SMC AI RM Monitoring and Learning

Monitors campaign outcomes and continuously improves AI models while maintaining compliance

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Workflow Information

ID: smc_ai_rm_monitoring_learning

Namespace: smc_wealth_management

Version: 1.0

Created: 2025-07-17

Updated: 2025-07-17

Tasks: 8

Quick Actions

Manage Secrets

Inputs

Name	Type	Required	Default
`monitoring_period`	string	Optional	`last_7_days`
`customer_ids`	string	Optional	`all`
`campaign_types`	string	Optional	`all`
`enable_model_update`	boolean	Optional	`True`

Outputs

Name	Type	Source
`anomalies`	object	Detected anomalies
`model_updates`	object	Applied model updates
`model_evaluation`	object	AI model performance metrics
`campaign_outcomes`	object	Campaign execution outcomes
`learning_insights`	object	Extracted learning insights
`monitoring_report`	object	Comprehensive monitoring report
`performance_analysis`	object	Detailed performance analysis

Tasks

collect_campaign_outcomes

script

Gather results from executed campaigns

Python Script:

import json
import time
import random
from datetime import datetime, timedelta

# Simulate data collection
time.sleep(1)

monitoring_period = "${monitoring_period}"
campaign_types = "${campaign_types}"

# Define period
period_days = {
    "last_24_hours": 1,
    "last_7_days": 7,
    "last_30_days": 30
}

days = period_days.get(monitoring_period, 7)

# Simulate campaign outcomes data
campaigns_executed = []

# Generate sample campaign outcomes
campaign_templates = {
    "retention": {
        "success_rate": 0.65,
        "response_rate": 0.75,
        "avg_response_time": 4.2
    },
    "engagement": {
        "success_rate": 0.72,
        "response_rate": 0.55,
        "avg_response_time": 8.5
    },
    "upsell": {
        "success_rate": 0.42,
        "response_rate": 0.35,
        "avg_response_time": 48.0
    }
}

# Generate campaigns for the period
num_campaigns = random.randint(20, 100)

for i in range(num_campaigns):
    campaign_type = random.choice(["retention", "engagement", "upsell"])
    template = campaign_templates[campaign_type]
    
    # Simulate outcome
    responded = random.random() < template["response_rate"]
    successful = responded and (random.random() < template["success_rate"])
    
    campaign = {
        "campaign_id": f"CAMP_{random.randint(10000, 99999)}",
        "customer_id": f"CUST{random.randint(1, 999):03d}",
        "campaign_type": campaign_type,
        "execution_date": (datetime.now() - timedelta(days=random.randint(0, days))).strftime("%Y-%m-%d"),
        "customer_segment": random.choice(["PLATINUM", "GOLD", "SILVER"]),
        "risk_level": random.choice(["HIGH", "MEDIUM", "LOW"]),
        "response_received": responded,
        "outcome": "SUCCESS" if successful else "FAILED" if responded else "NO_RESPONSE",
        "response_time_hours": random.uniform(1, template["avg_response_time"]) if responded else None,
        "revenue_impact": random.randint(5000, 50000) if successful else 0,
        "compliance_maintained": True
    }
    
    campaigns_executed.append(campaign)

# Calculate summary statistics
total_campaigns = len(campaigns_executed)
successful_campaigns = sum(1 for c in campaigns_executed if c["outcome"] == "SUCCESS")
response_rate = sum(1 for c in campaigns_executed if c["response_received"]) / total_campaigns

result = {
    "collection_completed": True,
    "monitoring_period": monitoring_period,
    "period_days": days,
    "campaigns_analyzed": total_campaigns,
    "campaigns_data": campaigns_executed,
    "summary": {
        "total_campaigns": total_campaigns,
        "successful_campaigns": successful_campaigns,
        "success_rate": round(successful_campaigns / total_campaigns, 2),
        "response_rate": round(response_rate, 2),
        "total_revenue_impact": sum(c["revenue_impact"] for c in campaigns_executed)
    },
    "timestamp": datetime.now().isoformat()
}

print(f"__OUTPUTS__ {json.dumps(result)}")

analyze_performance

script

Deep analysis of campaign performance by segment

Python Script:

import json
import time
from datetime import datetime
from collections import defaultdict

# Simulate analysis
time.sleep(1)

campaign_data = ${collect_campaign_outcomes.campaigns_data}

# Analyze by campaign type
type_analysis = defaultdict(lambda: {
    "total": 0,
    "successful": 0,
    "responded": 0,
    "revenue": 0
})

# Analyze by customer segment
segment_analysis = defaultdict(lambda: {
    "total": 0,
    "successful": 0,
    "responded": 0,
    "revenue": 0
})

# Analyze by risk level
risk_analysis = defaultdict(lambda: {
    "total": 0,
    "successful": 0,
    "responded": 0,
    "revenue": 0
})

# Process each campaign
for campaign in campaign_data:
    # By type
    type_stats = type_analysis[campaign["campaign_type"]]
    type_stats["total"] += 1
    if campaign["outcome"] == "SUCCESS":
        type_stats["successful"] += 1
    if campaign["response_received"]:
        type_stats["responded"] += 1
    type_stats["revenue"] += campaign["revenue_impact"]
    
    # By segment
    segment_stats = segment_analysis[campaign["customer_segment"]]
    segment_stats["total"] += 1
    if campaign["outcome"] == "SUCCESS":
        segment_stats["successful"] += 1
    if campaign["response_received"]:
        segment_stats["responded"] += 1
    segment_stats["revenue"] += campaign["revenue_impact"]
    
    # By risk
    risk_stats = risk_analysis[campaign["risk_level"]]
    risk_stats["total"] += 1
    if campaign["outcome"] == "SUCCESS":
        risk_stats["successful"] += 1
    if campaign["response_received"]:
        risk_stats["responded"] += 1
    risk_stats["revenue"] += campaign["revenue_impact"]

# Calculate rates
def calculate_rates(stats):
    for key, data in stats.items():
        if data["total"] > 0:
            data["success_rate"] = round(data["successful"] / data["total"], 2)
            data["response_rate"] = round(data["responded"] / data["total"], 2)
            data["avg_revenue"] = round(data["revenue"] / data["total"], 2)
        else:
            data["success_rate"] = 0
            data["response_rate"] = 0
            data["avg_revenue"] = 0
    return dict(stats)

# Key insights
insights = []

# Find best performing campaign type
best_type = max(type_analysis.items(), key=lambda x: x[1]["success_rate"])
insights.append(f"{best_type[0].title()} campaigns show highest success rate at {best_type[1]['success_rate']*100}%")

# Find best performing segment
best_segment = max(segment_analysis.items(), key=lambda x: x[1]["revenue"])
insights.append(f"{best_segment[0]} segment generates highest revenue: ₹{best_segment[1]['revenue']:,}")

