swift-rs-ai 0.1.0

//! Fraud detection and anomaly detection

use async_trait::async_trait;
use serde::{Deserialize, Serialize};
use tracing::info;

use swift_rs_core::domain::Message;

/// Fraud detection result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FraudScore {
    pub score: f64,
    pub risk_level: RiskLevel,
    pub flags: Vec<String>,
}

/// Risk level
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum RiskLevel {
    Low,
    Medium,
    High,
    Critical,
}

/// Fraud detector trait
#[async_trait]
pub trait FraudDetector: Send + Sync {
    /// Analyze a message for fraud indicators
    async fn analyze(&self, message: &Message) -> FraudScore;
}

/// Basic fraud detector implementation
pub struct BasicFraudDetector;

impl BasicFraudDetector {
    pub fn new() -> Self {
        Self
    }
}

#[async_trait]
impl FraudDetector for BasicFraudDetector {
    async fn analyze(&self, message: &Message) -> FraudScore {
        info!("Analyzing message {} for fraud", message.metadata.id);

        // TODO: Implement actual ML-based fraud detection
        // This would use ONNX runtime or Candle for inference

        let score = 0.1; // Placeholder
        let risk_level = if score > 0.8 {
            RiskLevel::Critical
        } else if score > 0.5 {
            RiskLevel::High
        } else if score > 0.3 {
            RiskLevel::Medium
        } else {
            RiskLevel::Low
        };

        FraudScore {
            score,
            risk_level,
            flags: vec![],
        }
    }
}