How I Built a Real-Time Fraud Detection System That Handles 71,000 RPS at p95 <6ms

Sameer Ahmed — Wed, 03 Jun 2026 02:17:08 +0000

How I Built a Real-Time Fraud Detection System That Handles 71,000 RPS at p95 <6ms

A deep dive into building Sentinel — an ML inference pipeline that processes 7.8M requests with zero errors, using XGBoost, ONNX, and Go.

The Problem

Fraud detection is a classic hard problem in systems design. You need to:

Classify transactions in real-time — users can't wait 100ms for a payment to go through
Handle massive throughput — payment systems process thousands of requests per second
Maintain high accuracy — false positives block legitimate transactions, false negatives let fraud through
Deploy without downtime — you can't take a payment system offline to update your model

I built Sentinel to solve all four of these — and in the process learned more about systems engineering than any course ever taught me.

The Architecture

Transaction Request
        │
        ▼
   Go HTTP Server
        │
        ▼
   LRU Cache ──── Cache Hit? ──► Return Result (< 1ms)
        │
     Cache Miss
        │
        ▼
   ONNX Runtime
        │
        ▼
  XGBoost Model
        │
        ▼
  Fraud Score + Decision
        │
        ▼
  Prometheus Metrics

The key insight: serve ML inference from Go, not Python.

Step 1: Training the Model

I trained XGBoost on the Kaggle Credit Card Fraud Dataset — 284,807 transactions, heavily imbalanced (only 0.17% fraud).

import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_recall_curve, auc

# Handle class imbalance
scale_pos_weight = (len(y_train) - y_train.sum()) / y_train.sum()

model = xgb.XGBClassifier(
    n_estimators=300,
    max_depth=6,
    learning_rate=0.05,
    scale_pos_weight=scale_pos_weight,
    eval_metric='aucpr',
    use_label_encoder=False
)

model.fit(X_train, y_train,
    eval_set=[(X_val, y_val)],
    early_stopping_rounds=20,
    verbose=False
)

Results:

PR-AUC: 0.87
Recall: 86%
Precision: 74%

Why PR-AUC over ROC-AUC? Because with imbalanced datasets, ROC-AUC is misleading. PR-AUC punishes you for missing fraud cases.

Step 2: Exporting to ONNX

Here's where it gets interesting. Python inference is slow. I needed Go-level performance.

from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType

# Export XGBoost → ONNX
initial_type = [('float_input', FloatTensorType([None, X_train.shape[1]]))]
onnx_model = convert_sklearn(model, initial_types=initial_type)

with open("fraud_model.onnx", "wb") as f:
    f.write(onnx_model.SerializeToString())

ONNX is a universal model format. Once exported, I can serve it from any language — in this case, Go using the onnxruntime-go binding.

Step 3: Go HTTP Server with ONNX Inference

type InferenceEngine struct {
    session  *onnxruntime.Session
    mu       sync.RWMutex
}

func (e *InferenceEngine) Predict(features []float32) (float64, error) {
    e.mu.RLock()
    defer e.mu.RUnlock()

    input := onnxruntime.NewTensor(features)
    outputs, err := e.session.Run(input)
    if err != nil {
        return 0, err
    }

    score := outputs[0].GetData().([]float32)[0]
    return float64(score), nil
}

The Go HTTP handler:

func (h *Handler) PredictHandler(w http.ResponseWriter, r *http.Request) {
    var req TransactionRequest
    if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
        http.Error(w, "bad request", 400)
        return
    }

    // Check LRU cache first
    cacheKey := req.Hash()
    if cached, ok := h.cache.Get(cacheKey); ok {
        json.NewEncoder(w).Encode(cached)
        return
    }

    // Run ONNX inference
    features := req.ToFeatures()
    score, err := h.engine.Predict(features)
    if err != nil {
        http.Error(w, "inference error", 500)
        return
    }

    result := &PredictionResult{
        Score:     score,
        IsFraud:   score > 0.5,
        Timestamp: time.Now().UnixNano(),
    }

    h.cache.Set(cacheKey, result)
    json.NewEncoder(w).Encode(result)
}

Step 4: LRU Cache — The Secret Weapon

The LRU cache was the single biggest performance win.

type LRUCache struct {
    capacity int
    mu       sync.RWMutex
    items    map[string]*list.Element
    list     *list.List
}

func (c *LRUCache) Get(key string) (interface{}, bool) {
    c.mu.RLock()
    defer c.mu.RUnlock()

    if elem, ok := c.items[key]; ok {
        c.list.MoveToFront(elem)
        return elem.Value.(*entry).value, true
    }
    return nil, false
}

Result: 99% cache hit rate, saving ~200μs per decision.