# Risk level effectiveness
high_risk_success = risk_analysis["HIGH"]["success_rate"] if "HIGH" in risk_analysis else 0
if high_risk_success > 0.5:
    insights.append(f"Retention strategies for high-risk customers showing {high_risk_success*100}% success rate")

result = {
    "analysis_completed": True,
    "performance_by_type": calculate_rates(type_analysis),
    "performance_by_segment": calculate_rates(segment_analysis),
    "performance_by_risk": calculate_rates(risk_analysis),
    "key_insights": insights,
    "recommendations": [
        "Focus on " + best_type[0] + " campaigns for better ROI",
        "Prioritize " + best_segment[0] + " segment customers",
        "Adjust risk thresholds based on outcome patterns"
    ],
    "timestamp": datetime.now().isoformat()
}

print(f"__OUTPUTS__ {json.dumps(result)}")

detect_anomalies

script

Identify unusual patterns in campaign outcomes

Python Script:

import json
import time
import random
from datetime import datetime

# Simulate anomaly detection
time.sleep(1)

performance_data = ${analyze_performance}

anomalies = []

# Check for unusual success rates
for campaign_type, stats in performance_data["performance_by_type"].items():
    if stats["success_rate"] < 0.3:
        anomalies.append({
            "type": "low_success_rate",
            "category": "campaign_type",
            "identifier": campaign_type,
            "metric": "success_rate",
            "value": stats["success_rate"],
            "severity": "HIGH",
            "description": f"{campaign_type} campaigns showing unusually low success rate"
        })

# Check for segment anomalies
for segment, stats in performance_data["performance_by_segment"].items():
    if stats["response_rate"] < 0.2:
        anomalies.append({
            "type": "low_response_rate",
            "category": "customer_segment",
            "identifier": segment,
            "metric": "response_rate",
            "value": stats["response_rate"],
            "severity": "MEDIUM",
            "description": f"{segment} segment showing poor response rates"
        })

# Check for risk level anomalies
for risk_level, stats in performance_data["performance_by_risk"].items():
    if risk_level == "LOW" and stats["success_rate"] < 0.4:
        anomalies.append({
            "type": "unexpected_pattern",
            "category": "risk_level",
            "identifier": risk_level,
            "metric": "success_rate",
            "value": stats["success_rate"],
            "severity": "MEDIUM",
            "description": "Low-risk customers showing poor campaign outcomes"
        })

# Generate corrective actions
corrective_actions = []
for anomaly in anomalies:
    if anomaly["type"] == "low_success_rate":
        corrective_actions.append({
            "anomaly_id": anomaly["identifier"],
            "action": "Review and revise campaign content",
            "priority": "HIGH"
        })
    elif anomaly["type"] == "low_response_rate":
        corrective_actions.append({
            "anomaly_id": anomaly["identifier"],
            "action": "Adjust communication timing and channels",
            "priority": "MEDIUM"
        })

result = {
    "anomaly_detection_completed": True,
    "anomalies_found": len(anomalies),
    "anomalies": anomalies,
    "corrective_actions": corrective_actions,
    "alert_required": len([a for a in anomalies if a["severity"] == "HIGH"]) > 0,
    "timestamp": datetime.now().isoformat()
}

print(f"__OUTPUTS__ {json.dumps(result)}")

evaluate_model_performance

script

Assess AI model prediction accuracy

Python Script:

import json
import time
import random
from datetime import datetime

# Simulate model evaluation
time.sleep(1)

campaign_data = ${collect_campaign_outcomes.campaigns_data}

# Simulate model predictions vs actual outcomes
model_metrics = {
    "risk_prediction": {
        "total_predictions": len(campaign_data),
        "correct_predictions": 0,
        "precision": 0,
        "recall": 0,
        "f1_score": 0
    },
    "campaign_selection": {
        "total_selections": len(campaign_data),
        "optimal_selections": 0,
        "selection_accuracy": 0
    },
    "response_prediction": {
        "total_predictions": len(campaign_data),
        "correct_predictions": 0,
        "mae": 0  # Mean Absolute Error for response time
    }
}

# Calculate risk prediction accuracy
risk_predictions_correct = 0
for campaign in campaign_data:
    # Simulate that HIGH risk correctly predicted low success
    if campaign["risk_level"] == "HIGH" and campaign["outcome"] != "SUCCESS":
        risk_predictions_correct += 1
    elif campaign["risk_level"] == "LOW" and campaign["outcome"] == "SUCCESS":
        risk_predictions_correct += 1
    elif campaign["risk_level"] == "MEDIUM":
        # Medium risk is correct 70% of the time
        if random.random() < 0.7:
            risk_predictions_correct += 1

model_metrics["risk_prediction"]["correct_predictions"] = risk_predictions_correct
model_metrics["risk_prediction"]["accuracy"] = round(risk_predictions_correct / len(campaign_data), 2)

# Calculate campaign selection accuracy
optimal_selections = sum(1 for c in campaign_data if c["outcome"] == "SUCCESS")
model_metrics["campaign_selection"]["optimal_selections"] = optimal_selections
model_metrics["campaign_selection"]["selection_accuracy"] = round(optimal_selections / len(campaign_data), 2)

# Response prediction accuracy
response_predictions_correct = sum(1 for c in campaign_data if c["response_received"])
model_metrics["response_prediction"]["correct_predictions"] = response_predictions_correct
model_metrics["response_prediction"]["accuracy"] = round(response_predictions_correct / len(campaign_data), 2)

# Model confidence scores
confidence_scores = {
    "overall_confidence": round(random.uniform(0.82, 0.91), 2),
    "risk_assessment_confidence": round(random.uniform(0.85, 0.93), 2),
    "campaign_selection_confidence": round(random.uniform(0.78, 0.88), 2),
    "timing_prediction_confidence": round(random.uniform(0.73, 0.85), 2)
}

# Areas for improvement
improvement_areas = []
if model_metrics["risk_prediction"]["accuracy"] < 0.8:
    improvement_areas.append("Enhance risk prediction algorithms")
if model_metrics["campaign_selection"]["selection_accuracy"] < 0.6:
    improvement_areas.append("Improve campaign selection logic")
if confidence_scores["timing_prediction_confidence"] < 0.8:
    improvement_areas.append("Better timing prediction models needed")

result = {
    "evaluation_completed": True,
    "model_metrics": model_metrics,
    "confidence_scores": confidence_scores,
    "improvement_areas": improvement_areas,
    "model_version": "2.0",
    "evaluation_timestamp": datetime.now().isoformat()
}

print(f"__OUTPUTS__ {json.dumps(result)}")

generate_learning_insights

script

Extract actionable insights for model improvement

Python Script:

import json
import time
from datetime import datetime

# Simulate insight generation
time.sleep(1)

performance_analysis = ${analyze_performance}
anomalies = ${detect_anomalies}
model_evaluation = ${evaluate_model_performance}

# Extract patterns from successful campaigns
successful_patterns = []

# From performance analysis
for campaign_type, stats in performance_analysis["performance_by_type"].items():
    if stats["success_rate"] > 0.6:
        successful_patterns.append({
            "pattern": f"{campaign_type}_high_success",
            "description": f"{campaign_type} campaigns show {stats['success_rate']*100}% success rate",
            "factors": ["campaign_type", "content_strategy", "timing"],
            "confidence": stats["success_rate"]
        })

# From segment analysis
for segment, stats in performance_analysis["performance_by_segment"].items():
    if stats["avg_revenue"] > 20000:
        successful_patterns.append({
            "pattern": f"{segment}_high_value",
            "description": f"{segment} segment generates ₹{stats['avg_revenue']} average revenue",
            "factors": ["customer_segment", "product_affinity", "engagement_level"],
            "confidence": 0.85
        })

