Regulatory Technology (RegTech): Compliance and Automation

Module: Fintech AI | Difficulty: Advanced

KYC (Know Your Customer)

AML (Anti-Money Laundering)

Regulatory Reporting

Automation Benefits

| Process | Manual | Automated | Improvement | |---------|--------|-----------|-------------| | KYC | 30 min | 5 min | 83% | | AML screening | 10 sec | 0.1 sec | 99% | | Reporting | 2 hours | 10 min | 92% |

import numpy as np

class AMLDetector:
    def __init__(self, threshold=0.8):
        self.threshold = threshold
    def analyze_transaction(self, transaction, customer_profile):
        features = self.extract_features(transaction, customer_profile)
        risk_score = self.model.predict_proba(features.reshape(1,-1))[0,1]
        return {
            'risk_score': risk_score,
            'flagged': risk_score > self.threshold,
            'reasons': self.explain(features)
        }
    def extract_features(self, tx, profile):
        return np.array([
            tx['amount'],
            tx['frequency'],
            profile['account_age'],
            profile['avg_balance']
        ])

Research Insight: RegTech uses AI to automate compliance, reducing costs by 50-70%. The key challenge is explainability — regulators require explanations for automated decisions. Graph networks improve AML detection by identifying suspicious transaction patterns.