Banking

Crate: rustkernel-banking Kernels: 1 Feature: banking

Specialized banking operations kernel for fraud detection.

Kernel Overview

Kernel Details

FraudPatternMatch

GPU-accelerated fraud pattern detection using behavioral analysis and rule matching.

ID: banking/fraud-pattern-match Modes: Batch, Ring Throughput: ~500,000 transactions/sec

Input

pub struct FraudPatternInput {
    /// Transactions to analyze
    pub transactions: Vec<Transaction>,
    /// Pattern rules to apply
    pub rules: Vec<FraudRule>,
    /// Historical behavior profiles
    pub profiles: HashMap<String, BehaviorProfile>,
    /// Detection threshold
    pub threshold: f64,
}

pub struct Transaction {
    pub id: String,
    pub account_id: String,
    pub amount: f64,
    pub currency: String,
    pub merchant_category: String,
    pub location: Option<GeoLocation>,
    pub timestamp: u64,
    pub channel: TransactionChannel,
}

pub struct BehaviorProfile {
    pub avg_transaction_amount: f64,
    pub typical_merchants: Vec<String>,
    pub typical_locations: Vec<GeoLocation>,
    pub typical_hours: Vec<u8>,
}

pub enum TransactionChannel {
    CardPresent,
    CardNotPresent,
    ATM,
    Wire,
    ACH,
}

Output

pub struct FraudPatternOutput {
    /// Fraud scores per transaction
    pub scores: Vec<TransactionScore>,
    /// Triggered rules
    pub triggered_rules: Vec<TriggeredRule>,
    /// Recommended actions
    pub actions: Vec<RecommendedAction>,
}

pub struct TransactionScore {
    pub transaction_id: String,
    pub fraud_score: f64,
    pub risk_factors: Vec<RiskFactor>,
    pub recommendation: Recommendation,
}

pub enum Recommendation {
    Approve,
    Review,
    Decline,
    Challenge,  // 3D Secure, OTP, etc.
}

Example

use rustkernel::banking::fraud::{FraudPatternMatch, FraudPatternInput};

let kernel = FraudPatternMatch::new();

let result = kernel.execute(FraudPatternInput {
    transactions: vec![
        Transaction {
            id: "TX001".into(),
            account_id: "ACC123".into(),
            amount: 5000.0,
            currency: "USD".into(),
            merchant_category: "ELECTRONICS".into(),
            location: Some(GeoLocation { lat: 40.7128, lon: -74.0060 }),
            timestamp: 1699000000,
            channel: TransactionChannel::CardNotPresent,
        },
    ],
    rules: default_rules(),
    profiles: customer_profiles,
    threshold: 0.7,
}).await?;

for score in result.scores {
    if score.fraud_score > 0.7 {
        println!("High risk: {} - score {:.2}",
            score.transaction_id,
            score.fraud_score
        );
        for factor in score.risk_factors {
            println!("  - {:?}", factor);
        }
    }
}

Detection Methods

Behavioral Analysis

Compares transactions against established customer behavior:

• Amount deviation: Transaction size vs historical average
• Location deviation: Geographic distance from typical locations
• Time deviation: Transaction time vs typical activity hours
• Merchant deviation: New merchant category or type

Rule-Based Detection

Configurable rules for known fraud patterns:

pub struct FraudRule {
    pub id: String,
    pub name: String,
    pub conditions: Vec<RuleCondition>,
    pub score_impact: f64,
    pub enabled: bool,
}

pub enum RuleCondition {
    AmountGreaterThan(f64),
    VelocityExceeds { count: u32, window_seconds: u64 },
    LocationMismatch { max_distance_km: f64 },
    NewMerchant,
    HighRiskCountry(Vec<String>),
    CardNotPresentHighValue,
}

Velocity Checks

Detects rapid transaction patterns:

• Multiple transactions in short time
• Multiple cards on same device
• Multiple devices for same card

Ring Mode for Real-Time Scoring

Ring mode enables sub-millisecond fraud scoring:

use rustkernel::banking::fraud::FraudPatternRing;

let ring = FraudPatternRing::new();

// Pre-load customer profiles
ring.load_profiles(profiles).await?;

// Score transactions in real-time
async fn score_transaction(tx: Transaction) -> FraudDecision {
    let score = ring.score(tx).await?;

    match score.fraud_score {
        s if s < 0.3 => FraudDecision::Approve,
        s if s < 0.7 => FraudDecision::Review,
        _ => FraudDecision::Decline,
    }
}

Integration Patterns

Authorization Flow

// In payment authorization path
async fn authorize(tx: Transaction) -> AuthResult {
    // 1. Real-time fraud scoring (< 100ms)
    let fraud_result = fraud_ring.score(tx.clone()).await?;

    if fraud_result.recommendation == Recommendation::Decline {
        return AuthResult::Declined("Fraud risk");
    }

    if fraud_result.recommendation == Recommendation::Challenge {
        return AuthResult::Challenge3DS;
    }

    // 2. Continue with authorization
    process_authorization(tx).await
}

Batch Analysis

// Daily fraud pattern review
async fn daily_fraud_review() {
    let transactions = load_day_transactions().await?;

    let result = fraud_kernel.execute(FraudPatternInput {
        transactions,
        rules: all_rules(),
        profiles: all_profiles(),
        threshold: 0.5,  // Lower threshold for review
    }).await?;

    // Generate suspicious activity report
    generate_sar_report(result.scores.filter(|s| s.fraud_score > 0.5));
}

Performance Considerations

1. Profile caching: Keep frequently accessed profiles in GPU memory
2. Rule optimization: Order rules by selectivity (most filtering first)
3. Batch when possible: Process multiple transactions per GPU call
4. Async patterns: Don’t block authorization on slow operations