# Failed pattern analysis
failure_patterns = []
for anomaly in anomalies["anomalies"]:
    failure_patterns.append({
        "pattern": anomaly["type"],
        "description": anomaly["description"],
        "impact": anomaly["severity"],
        "recommended_action": "Adjust model parameters for " + anomaly["identifier"]
    })

# Model adjustment recommendations
model_adjustments = []

if model_evaluation["model_metrics"]["risk_prediction"]["accuracy"] < 0.85:
    model_adjustments.append({
        "component": "risk_prediction_model",
        "adjustment": "Add more behavioral features",
        "expected_improvement": "+5-8% accuracy"
    })

if len(failure_patterns) > 2:
    model_adjustments.append({
        "component": "campaign_selection_algorithm",
        "adjustment": "Implement adaptive thresholds",
        "expected_improvement": "+10% success rate"
    })

# Feature importance updates
feature_importance_updates = {
    "login_frequency": 0.25,  # Increased from 0.20
    "transaction_recency": 0.30,  # Increased from 0.25
    "portfolio_concentration": 0.20,  # Decreased from 0.25
    "fund_movement": 0.15,  # Same
    "product_usage": 0.10  # Decreased from 0.15
}

# Learning summary
learning_summary = {
    "total_patterns_identified": len(successful_patterns) + len(failure_patterns),
    "success_patterns": len(successful_patterns),
    "failure_patterns": len(failure_patterns),
    "model_adjustments_recommended": len(model_adjustments),
    "confidence_in_insights": 0.88
}

result = {
    "insights_generated": True,
    "successful_patterns": successful_patterns,
    "failure_patterns": failure_patterns,
    "model_adjustments": model_adjustments,
    "feature_importance_updates": feature_importance_updates,
    "learning_summary": learning_summary,
    "timestamp": datetime.now().isoformat()
}

print(f"__OUTPUTS__ {json.dumps(result)}")

update_ai_models

script

Apply learning insights to improve AI models

Python Script:

import json
import time
import random
from datetime import datetime, timedelta

# Check if model update is enabled
enable_update = ${enable_model_update}

if not enable_update:
    result = {
        "update_skipped": True,
        "reason": "Model update disabled",
        "timestamp": datetime.now().isoformat()
    }
else:
    # Simulate model update
    time.sleep(2)
    
    learning_insights = ${generate_learning_insights}
    
    # Models to update
    models_updated = []
    
    # Update risk prediction model
    risk_model_update = {
        "model_name": "risk_prediction_model",
        "version_before": "2.0",
        "version_after": "2.1",
        "changes_applied": [
            "Updated feature weights based on importance",
            "Added behavioral pattern recognition",
            "Adjusted risk thresholds"
        ],
        "expected_improvement": "+6% accuracy",
        "validation_score": 0.89,
        "deployment_status": "STAGED"
    }
    models_updated.append(risk_model_update)
    
    # Update campaign selection model
    campaign_model_update = {
        "model_name": "campaign_selection_model",
        "version_before": "1.8",
        "version_after": "1.9",
        "changes_applied": [
            "Incorporated successful pattern learnings",
            "Added segment-specific strategies",
            "Improved timing predictions"
        ],
        "expected_improvement": "+8% success rate",
        "validation_score": 0.86,
        "deployment_status": "STAGED"
    }
    models_updated.append(campaign_model_update)
    
    # Update response prediction model
    response_model_update = {
        "model_name": "response_prediction_model",
        "version_before": "1.5",
        "version_after": "1.6",
        "changes_applied": [
            "Enhanced customer engagement scoring",
            "Added communication preference learning",
            "Improved response time predictions"
        ],
        "expected_improvement": "+12% response rate",
        "validation_score": 0.84,
        "deployment_status": "STAGED"
    }
    models_updated.append(response_model_update)
    
    # A/B testing configuration
    ab_testing_config = {
        "enabled": True,
        "test_percentage": 20,  # 20% on new models
        "control_percentage": 80,  # 80% on current models
        "duration_days": 14,
        "success_metrics": [
            "campaign_success_rate",
            "customer_response_rate",
            "revenue_impact"
        ]
    }
    
    # Rollback plan
    rollback_plan = {
        "automatic_rollback_enabled": True,
        "rollback_triggers": [
            "success_rate_drop > 10%",
            "error_rate > 5%",
            "compliance_violation"
        ],
        "monitoring_period": "48_hours",
        "approval_required": False
    }
    
    result = {
        "update_completed": True,
        "models_updated": models_updated,
        "total_models_updated": len(models_updated),
        "ab_testing_config": ab_testing_config,
        "rollback_plan": rollback_plan,
        "deployment_schedule": (datetime.now() + timedelta(days=1)).isoformat(),
        "update_confidence": 0.91,
        "timestamp": datetime.now().isoformat()
    }

print(f"__OUTPUTS__ {json.dumps(result)}")

generate_monitoring_report

script

Create comprehensive monitoring and learning report

Python Script:

import json
import time
from datetime import datetime

# Simulate report generation
time.sleep(1)

# Gather all results
campaign_outcomes = ${collect_campaign_outcomes.summary}
performance_analysis = ${analyze_performance}
anomalies = ${detect_anomalies}
model_evaluation = ${evaluate_model_performance}
learning_insights = ${generate_learning_insights}
model_updates = ${update_ai_models}

# Executive summary
executive_summary = {
    "monitoring_period": "${monitoring_period}",
    "campaigns_analyzed": campaign_outcomes["total_campaigns"],
    "overall_success_rate": f"{campaign_outcomes['success_rate']*100}%",
    "total_revenue_impact": f"₹{campaign_outcomes['total_revenue_impact']:,}",
    "anomalies_detected": anomalies["anomalies_found"],
    "model_accuracy": f"{model_evaluation['model_metrics']['risk_prediction']['accuracy']*100}%",
    "models_updated": model_updates.get("total_models_updated", 0)
}

# Key findings
key_findings = [
    f"Campaign success rate: {campaign_outcomes['success_rate']*100}%",
    f"Customer response rate: {campaign_outcomes['response_rate']*100}%",
    f"Revenue generated: ₹{campaign_outcomes['total_revenue_impact']:,}",
    f"Model prediction accuracy: {model_evaluation['model_metrics']['risk_prediction']['accuracy']*100}%"
]

# Add performance insights
key_findings.extend(performance_analysis["key_insights"])

# Recommendations
recommendations = {
    "immediate_actions": [],
    "short_term": [],
    "long_term": []
}

# Immediate actions for anomalies
if anomalies["alert_required"]:
    recommendations["immediate_actions"].append("Address high-severity anomalies in campaign performance")

# Short-term recommendations
recommendations["short_term"].extend(performance_analysis["recommendations"])

# Long-term recommendations
if learning_insights["model_adjustments"]:
    recommendations["long_term"].append("Implement recommended model adjustments")

# Compliance summary
compliance_summary = {
    "compliance_maintained": True,
    "violations_detected": 0,
    "audit_trail_complete": True,
    "data_privacy_maintained": True,
    "sebi_guidelines_followed": True
}

# Next steps
next_steps = [
    {
        "action": "Deploy updated models to A/B testing",
        "timeline": "Within 24 hours",
        "owner": "AI Team"
    },
    {
        "action": "Monitor A/B test results",
        "timeline": "Next 14 days",
        "owner": "Analytics Team"
    },
    {
        "action": "Review anomaly corrections",
        "timeline": "Within 48 hours",
        "owner": "Business Team"
    }
]

