58：Agentic在金融风控中的应用实战

# 金融风控Agentic系统架构
class FinancialRiskControlAgent:
    def __init__(self, knowledge_base, model):
        self.knowledge_base = knowledge_base
        self.model = model
        self.tools = {
            "transaction_monitor": TransactionMonitor(),
            "fraud_detector": FraudDetector(),
            "credit_evaluator": CreditEvaluator(),
            "compliance_monitor": ComplianceMonitor()
        }
    
    def monitor_transaction(self, transaction):
        """监控交易"""
        # 分析交易
        analysis_result = self.tools["transaction_monitor"].analyze(transaction)
        
        # 检测欺诈
        fraud_result = self.tools["fraud_detector"].detect(transaction, analysis_result)
        
        # 评估信用风险
        credit_result = self.tools["credit_evaluator"].evaluate(transaction, analysis_result)
        
        # 检查合规性
        compliance_result = self.tools["compliance_monitor"].check(transaction, analysis_result)
        
        # 生成风险评估报告
        report = self._generate_report(analysis_result, fraud_result, credit_result, compliance_result)
        
        return report
    
    def _generate_report(self, analysis_result, fraud_result, credit_result, compliance_result):
        """生成风险评估报告"""
        risk_score = self._calculate_risk_score(fraud_result, credit_result, compliance_result)
        
        report = {
            "analysis": analysis_result,
            "fraud": fraud_result,
            "credit": credit_result,
            "compliance": compliance_result,
            "risk_score": risk_score,
            "recommendation": self._generate_recommendation(risk_score)
        }
        return report
    
    def _calculate_risk_score(self, fraud_result, credit_result, compliance_result):
        """计算风险评分"""
        # 实现风险评分计算逻辑
        fraud_score = fraud_result["risk_score"]
        credit_score = credit_result["risk_score"]
        compliance_score = compliance_result["risk_score"]
        
        # 加权计算总风险评分
        total_score = 0.4 * fraud_score + 0.3 * credit_score + 0.3 * compliance_score
        return total_score
    
    def _generate_recommendation(self, risk_score):
        """生成建议"""
        if risk_score < 0.3:
            return "低风险，建议通过"
        elif risk_score < 0.7:
            return "中等风险，建议进一步审查"
        else:
            return "高风险，建议拒绝"

class TransactionMonitor:
    """交易监控器"""
    def analyze(self, transaction):
        """分析交易"""
        # 实现交易分析逻辑
        amount = transaction.get("amount", 0)
        frequency = self._calculate_frequency(transaction["user_id"])
        location = transaction.get("location", "")
        time = transaction.get("time", "")
        
        return {
            "amount": amount,
            "frequency": frequency,
            "location": location,
            "time": time,
            "is_unusual_amount": amount > 10000,
            "is_unusual_location": self._is_unusual_location(location, transaction["user_id"]),
            "is_unusual_time": self._is_unusual_time(time, transaction["user_id"])
        }
    
    def _calculate_frequency(self, user_id):
        """计算交易频率"""
        # 实现交易频率计算逻辑
        pass
    
    def _is_unusual_location(self, location, user_id):
        """判断是否为异常地点"""
        # 实现异常地点判断逻辑
        pass
    
    def _is_unusual_time(self, time, user_id):
        """判断是否为异常时间"""
        # 实现异常时间判断逻辑
        pass

class FraudDetector:
    """欺诈检测器"""
    def detect(self, transaction, analysis_result):
        """检测欺诈"""
        # 实现欺诈检测逻辑
        risk_factors = []
        
        if analysis_result["is_unusual_amount"]:
            risk_factors.append("异常交易金额")
        if analysis_result["is_unusual_location"]:
            risk_factors.append("异常交易地点")
        if analysis_result["is_unusual_time"]:
            risk_factors.append("异常交易时间")
        
        # 计算欺诈风险评分
        risk_score = min(1.0, len(risk_factors) * 0.33)
        
        return {
            "risk_factors": risk_factors,
            "risk_score": risk_score,
            "is_fraud": risk_score > 0.6
        }

class CreditEvaluator:
    """信用评估器"""
    def evaluate(self, transaction, analysis_result):
        """评估信用风险"""
        # 实现信用评估逻辑
        user_id = transaction["user_id"]
        credit_score = self._get_user_credit_score(user_id)
        debt_ratio = self._calculate_debt_ratio(user_id)
        payment_history = self._get_payment_history(user_id)
        
        # 计算信用风险评分
        risk_score = 1.0 - credit_score * 0.6 - (1 - debt_ratio) * 0.2 - payment_history * 0.2
        
        return {
            "credit_score": credit_score,
            "debt_ratio": debt_ratio,
            "payment_history": payment_history,
            "risk_score": risk_score
        }
    
    def _get_user_credit_score(self, user_id):
        """获取用户信用评分"""
        # 实现信用评分获取逻辑
        pass
    
    def _calculate_debt_ratio(self, user_id):
        """计算债务比率"""
        # 实现债务比率计算逻辑
        pass
    
    def _get_payment_history(self, user_id):
        """获取还款历史"""
        # 实现还款历史获取逻辑
        pass

class ComplianceMonitor:
    """合规监控器"""
    def check(self, transaction, analysis_result):
        """检查合规性"""
        # 实现合规性检查逻辑
        issues = []
        
        # 检查反洗钱
        if self._is_suspicious_transaction(transaction):
            issues.append("疑似洗钱交易")
        
        # 检查交易限额
        if self._exceeds_limit(transaction):
            issues.append("超出交易限额")
        
        # 计算合规风险评分
        risk_score = min(1.0, len(issues) * 0.5)
        
        return {
            "issues": issues,
            "risk_score": risk_score,
            "is_compliant": len(issues) == 0
        }
    
    def _is_suspicious_transaction(self, transaction):
        """判断是否为可疑交易"""
        # 实现可疑交易判断逻辑
        pass
    
    def _exceeds_limit(self, transaction):
        """判断是否超出限额"""
        # 实现限额检查逻辑
        pass