional[float] = None
    device_fingerprint: Optional[str] = None

class RetrievedContext(BaseModel):
    """Context retrieved from Vector DB and Rule Engine."""
    similar_historical_patterns: List[Dict[str, Any]]
    applicable_risk_rules: List[Dict[str, Any]]
    merchant_profile_summary: str
    recent_anomalies: List[str]

class PredictionResult(BaseModel):
    """Output of the predictive agent."""
    fraud_probability: float = Field(ge=0.0, le=1.0)
    settlement_delay_risk: str = Field(description="high, medium, low")
    customer_friction_score: float = Field(ge=0.0, le=1.0)
    recommended_action: str = Field(description="allow, hold, review, alert_merchant")
    reasoning: str

class PaymentState(TypedDict):
    """State passed between agents in LangGraph workflow."""
    event: QRScanEvent
    retrieved_context: Optional[RetrievedContext]
    prediction: Optional[PredictionResult]
    action_taken: Optional[str]
    conversation_history: List[Dict[str, str]]  # For audit/debugging
    error_message: Optional[str]

Step 2: Context Retriever Agent (RAG Core)

This agent retrieves historical patterns and dynamic rules relevant to the current transaction.

from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_core.documents import Document
import psycopg2
import json

class ContextRetrieverAgent:
    def __init__(self, vector_db_path: str = "./qr_payment_db"):
        self.embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
        self.vector_db = Chroma(
            persist_directory=vector_db_path,
            embedding_function=self.embeddings,
            collection_name="payment_patterns"
        )
        self._seed_data()

    def _seed_data(self):
        """Seed with sample historical patterns and risk rules."""
        if self.vector_db._collection.count() == 0:
            docs = [
                Document(
                    page_content="High frequency small transactions (< ₹100) from new users at night often indicate fraud testing.",
                    metadata={"type": "pattern", "risk_level": "high"}
                ),
                Document(
                    page_content="Merchants with > 5% failure rate in last 24 hours face settlement delays.",
                    metadata={"type": "rule", "source": "internal_policy"}
                ),
                Document(
                    page_content="Festival seasons see 3x normal volume; false positives increase if rules are static.",
                    metadata={"type": "pattern", "seasonal": True}
                )
            ]
            self.vector_db.add_documents(docs)

    def retrieve_historical_patterns(self, event: QRScanEvent) -> List[Dict]:
        """Find similar historical transactions."""
        query = f"merchant {event.merchant_id} amount {event.amount} time {event.timestamp.hour}"
        results = self.vector_db.similarity_search(query, k=3)
        return [{"content": doc.page_content, "metadata": doc.metadata} for doc in results]

    def retrieve_risk_rules(self, merchant_id: str) -> List[Dict]:
        """Fetch applicable risk rules."""
        # In production, query a rule engine or SQL DB
        return [
            {"rule_id": "R001", "description": "Flag transactions > ₹50,000 without KYC"},
            {"rule_id": "R002", "description": "Hold funds if merchant failure rate > 5%"}
        ]

    def get_merchant_profile(self, merchant_id: str) -> str:
        """Fetch merchant summary from SQL."""
        # Simulated DB call
        return f"Merchant {merchant_id}: Restaurant, Avg Daily Vol: ₹25,000, Risk Tier: Medium"

    def run(self, state: PaymentState) -> PaymentState:
        """Execute retrieval logic."""
        event = state['event']
        
        patterns = self.retrieve_historical_patterns(event)
        rules = self.retrieve_risk_rules(event.merchant_id)
        profile = self.get_merchant_profile(event.merchant_id)
        
        state['retrieved_context'] = RetrievedContext(
            similar_historical_patterns=patterns,
            applicable_risk_rules=rules,
            merchant_profile_summary=profile,
            recent_anomalies=[]  # Populate from real-time anomaly detection service
        )
        
        return state

Step 3: Predictor Agent (LLM-Based Reasoning)

This agent uses the retrieved context to predict outcomes. It doesn’t just answer questions; it generates structured predictions.

from langchain_openai import ChatOpenAI
import json

class PredictorAgent:
    def __init__(self):
        self.llm = ChatOpenAI(model="gpt-4o", temperature=0.1)  # Low temp for consistency

    def predict_outcomes(self, state: PaymentState) -> PaymentState:
        """Generate predictions based on retrieved context."""
        event = state['event']
        context = state['retrieved_context']
        
        prompt = f"""
        You are a Financial Risk AI. Analyze the current QR payment event and predict risks.
        
        Event Details:
        - Amount: ₹{event.amount}
        - Time: {event.timestamp.strftime('%H:%M')}
        - Merchant: {context.merchant_profile_summary}
        
        Retrieved Context:
        - Historical Patterns: {json.dumps(context.similar_historical_patterns, indent=2)}
        - Risk Rules: {json.dumps(context.applicable_risk_rules, indent=2)}
        
        Tasks:
        1. Estimate Fraud Probability (0-1). Consider time, amount, and patterns.
        2. Assess Settlement Delay Risk (high/medium/low).
        3. Calculate Customer Friction Score (0-1). High score means likely drop-off.
        4. Recommend Action: 'allow', 'hold', 'review', or 'alert_merchant'.
        5. Provide brief reasoning.
        