# Full report
report = {
    "report_id": f"MON_REPORT_{datetime.now().strftime('%Y%m%d%H%M%S')}",
    "report_type": "AI_RM_MONITORING_LEARNING",
    "generated_at": datetime.now().isoformat(),
    "executive_summary": executive_summary,
    "key_findings": key_findings,
    "detailed_analysis": {
        "campaign_performance": performance_analysis,
        "anomaly_detection": anomalies,
        "model_evaluation": model_evaluation,
        "learning_insights": learning_insights
    },
    "recommendations": recommendations,
    "compliance_summary": compliance_summary,
    "model_updates": model_updates,
    "next_steps": next_steps,
    "report_confidence": 0.92
}

result = {
    "report_generated": True,
    "report": report,
    "distribution_list": ["ai_team", "business_team", "compliance_team", "management"],
    "timestamp": datetime.now().isoformat()
}

print(f"__OUTPUTS__ {json.dumps(result)}")

store_learning_data

script

Persist learning data for future reference

YAML Source

id: smc_ai_rm_monitoring_learning
name: SMC AI RM Monitoring and Learning
retry:
  retryOn:
  - TEMPORARY_FAILURE
  - TIMEOUT
  maxDelay: 30s
  maxAttempts: 2
  initialDelay: 5s
  backoffMultiplier: 2.0
tasks:
- id: collect_campaign_outcomes
  name: Collect Campaign Outcomes
  type: script
  script: "import json\nimport time\nimport random\nfrom datetime import datetime,\
    \ timedelta\n\n# Simulate data collection\ntime.sleep(1)\n\nmonitoring_period\
    \ = \"${monitoring_period}\"\ncampaign_types = \"${campaign_types}\"\n\n# Define\
    \ period\nperiod_days = {\n    \"last_24_hours\": 1,\n    \"last_7_days\": 7,\n\
    \    \"last_30_days\": 30\n}\n\ndays = period_days.get(monitoring_period, 7)\n\
    \n# Simulate campaign outcomes data\ncampaigns_executed = []\n\n# Generate sample\
    \ campaign outcomes\ncampaign_templates = {\n    \"retention\": {\n        \"\
    success_rate\": 0.65,\n        \"response_rate\": 0.75,\n        \"avg_response_time\"\
    : 4.2\n    },\n    \"engagement\": {\n        \"success_rate\": 0.72,\n      \
    \  \"response_rate\": 0.55,\n        \"avg_response_time\": 8.5\n    },\n    \"\
    upsell\": {\n        \"success_rate\": 0.42,\n        \"response_rate\": 0.35,\n\
    \        \"avg_response_time\": 48.0\n    }\n}\n\n# Generate campaigns for the\
    \ period\nnum_campaigns = random.randint(20, 100)\n\nfor i in range(num_campaigns):\n\
    \    campaign_type = random.choice([\"retention\", \"engagement\", \"upsell\"\
    ])\n    template = campaign_templates[campaign_type]\n    \n    # Simulate outcome\n\
    \    responded = random.random() < template[\"response_rate\"]\n    successful\
    \ = responded and (random.random() < template[\"success_rate\"])\n    \n    campaign\
    \ = {\n        \"campaign_id\": f\"CAMP_{random.randint(10000, 99999)}\",\n  \
    \      \"customer_id\": f\"CUST{random.randint(1, 999):03d}\",\n        \"campaign_type\"\
    : campaign_type,\n        \"execution_date\": (datetime.now() - timedelta(days=random.randint(0,\
    \ days))).strftime(\"%Y-%m-%d\"),\n        \"customer_segment\": random.choice([\"\
    PLATINUM\", \"GOLD\", \"SILVER\"]),\n        \"risk_level\": random.choice([\"\
    HIGH\", \"MEDIUM\", \"LOW\"]),\n        \"response_received\": responded,\n  \
    \      \"outcome\": \"SUCCESS\" if successful else \"FAILED\" if responded else\
    \ \"NO_RESPONSE\",\n        \"response_time_hours\": random.uniform(1, template[\"\
    avg_response_time\"]) if responded else None,\n        \"revenue_impact\": random.randint(5000,\
    \ 50000) if successful else 0,\n        \"compliance_maintained\": True\n    }\n\
    \    \n    campaigns_executed.append(campaign)\n\n# Calculate summary statistics\n\
    total_campaigns = len(campaigns_executed)\nsuccessful_campaigns = sum(1 for c\
    \ in campaigns_executed if c[\"outcome\"] == \"SUCCESS\")\nresponse_rate = sum(1\
    \ for c in campaigns_executed if c[\"response_received\"]) / total_campaigns\n\
    \nresult = {\n    \"collection_completed\": True,\n    \"monitoring_period\":\
    \ monitoring_period,\n    \"period_days\": days,\n    \"campaigns_analyzed\":\
    \ total_campaigns,\n    \"campaigns_data\": campaigns_executed,\n    \"summary\"\
    : {\n        \"total_campaigns\": total_campaigns,\n        \"successful_campaigns\"\
    : successful_campaigns,\n        \"success_rate\": round(successful_campaigns\
    \ / total_campaigns, 2),\n        \"response_rate\": round(response_rate, 2),\n\
    \        \"total_revenue_impact\": sum(c[\"revenue_impact\"] for c in campaigns_executed)\n\
    \    },\n    \"timestamp\": datetime.now().isoformat()\n}\n\nprint(f\"__OUTPUTS__\
    \ {json.dumps(result)}\")\n"
  description: Gather results from executed campaigns
  timeout_seconds: 180
- id: analyze_performance
  name: Analyze Campaign Performance
  type: script
  script: "import json\nimport time\nfrom datetime import datetime\nfrom collections\
    \ import defaultdict\n\n# Simulate analysis\ntime.sleep(1)\n\ncampaign_data =\
    \ ${collect_campaign_outcomes.campaigns_data}\n\n# Analyze by campaign type\n\
    type_analysis = defaultdict(lambda: {\n    \"total\": 0,\n    \"successful\":\
    \ 0,\n    \"responded\": 0,\n    \"revenue\": 0\n})\n\n# Analyze by customer segment\n\
    segment_analysis = defaultdict(lambda: {\n    \"total\": 0,\n    \"successful\"\
    : 0,\n    \"responded\": 0,\n    \"revenue\": 0\n})\n\n# Analyze by risk level\n\
    risk_analysis = defaultdict(lambda: {\n    \"total\": 0,\n    \"successful\":\
    \ 0,\n    \"responded\": 0,\n    \"revenue\": 0\n})\n\n# Process each campaign\n\
    for campaign in campaign_data:\n    # By type\n    type_stats = type_analysis[campaign[\"\
    campaign_type\"]]\n    type_stats[\"total\"] += 1\n    if campaign[\"outcome\"\
    ] == \"SUCCESS\":\n        type_stats[\"successful\"] += 1\n    if campaign[\"\
    response_received\"]:\n        type_stats[\"responded\"] += 1\n    type_stats[\"\
    revenue\"] += campaign[\"revenue_impact\"]\n    \n    # By segment\n    segment_stats\
    \ = segment_analysis[campaign[\"customer_segment\"]]\n    segment_stats[\"total\"\
    ] += 1\n    if campaign[\"outcome\"] == \"SUCCESS\":\n        segment_stats[\"\
    successful\"] += 1\n    if campaign[\"response_received\"]:\n        segment_stats[\"\
    responded\"] += 1\n    segment_stats[\"revenue\"] += campaign[\"revenue_impact\"\
    ]\n    \n    # By risk\n    risk_stats = risk_analysis[campaign[\"risk_level\"\