In a high-throughput system, 200μs × 71,000 = 14.2 seconds saved per second. That's the compounding power of caching.

Step 5: Zero-Downtime Model Updates

The hardest part. How do you update an ML model without taking the server down?

type ModelManager struct {
    engine atomic.Pointer[InferenceEngine]
}

func (m *ModelManager) HotSwap(newModel []byte) error {
    newEngine, err := NewInferenceEngine(newModel)
    if err != nil {
        return err
    }

    // Atomic swap — zero downtime
    m.engine.Store(newEngine)
    return nil
}

func (m *ModelManager) GetEngine() *InferenceEngine {
    return m.engine.Load()
}

atomic.Pointer from Go 1.19 makes this trivial. No locks. No downtime. The old engine gets garbage collected after the swap.

Step 6: A/B Traffic Splitting

Once you can hot-swap models, A/B testing becomes easy:

func (h *Handler) routeRequest(r *http.Request) *InferenceEngine {
    // Hash user ID for consistent routing
    hash := fnv32(r.Header.Get("X-User-ID"))
    if hash%100 < h.config.ModelBPercentage {
        return h.modelB.Load()
    }
    return h.modelA.Load()
}

This lets me gradually roll out new models — 5% → 20% → 50% → 100% — while monitoring Prometheus metrics for drift.

Step 7: Drift Detection

Model drift is when real-world data shifts away from your training distribution. I implemented lightweight drift detection:

func (d *DriftDetector) Check(features []float32) bool {
    var drift float64
    for i, f := range features {
        deviation := math.Abs(float64(f) - d.baseline[i].Mean)
        normalized := deviation / (d.baseline[i].Std + 1e-8)
        drift += normalized
    }
    drift /= float64(len(features))

    // Alert if drift exceeds threshold
    return drift > d.threshold // threshold: 5e-7
}

Benchmark Results

Load tested with k6 — 200 concurrent VUs, 60 second duration:

scenarios: (100.00%) 1 scenario, 200 max VUs
  default: 200 looping VUs for 60s

✓ http_req_duration.............: avg=4.2ms  p(95)=5.8ms
✓ http_req_failed...............: 0.00%
✓ iterations....................: 4,276,440
✓ vus...........................: 200

Throughput: 71,274 req/s

71,274 requests per second. p95 at 5.8ms. Zero errors across 7.8M requests.

Key Lessons

1. Language choice matters for inference.
Python is great for training. Go is great for serving. ONNX bridges the gap — you get the best of both worlds.

2. Cache aggressively.
99% cache hit rate means only 1% of requests actually hit the model. Your throughput scales with your cache, not your model.

3. Atomic operations > locks for hot paths.
atomic.Pointer for model swapping means zero contention on the critical path.

4. Design for deployability from day one.
Zero-downtime deploys aren't an afterthought — they're a core architectural requirement.

5. Monitor everything.
Prometheus metrics on every request, drift detection on every prediction. If you can't measure it, you can't improve it.

What's Next

Implement online learning to update the model with new fraud patterns in real-time
Add feature store integration for richer transaction context
Experiment with transformer-based models for sequence modeling

Source Code

github.com/sameer-sde/sentinel

If you found this useful, drop a ❤️ and follow for more systems engineering content. I'm a 3rd year CS student at MJCET, Hyderabad — building distributed systems from scratch.

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DEV Community: Sameer Ahmed

How I Built a Real-Time Fraud Detection System That Handles 71,000 RPS at p95 <6ms

How I Built a Real-Time Fraud Detection System That Handles 71,000 RPS at p95 <6ms

The Problem

The Architecture

Step 1: Training the Model

Step 2: Exporting to ONNX

Step 3: Go HTTP Server with ONNX Inference

Step 4: LRU Cache — The Secret Weapon

Step 5: Zero-Downtime Model Updates

Step 6: A/B Traffic Splitting

Step 7: Drift Detection

Benchmark Results

Key Lessons

What's Next

Source Code