        Return ONLY valid JSON:
        {{
            "fraud_probability": 0.1,
            "settlement_delay_risk": "low",
            "customer_friction_score": 0.2,
            "recommended_action": "allow",
            "reasoning": "Normal pattern for this merchant."
        }}
        """
        
        try:
            response = self.llm.invoke(prompt)
            prediction_data = json.loads(response.content)
            
            state['prediction'] = PredictionResult(**prediction_data)
            
        except Exception as e:
            state['error_message'] = f"Prediction failed: {str(e)}"
        
        return state

Step 4: Decision Orchestrator Agent

This agent executes the recommended action and updates state.

class DecisionOrchestratorAgent:
    def run(self, state: PaymentState) -> PaymentState:
        """Execute the predicted action."""
        if not state.get('prediction'):
            state['action_taken'] = "error"
            return state
        
        action = state['prediction'].recommended_action
        
        # Simulate action execution
        if action == "hold":
            state['action_taken'] = "funds_held_for_review"
        elif action == "alert_merchant":
            state['action_taken'] = "merchant_alert_sent"
        else:
            state['action_taken'] = "transaction_allowed"
        
        # Log to conversation history for audit
        state['conversation_history'].append({
            "role": "system",
            "content": f"Action: {state['action_taken']} | Reason: {state['prediction'].reasoning}"
        })
        
        return state

Step 5: Assemble the LangGraph Workflow

from langgraph.graph import StateGraph, END
from langgraph.checkpoint.memory import MemorySaver

# Initialize agents
retriever = ContextRetrieverAgent()
predictor = PredictorAgent()
orchestrator = DecisionOrchestratorAgent()

# Define nodesdef retrieve_node(state: PaymentState) -> PaymentState:
    return retriever.run(state)

def predict_node(state: PaymentState) -> PaymentState:
    return predictor.predict_outcomes(state)

def orchestrate_node(state: PaymentState) -> PaymentState:
    return orchestrator.run(state)

# Build graph
workflow = StateGraph(PaymentState)

workflow.add_node("retrieve", retrieve_node)
workflow.add_node("predict", predict_node)
workflow.add_node("orchestrate", orchestrate_node)

workflow.set_entry_point("retrieve")
workflow.add_edge("retrieve", "predict")
workflow.add_edge("predict", "orchestrate")
workflow.add_edge("orchestrate", END)

# Compile with memory
memory = MemorySaver()
app = workflow.compile(checkpointer=memory)

Step 6: Execute the Workflow

def process_qr_payment(event: QRScanEvent) -> Dict:
    """Main entry point for predictive processing."""
    
    initial_state = PaymentState(
        event=event,
        retrieved_context=None,
        prediction=None,
        action_taken=None,
        conversation_history=[],
        error_message=None
    )
    
    thread_id = f"qr_{event.event_id}"
    config = {"configurable": {"thread_id": thread_id}}
    
    result = app.invoke(initial_state, config=config)
    
    return {
        "event_id": event.event_id,
        "prediction": result['prediction'].dict() if result['prediction'] else None,
        "action": result['action_taken']
    }

# Example usageif __name__ == "__main__":
    event = QRScanEvent(
        merchant_id="MERCH_001",
        user_id="USER_123",
        amount=75.0,
        timestamp=datetime.now(),
        location_lat=28.6139,
        location_lon=77.2090
    )
    
    result = process_qr_payment(event)
    print(json.dumps(result, indent=2))

Sample Output

{"event_id": "evt_abc123","prediction": {
    "fraud_probability": 0.12,
    "settlement_delay_risk": "low",
    "customer_friction_score": 0.05,
    "recommended_action": "allow",
    "reasoning": "Transaction amount is within normal range for this merchant. No historical anomalies detected."},"action": "transaction_allowed"}

Why This Is "Predictive Intelligence" and Not Just QA

1. Proactive vs. Reactive: The system predicts future settlement delays and fraud before the transaction completes, allowing pre-emptive holds.

2. Structured Output: The LLM outputs a PredictionResult object, not text. This can be directly consumed by downstream systems (payment gateways, dashboards).

3. Dynamic Context: Retrieval fetches real-time patterns (e.g., "similar spikes happened yesterday"), not static FAQs.

4. Actionable Insights: The output drives automated actions (hold, alert), not just user notifications.

Memory and State Management

Persistent State for Continuous Learning

1. Short-Term: LangGraph MemorySaver tracks the lifecycle of a single transaction.

2. Long-Term: Redis stores merchant-specific risk profiles updated after each prediction.

3. Feedback Loop: If a predicted "fraud" turns out to be legitimate, the system logs this to retrain the retrieval embeddings or fine-tune the LLM prompt.

def update_merchant_profile(merchant_id: str, prediction: PredictionResult):
    """Update long-term memory with new data."""
    redis_client.hset(f"merchant:{merchant_id}", mapping={
        "last_fraud_score": prediction.fraud_probability,
        "last_action": prediction.recommended_action
    })

Compliance and Security

1. Explainability: Every prediction includes a reasoning field for audit trails.

2. Data Privacy: PII (user_id, location) is hashed before storage in vector DB.

3. Guardrails: The DecisionOrchestrator ensures no action violates hard-coded regulatory limits (e.g., never hold funds > 24 hours without manual review).

Conclusion

Integrating RAG into a predictive workflow transforms QR payment processing from a simple pass/fail check into an intelligent, adaptive system that:

✅ Predicts Fraud before it impacts the merchant
✅ Optimizes Settlements by anticipating delays
✅ Reduces Friction by understanding contextual nuances

This approach leverages the reasoning power of LLMs combined with the precision of retrieval to create a competitive advantage in the fast-paced world of digital payments.