    ]]\n    risk_stats[\"total\"] += 1\n    if campaign[\"outcome\"] == \"SUCCESS\"\
    :\n        risk_stats[\"successful\"] += 1\n    if campaign[\"response_received\"\
    ]:\n        risk_stats[\"responded\"] += 1\n    risk_stats[\"revenue\"] += campaign[\"\
    revenue_impact\"]\n\n# Calculate rates\ndef calculate_rates(stats):\n    for key,\
    \ data in stats.items():\n        if data[\"total\"] > 0:\n            data[\"\
    success_rate\"] = round(data[\"successful\"] / data[\"total\"], 2)\n         \
    \   data[\"response_rate\"] = round(data[\"responded\"] / data[\"total\"], 2)\n\
    \            data[\"avg_revenue\"] = round(data[\"revenue\"] / data[\"total\"\
    ], 2)\n        else:\n            data[\"success_rate\"] = 0\n            data[\"\
    response_rate\"] = 0\n            data[\"avg_revenue\"] = 0\n    return dict(stats)\n\
    \n# Key insights\ninsights = []\n\n# Find best performing campaign type\nbest_type\
    \ = max(type_analysis.items(), key=lambda x: x[1][\"success_rate\"])\ninsights.append(f\"\
    {best_type[0].title()} campaigns show highest success rate at {best_type[1]['success_rate']*100}%\"\
    )\n\n# Find best performing segment\nbest_segment = max(segment_analysis.items(),\
    \ key=lambda x: x[1][\"revenue\"])\ninsights.append(f\"{best_segment[0]} segment\
    \ generates highest revenue: \u20B9{best_segment[1]['revenue']:,}\")\n\n# Risk\
    \ level effectiveness\nhigh_risk_success = risk_analysis[\"HIGH\"][\"success_rate\"\
    ] if \"HIGH\" in risk_analysis else 0\nif high_risk_success > 0.5:\n    insights.append(f\"\
    Retention strategies for high-risk customers showing {high_risk_success*100}%\
    \ success rate\")\n\nresult = {\n    \"analysis_completed\": True,\n    \"performance_by_type\"\
    : calculate_rates(type_analysis),\n    \"performance_by_segment\": calculate_rates(segment_analysis),\n\
    \    \"performance_by_risk\": calculate_rates(risk_analysis),\n    \"key_insights\"\
    : insights,\n    \"recommendations\": [\n        \"Focus on \" + best_type[0]\
    \ + \" campaigns for better ROI\",\n        \"Prioritize \" + best_segment[0]\
    \ + \" segment customers\",\n        \"Adjust risk thresholds based on outcome\
    \ patterns\"\n    ],\n    \"timestamp\": datetime.now().isoformat()\n}\n\nprint(f\"\
    __OUTPUTS__ {json.dumps(result)}\")\n"
  depends_on:
  - collect_campaign_outcomes
  description: Deep analysis of campaign performance by segment
  previous_node: collect_campaign_outcomes
  timeout_seconds: 120
- id: detect_anomalies
  name: Detect Anomalies
  type: script
  script: "import json\nimport time\nimport random\nfrom datetime import datetime\n\
    \n# Simulate anomaly detection\ntime.sleep(1)\n\nperformance_data = ${analyze_performance}\n\
    \nanomalies = []\n\n# Check for unusual success rates\nfor campaign_type, stats\
    \ in performance_data[\"performance_by_type\"].items():\n    if stats[\"success_rate\"\
    ] < 0.3:\n        anomalies.append({\n            \"type\": \"low_success_rate\"\
    ,\n            \"category\": \"campaign_type\",\n            \"identifier\": campaign_type,\n\
    \            \"metric\": \"success_rate\",\n            \"value\": stats[\"success_rate\"\
    ],\n            \"severity\": \"HIGH\",\n            \"description\": f\"{campaign_type}\
    \ campaigns showing unusually low success rate\"\n        })\n\n# Check for segment\
    \ anomalies\nfor segment, stats in performance_data[\"performance_by_segment\"\
    ].items():\n    if stats[\"response_rate\"] < 0.2:\n        anomalies.append({\n\
    \            \"type\": \"low_response_rate\",\n            \"category\": \"customer_segment\"\
    ,\n            \"identifier\": segment,\n            \"metric\": \"response_rate\"\
    ,\n            \"value\": stats[\"response_rate\"],\n            \"severity\"\
    : \"MEDIUM\",\n            \"description\": f\"{segment} segment showing poor\
    \ response rates\"\n        })\n\n# Check for risk level anomalies\nfor risk_level,\
    \ stats in performance_data[\"performance_by_risk\"].items():\n    if risk_level\
    \ == \"LOW\" and stats[\"success_rate\"] < 0.4:\n        anomalies.append({\n\
    \            \"type\": \"unexpected_pattern\",\n            \"category\": \"risk_level\"\
    ,\n            \"identifier\": risk_level,\n            \"metric\": \"success_rate\"\
    ,\n            \"value\": stats[\"success_rate\"],\n            \"severity\":\
    \ \"MEDIUM\",\n            \"description\": \"Low-risk customers showing poor\
    \ campaign outcomes\"\n        })\n\n# Generate corrective actions\ncorrective_actions\
    \ = []\nfor anomaly in anomalies:\n    if anomaly[\"type\"] == \"low_success_rate\"\
    :\n        corrective_actions.append({\n            \"anomaly_id\": anomaly[\"\
    identifier\"],\n            \"action\": \"Review and revise campaign content\"\
    ,\n            \"priority\": \"HIGH\"\n        })\n    elif anomaly[\"type\"]\
    \ == \"low_response_rate\":\n        corrective_actions.append({\n           \
    \ \"anomaly_id\": anomaly[\"identifier\"],\n            \"action\": \"Adjust communication\
    \ timing and channels\",\n            \"priority\": \"MEDIUM\"\n        })\n\n\
    result = {\n    \"anomaly_detection_completed\": True,\n    \"anomalies_found\"\
    : len(anomalies),\n    \"anomalies\": anomalies,\n    \"corrective_actions\":\
    \ corrective_actions,\n    \"alert_required\": len([a for a in anomalies if a[\"\
    severity\"] == \"HIGH\"]) > 0,\n    \"timestamp\": datetime.now().isoformat()\n\
    }\n\nprint(f\"__OUTPUTS__ {json.dumps(result)}\")\n"
  depends_on:
  - analyze_performance
  description: Identify unusual patterns in campaign outcomes
  previous_node: analyze_performance
  timeout_seconds: 120
- id: evaluate_model_performance
  name: Evaluate Model Performance
  type: script
  script: "import json\nimport time\nimport random\nfrom datetime import datetime\n\
    \n# Simulate model evaluation\ntime.sleep(1)\n\ncampaign_data = ${collect_campaign_outcomes.campaigns_data}\n\
    \n# Simulate model predictions vs actual outcomes\nmodel_metrics = {\n    \"risk_prediction\"\
    : {\n        \"total_predictions\": len(campaign_data),\n        \"correct_predictions\"\
    : 0,\n        \"precision\": 0,\n        \"recall\": 0,\n        \"f1_score\"\
    : 0\n    },\n    \"campaign_selection\": {\n        \"total_selections\": len(campaign_data),\n\
