AI for Banking

Overview

AI dan ML dapat digunakan untuk meningkatkan kemampuan CLM Platform dalam industri perbankan.

AI Use Cases

1. Fraud Detection

┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│ Transaction  │────▶│  AI/ML       │────▶│  Alert       │
│ Data         │     │  Engine      │     │  System      │
│              │     │              │     │              │
│ • Amount     │     │ • Model      │     │ • Fraud      │
│ • Time       │     │ • Scoring    │     │ • Risk       │
│ • Location   │     │ • Pattern    │     │ • Action     │
└──────────────┘     └──────────────┘     └──────────────┘

AI Keywords for Fraud Detection

fraud_detection, anomaly_detection, pattern_matching,
velocity_check, amount_analysis, geo_location,
behavioral_analysis, user_profiling, risk_scoring,
machine_learning, deep_learning, neural_network,
supervised_learning, unsupervised_learning, ensemble_method

Prompt Examples

"Build a fraud detection model for banking transactions"
"Create anomaly detection rules for suspicious transfers"
"Design a velocity check system for rapid transactions"
"Implement behavioral analysis for user authentication"

2. AML (Anti-Money Laundering)

┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│ Transaction  │────▶│  AML         │────▶│  Suspicious  │
│ Network      │     │  Analysis    │     │  Activity    │
│              │     │              │     │  Report      │
│ • Transfers  │     │ • Graph      │     │ • SAR        │
│ • Accounts   │     │ • Pattern    │     │ • Compliance │
│ • Networks   │     │ • Risk       │     │ • Audit      │
└──────────────┘     └──────────────┘     └──────────────┘

AI Keywords for AML

anti_money_laundering, aml, kyc, know_your_customer,
suspicious_activity_report, sar, currency_transaction_report,
ctr, beneficial_ownership, politically_exposed_persons,
pep, adverse_media, sanctions_screening,
network_analysis, graph_anomaly, pattern_detection

Prompt Examples

"Design an AML transaction monitoring system"
"Create rules for suspicious activity detection"
"Implement network analysis for money laundering"
"Build a KYC verification pipeline"

3. Credit Risk Scoring

┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│ Customer     │────▶│  Risk        │────▶│  Credit      │
│ Data         │     │  Model       │     │  Decision    │
│              │     │              │     │              │
│ • Income     │     │ • Score      │     │ • Approve    │
│ • History    │     │ • Grade      │     │ • Deny       │
│ • Behavior   │     │ • Limit      │     │ • Terms      │
└──────────────┘     └──────────────┘     └──────────────┘

AI Keywords for Credit Risk

credit_scoring, credit_risk, default_prediction,
probability_of_default, loss_given_default, exposure_at_default,
risk_rating, credit_grading, scorecard,
logistic_regression, random_forest, gradient_boosting,
feature_engineering, model_validation, backtesting

4. Customer Churn Prediction

┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│ Customer     │────▶│  Churn       │────▶│  Retention   │
│ Behavior     │     │  Model       │     │  Campaign    │
│              │     │              │     │              │
│ • Usage      │     │ • Probability│     │ • Offer      │
│ • Complaints │     │ • Segment    │     │ • Service    │
│ • Feedback   │     │ • Timeline   │     │ • Engagement │
└──────────────┘     └──────────────┘     └──────────────┘

AI Keywords for Churn

churn_prediction, customer_retention, customer_segmentation,
lifetime_value, engagement_score, satisfaction_score,
predictive_modeling, classification, regression,
cohort_analysis, rfm_analysis, behavioral_segmentation

AI Model Architecture

Fraud Detection Model

# Example: Fraud Detection with ML
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler

class FraudDetector:
    def __init__(self):
        self.model = RandomForestClassifier(n_estimators=100)
        self.scaler = StandardScaler()
    
    def train(self, X, y):
        X_scaled = self.scaler.fit_transform(X)
        self.model.fit(X_scaled, y)
    
    def predict(self, X):
        X_scaled = self.scaler.transform(X)
        return self.model.predict_proba(X_scaled)[:, 1]
    
    def get_feature_importance(self):
        return self.model.feature_importances_

Anomaly Detection Model

# Example: Anomaly Detection with Isolation Forest
from sklearn.ensemble import IsolationForest

class AnomalyDetector:
    def __init__(self, contamination=0.1):
        self.model = IsolationForest(contamination=contamination)
    
    def train(self, X):
        self.model.fit(X)
    
    def predict(self, X):
        return self.model.predict(X)
    
    def get_anomaly_scores(self, X):
        return self.model.decision_function(X)

Integration with CLM Platform

Data Pipeline

┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│ Transaction  │────▶│  Feature     │────▶│  ML Model    │
│ Logs         │     │  Engineering │     │  Inference   │
│              │     │              │     │              │
│ • Raw data   │     │ • Transform  │     │ • Predict    │
│ • Enriched   │     │ • Aggregate  │     │ • Score      │
│ • Historical │     │ • Features   │     │ • Alert      │
└──────────────┘     └──────────────┘     └──────────────┘

Alert Integration

{
  "alert_type": "fraud_detected",
  "severity": "HIGH",
  "confidence": 0.95,
  "transaction_id": "TXN-123456",
  "risk_score": 85,
  "recommended_action": "BLOCK",
  "model_version": "1.2.3",
  "features_used": ["amount", "velocity", "location"]
}

Metrics

Model Performance

Metric	Target	Description
Precision	> 90%	True positives / predicted positives
Recall	> 85%	True positives / actual positives
F1 Score	> 87%	Harmonic mean of precision and recall
AUC-ROC	> 0.95	Area under ROC curve
False Positive Rate	< 5%	False positives / negatives

Business Impact

Metric	Target	Description
Fraud Loss Reduction	> 50%	Reduction in fraud losses
Alert Accuracy	> 90%	Accuracy of generated alerts
Investigation Time	< 30 min	Time to investigate alert
Customer Friction	< 5%	False positive impact on customers

Next Steps

• AI Healthcare - AI for healthcare
• AI General - AI general use cases
• AI Keywords - Keywords overview