    \        \"optimal_selections\": 0,\n        \"selection_accuracy\": 0\n    },\n\
    \    \"response_prediction\": {\n        \"total_predictions\": len(campaign_data),\n\
    \        \"correct_predictions\": 0,\n        \"mae\": 0  # Mean Absolute Error\
    \ for response time\n    }\n}\n\n# Calculate risk prediction accuracy\nrisk_predictions_correct\
    \ = 0\nfor campaign in campaign_data:\n    # Simulate that HIGH risk correctly\
    \ predicted low success\n    if campaign[\"risk_level\"] == \"HIGH\" and campaign[\"\
    outcome\"] != \"SUCCESS\":\n        risk_predictions_correct += 1\n    elif campaign[\"\
    risk_level\"] == \"LOW\" and campaign[\"outcome\"] == \"SUCCESS\":\n        risk_predictions_correct\
    \ += 1\n    elif campaign[\"risk_level\"] == \"MEDIUM\":\n        # Medium risk\
    \ is correct 70% of the time\n        if random.random() < 0.7:\n            risk_predictions_correct\
    \ += 1\n\nmodel_metrics[\"risk_prediction\"][\"correct_predictions\"] = risk_predictions_correct\n\
    model_metrics[\"risk_prediction\"][\"accuracy\"] = round(risk_predictions_correct\
    \ / len(campaign_data), 2)\n\n# Calculate campaign selection accuracy\noptimal_selections\
    \ = sum(1 for c in campaign_data if c[\"outcome\"] == \"SUCCESS\")\nmodel_metrics[\"\
    campaign_selection\"][\"optimal_selections\"] = optimal_selections\nmodel_metrics[\"\
    campaign_selection\"][\"selection_accuracy\"] = round(optimal_selections / len(campaign_data),\
    \ 2)\n\n# Response prediction accuracy\nresponse_predictions_correct = sum(1 for\
    \ c in campaign_data if c[\"response_received\"])\nmodel_metrics[\"response_prediction\"\
    ][\"correct_predictions\"] = response_predictions_correct\nmodel_metrics[\"response_prediction\"\
    ][\"accuracy\"] = round(response_predictions_correct / len(campaign_data), 2)\n\
    \n# Model confidence scores\nconfidence_scores = {\n    \"overall_confidence\"\
    : round(random.uniform(0.82, 0.91), 2),\n    \"risk_assessment_confidence\": round(random.uniform(0.85,\
    \ 0.93), 2),\n    \"campaign_selection_confidence\": round(random.uniform(0.78,\
    \ 0.88), 2),\n    \"timing_prediction_confidence\": round(random.uniform(0.73,\
    \ 0.85), 2)\n}\n\n# Areas for improvement\nimprovement_areas = []\nif model_metrics[\"\
    risk_prediction\"][\"accuracy\"] < 0.8:\n    improvement_areas.append(\"Enhance\
    \ risk prediction algorithms\")\nif model_metrics[\"campaign_selection\"][\"selection_accuracy\"\
    ] < 0.6:\n    improvement_areas.append(\"Improve campaign selection logic\")\n\
    if confidence_scores[\"timing_prediction_confidence\"] < 0.8:\n    improvement_areas.append(\"\
    Better timing prediction models needed\")\n\nresult = {\n    \"evaluation_completed\"\
    : True,\n    \"model_metrics\": model_metrics,\n    \"confidence_scores\": confidence_scores,\n\
    \    \"improvement_areas\": improvement_areas,\n    \"model_version\": \"2.0\"\
    ,\n    \"evaluation_timestamp\": datetime.now().isoformat()\n}\n\nprint(f\"__OUTPUTS__\
    \ {json.dumps(result)}\")\n"
  depends_on:
  - collect_campaign_outcomes
  - analyze_performance
  description: Assess AI model prediction accuracy
  previous_node: detect_anomalies
  timeout_seconds: 120
- id: generate_learning_insights
  name: Generate Learning Insights
  type: script
  script: "import json\nimport time\nfrom datetime import datetime\n\n# Simulate insight\
    \ generation\ntime.sleep(1)\n\nperformance_analysis = ${analyze_performance}\n\
    anomalies = ${detect_anomalies}\nmodel_evaluation = ${evaluate_model_performance}\n\
    \n# Extract patterns from successful campaigns\nsuccessful_patterns = []\n\n#\
    \ From performance analysis\nfor campaign_type, stats in performance_analysis[\"\
    performance_by_type\"].items():\n    if stats[\"success_rate\"] > 0.6:\n     \
    \   successful_patterns.append({\n            \"pattern\": f\"{campaign_type}_high_success\"\
    ,\n            \"description\": f\"{campaign_type} campaigns show {stats['success_rate']*100}%\
    \ success rate\",\n            \"factors\": [\"campaign_type\", \"content_strategy\"\
    , \"timing\"],\n            \"confidence\": stats[\"success_rate\"]\n        })\n\
    \n# From segment analysis\nfor segment, stats in performance_analysis[\"performance_by_segment\"\
    ].items():\n    if stats[\"avg_revenue\"] > 20000:\n        successful_patterns.append({\n\
    \            \"pattern\": f\"{segment}_high_value\",\n            \"description\"\
    : f\"{segment} segment generates \u20B9{stats['avg_revenue']} average revenue\"\
    ,\n            \"factors\": [\"customer_segment\", \"product_affinity\", \"engagement_level\"\
    ],\n            \"confidence\": 0.85\n        })\n\n# Failed pattern analysis\n\
    failure_patterns = []\nfor anomaly in anomalies[\"anomalies\"]:\n    failure_patterns.append({\n\
    \        \"pattern\": anomaly[\"type\"],\n        \"description\": anomaly[\"\
    description\"],\n        \"impact\": anomaly[\"severity\"],\n        \"recommended_action\"\
    : \"Adjust model parameters for \" + anomaly[\"identifier\"]\n    })\n\n# Model\
    \ adjustment recommendations\nmodel_adjustments = []\n\nif model_evaluation[\"\
    model_metrics\"][\"risk_prediction\"][\"accuracy\"] < 0.85:\n    model_adjustments.append({\n\
    \        \"component\": \"risk_prediction_model\",\n        \"adjustment\": \"\
    Add more behavioral features\",\n        \"expected_improvement\": \"+5-8% accuracy\"\
    \n    })\n\nif len(failure_patterns) > 2:\n    model_adjustments.append({\n  \
    \      \"component\": \"campaign_selection_algorithm\",\n        \"adjustment\"\
    : \"Implement adaptive thresholds\",\n        \"expected_improvement\": \"+10%\
    \ success rate\"\n    })\n\n# Feature importance updates\nfeature_importance_updates\
    \ = {\n    \"login_frequency\": 0.25,  # Increased from 0.20\n    \"transaction_recency\"\
    : 0.30,  # Increased from 0.25\n    \"portfolio_concentration\": 0.20,  # Decreased\
    \ from 0.25\n    \"fund_movement\": 0.15,  # Same\n    \"product_usage\": 0.10\
    \  # Decreased from 0.15\n}\n\n# Learning summary\nlearning_summary = {\n    \"\
    total_patterns_identified\": len(successful_patterns) + len(failure_patterns),\n\
    \    \"success_patterns\": len(successful_patterns),\n    \"failure_patterns\"\
    : len(failure_patterns),\n    \"model_adjustments_recommended\": len(model_adjustments),\n\
    \    \"confidence_in_insights\": 0.88\n}\n\nresult = {\n    \"insights_generated\"\
    : True,\n    \"successful_patterns\": successful_patterns,\n    \"failure_patterns\"\
    : failure_patterns,\n    \"model_adjustments\": model_adjustments,\n    \"feature_importance_updates\"\
    : feature_importance_updates,\n    \"learning_summary\": learning_summary,\n \
    \   \"timestamp\": datetime.now().isoformat()\n}\n\nprint(f\"__OUTPUTS__ {json.dumps(result)}\"\
    )\n"
  depends_on:
  - analyze_performance
  - detect_anomalies
  - evaluate_model_performance
  description: Extract actionable insights for model improvement
  previous_node: evaluate_model_performance
  timeout_seconds: 120
- id: update_ai_models
  name: Update AI Models
  type: script
  script: "import json\nimport time\nimport random\nfrom datetime import datetime,\
    \ timedelta\n\n# Check if model update is enabled\nenable_update = ${enable_model_update}\n\
    \nif not enable_update:\n    result = {\n        \"update_skipped\": True,\n \
    \       \"reason\": \"Model update disabled\",\n        \"timestamp\": datetime.now().isoformat()\n\
    \    }\nelse:\n    # Simulate model update\n    time.sleep(2)\n    \n    learning_insights\
    \ = ${generate_learning_insights}\n    \n    # Models to update\n    models_updated\
    \ = []\n    \n    # Update risk prediction model\n    risk_model_update = {\n\
    \        \"model_name\": \"risk_prediction_model\",\n        \"version_before\"\
    : \"2.0\",\n        \"version_after\": \"2.1\",\n        \"changes_applied\":\
    \ [\n            \"Updated feature weights based on importance\",\n          \
    \  \"Added behavioral pattern recognition\",\n            \"Adjusted risk thresholds\"\
    \n        ],\n        \"expected_improvement\": \"+6% accuracy\",\n        \"\
    validation_score\": 0.89,\n        \"deployment_status\": \"STAGED\"\n    }\n\
    \    models_updated.append(risk_model_update)\n    \n    # Update campaign selection\
    \ model\n    campaign_model_update = {\n        \"model_name\": \"campaign_selection_model\"\
    ,\n        \"version_before\": \"1.8\",\n        \"version_after\": \"1.9\",\n\
    \        \"changes_applied\": [\n            \"Incorporated successful pattern\
    \ learnings\",\n            \"Added segment-specific strategies\",\n         \
    \   \"Improved timing predictions\"\n        ],\n        \"expected_improvement\"\
    : \"+8% success rate\",\n        \"validation_score\": 0.86,\n        \"deployment_status\"\
    : \"STAGED\"\n    }\n    models_updated.append(campaign_model_update)\n    \n\
    \    # Update response prediction model\n    response_model_update = {\n     \
    \   \"model_name\": \"response_prediction_model\",\n        \"version_before\"\
    : \"1.5\",\n        \"version_after\": \"1.6\",\n        \"changes_applied\":\
    \ [\n            \"Enhanced customer engagement scoring\",\n            \"Added\
    \ communication preference learning\",\n            \"Improved response time predictions\"\
    \n        ],\n        \"expected_improvement\": \"+12% response rate\",\n    \
    \    \"validation_score\": 0.84,\n        \"deployment_status\": \"STAGED\"\n\
    \    }\n    models_updated.append(response_model_update)\n    \n    # A/B testing\
    \ configuration\n    ab_testing_config = {\n        \"enabled\": True,\n     \
    \   \"test_percentage\": 20,  # 20% on new models\n        \"control_percentage\"\
    : 80,  # 80% on current models\n        \"duration_days\": 14,\n        \"success_metrics\"\
    : [\n            \"campaign_success_rate\",\n            \"customer_response_rate\"\
    ,\n            \"revenue_impact\"\n        ]\n    }\n    \n    # Rollback plan\n\
    \    rollback_plan = {\n        \"automatic_rollback_enabled\": True,\n      \
    \  \"rollback_triggers\": [\n            \"success_rate_drop > 10%\",\n      \
    \      \"error_rate > 5%\",\n            \"compliance_violation\"\n        ],\n\
    \        \"monitoring_period\": \"48_hours\",\n        \"approval_required\":\
    \ False\n    }\n    \n    result = {\n        \"update_completed\": True,\n  \
    \      \"models_updated\": models_updated,\n        \"total_models_updated\":\
    \ len(models_updated),\n        \"ab_testing_config\": ab_testing_config,\n  \
    \      \"rollback_plan\": rollback_plan,\n        \"deployment_schedule\": (datetime.now()\
    \ + timedelta(days=1)).isoformat(),\n        \"update_confidence\": 0.91,\n  \
    \      \"timestamp\": datetime.now().isoformat()\n    }\n\nprint(f\"__OUTPUTS__\
    \ {json.dumps(result)}\")\n"
  depends_on:
  - generate_learning_insights
  description: Apply learning insights to improve AI models
  previous_node: generate_learning_insights
  timeout_seconds: 180
- id: generate_monitoring_report
  name: Generate Monitoring Report
  type: script
  script: "import json\nimport time\nfrom datetime import datetime\n\n# Simulate report\
    \ generation\ntime.sleep(1)\n\n# Gather all results\ncampaign_outcomes = ${collect_campaign_outcomes.summary}\n\
    performance_analysis = ${analyze_performance}\nanomalies = ${detect_anomalies}\n\
    model_evaluation = ${evaluate_model_performance}\nlearning_insights = ${generate_learning_insights}\n\
    model_updates = ${update_ai_models}\n\n# Executive summary\nexecutive_summary\
    \ = {\n    \"monitoring_period\": \"${monitoring_period}\",\n    \"campaigns_analyzed\"\
    : campaign_outcomes[\"total_campaigns\"],\n    \"overall_success_rate\": f\"{campaign_outcomes['success_rate']*100}%\"\
    ,\n    \"total_revenue_impact\": f\"\u20B9{campaign_outcomes['total_revenue_impact']:,}\"\
    ,\n    \"anomalies_detected\": anomalies[\"anomalies_found\"],\n    \"model_accuracy\"\
    : f\"{model_evaluation['model_metrics']['risk_prediction']['accuracy']*100}%\"\
    ,\n    \"models_updated\": model_updates.get(\"total_models_updated\", 0)\n}\n\
    \n# Key findings\nkey_findings = [\n    f\"Campaign success rate: {campaign_outcomes['success_rate']*100}%\"\
    ,\n    f\"Customer response rate: {campaign_outcomes['response_rate']*100}%\"\
    ,\n    f\"Revenue generated: \u20B9{campaign_outcomes['total_revenue_impact']:,}\"\
    ,\n    f\"Model prediction accuracy: {model_evaluation['model_metrics']['risk_prediction']['accuracy']*100}%\"\
    \n]\n\n# Add performance insights\nkey_findings.extend(performance_analysis[\"\
    key_insights\"])\n\n# Recommendations\nrecommendations = {\n    \"immediate_actions\"\
    : [],\n    \"short_term\": [],\n    \"long_term\": []\n}\n\n# Immediate actions\
    \ for anomalies\nif anomalies[\"alert_required\"]:\n    recommendations[\"immediate_actions\"\
    ].append(\"Address high-severity anomalies in campaign performance\")\n\n# Short-term\
    \ recommendations\nrecommendations[\"short_term\"].extend(performance_analysis[\"\
    recommendations\"])\n\n# Long-term recommendations\nif learning_insights[\"model_adjustments\"\
    ]:\n    recommendations[\"long_term\"].append(\"Implement recommended model adjustments\"\
    )\n\n# Compliance summary\ncompliance_summary = {\n    \"compliance_maintained\"\
    : True,\n    \"violations_detected\": 0,\n    \"audit_trail_complete\": True,\n\
    \    \"data_privacy_maintained\": True,\n    \"sebi_guidelines_followed\": True\n\
    }\n\n# Next steps\nnext_steps = [\n    {\n        \"action\": \"Deploy updated\
    \ models to A/B testing\",\n        \"timeline\": \"Within 24 hours\",\n     \
    \   \"owner\": \"AI Team\"\n    },\n    {\n        \"action\": \"Monitor A/B test\
    \ results\",\n        \"timeline\": \"Next 14 days\",\n        \"owner\": \"Analytics\
    \ Team\"\n    },\n    {\n        \"action\": \"Review anomaly corrections\",\n\
    \        \"timeline\": \"Within 48 hours\",\n        \"owner\": \"Business Team\"\
    \n    }\n]\n\n# Full report\nreport = {\n    \"report_id\": f\"MON_REPORT_{datetime.now().strftime('%Y%m%d%H%M%S')}\"\
    ,\n    \"report_type\": \"AI_RM_MONITORING_LEARNING\",\n    \"generated_at\":\
    \ datetime.now().isoformat(),\n    \"executive_summary\": executive_summary,\n\
    \    \"key_findings\": key_findings,\n    \"detailed_analysis\": {\n        \"\
    campaign_performance\": performance_analysis,\n        \"anomaly_detection\":\
    \ anomalies,\n        \"model_evaluation\": model_evaluation,\n        \"learning_insights\"\
    : learning_insights\n    },\n    \"recommendations\": recommendations,\n    \"\
    compliance_summary\": compliance_summary,\n    \"model_updates\": model_updates,\n\
    \    \"next_steps\": next_steps,\n    \"report_confidence\": 0.92\n}\n\nresult\
    \ = {\n    \"report_generated\": True,\n    \"report\": report,\n    \"distribution_list\"\
    : [\"ai_team\", \"business_team\", \"compliance_team\", \"management\"],\n   \
    \ \"timestamp\": datetime.now().isoformat()\n}\n\nprint(f\"__OUTPUTS__ {json.dumps(result)}\"\
    )\n"
  depends_on:
  - collect_campaign_outcomes
  - analyze_performance
  - detect_anomalies
  - evaluate_model_performance
  - generate_learning_insights
  - update_ai_models
  description: Create comprehensive monitoring and learning report
  previous_node: update_ai_models
  timeout_seconds: 120
- id: store_learning_data
  name: Store Learning Data
  type: script
  script: "import json\nimport time\nfrom datetime import datetime\n\n# Simulate data\
    \ storage\ntime.sleep(0.5)\n\nmonitoring_report = ${generate_monitoring_report.report}\n\
    \n# Prepare data for storage\nlearning_record = {\n    \"record_id\": f\"LEARN_{datetime.now().strftime('%Y%m%d%H%M%S')}\"\
    ,\n    \"monitoring_period\": \"${monitoring_period}\",\n    \"execution_date\"\
    : datetime.now().isoformat(),\n    \"report_id\": monitoring_report[\"report_id\"\
    ],\n    \"summary_metrics\": monitoring_report[\"executive_summary\"],\n    \"\
    model_updates_applied\": monitoring_report[\"model_updates\"],\n    \"learning_outcomes\"\
    : {\n        \"patterns_identified\": len(monitoring_report[\"detailed_analysis\"\
    ][\"learning_insights\"][\"successful_patterns\"]),\n        \"anomalies_found\"\
    : monitoring_report[\"detailed_analysis\"][\"anomaly_detection\"][\"anomalies_found\"\
    ],\n        \"model_improvements\": monitoring_report[\"model_updates\"].get(\"\
    total_models_updated\", 0)\n    },\n    \"storage_location\": \"ml_learning_database\"\
    ,\n    \"retention_period\": \"2_years\",\n    \"encryption_applied\": True,\n\
    \    \"compliance_verified\": True\n}\n\nresult = {\n    \"storage_completed\"\
    : True,\n    \"learning_record\": learning_record,\n    \"storage_confirmation\"\
    : f\"STORED_{learning_record['record_id']}\",\n    \"timestamp\": datetime.now().isoformat()\n\
    }\n\nprint(f\"__OUTPUTS__ {json.dumps(result)}\")\n"
  depends_on:
  - generate_monitoring_report
  description: Persist learning data for future reference
  previous_node: generate_monitoring_report
  timeout_seconds: 60
inputs:
- name: monitoring_period
  type: string
  default: last_7_days
  required: false
  description: 'Period to monitor: last_24_hours, last_7_days, last_30_days'
- name: customer_ids
  type: string
  default: all
  required: false
  description: Comma-separated customer IDs or 'all'
- name: campaign_types
  type: string
  default: all
  required: false
  description: 'Campaign types to monitor: retention, engagement, upsell, all'
- name: enable_model_update
  type: boolean
  default: true
  required: false
  description: Enable AI model updates based on outcomes
labels:
  product: relationship_manager
  version: '1.0'
  priority: medium
  department: wealth_management
  environment: production
  workflow_type: monitoring
outputs:
  anomalies:
    type: object
    source: detect_anomalies
    description: Detected anomalies
  model_updates:
    type: object
    source: update_ai_models
    description: Applied model updates
  model_evaluation:
    type: object
    source: evaluate_model_performance
    description: AI model performance metrics
  campaign_outcomes:
    type: object
    source: collect_campaign_outcomes
    description: Campaign execution outcomes
  learning_insights:
    type: object
    source: generate_learning_insights
    description: Extracted learning insights
  monitoring_report:
    type: object
    source: generate_monitoring_report.report
    description: Comprehensive monitoring report
  performance_analysis:
    type: object
    source: analyze_performance
    description: Detailed performance analysis
version: '1.0'
metadata:
  author: SMC AI Team
  use_case: AI RM Continuous Learning
  created_date: '2024-01-17'
  execution_frequency: daily
  compliance_framework: SEBI
namespace: smc_wealth_management
description: Monitors campaign outcomes and continuously improves AI models while
  maintaining compliance
timeout_seconds: 1800

Execution ID	Status	Started	Duration	Actions
`b85b93f0...`	COMPLETED	2025-07-17 09:40:53	N/A	View
`e79a1ac8...`	COMPLETED	2025-07-17 09:36:47	N/A	View