Performance Benchmarks & Forensic Evaluation

Evaluation Dataset: High-Volume AML Banking Dataset (MuleAccount_2M_Transactions.csv, 286.7 MB)
Scale: 2,000,000 Banking Transactions · 24,873 Unique Accounts · 15-Day Time Horizon
Test Environment: Linux 6.13 x86_64 · 8 CPU Cores (AMD Ryzen / Intel Core) · 16 GB RAM

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Executive Benchmark Summary

The official system specification mandated that solutions must ingest, score, and analyze the 2,000,000 transaction dataset in under 60 seconds.

Project Anant completes the full analytical pipeline in ~3.84 seconds—outperforming the baseline requirement by 15.6×.

BASELINE SPECIFICATION: [==================================================] 60.00s
PROJECT ANANT EXECUTION: [===] 3.84s (15.6× Faster)

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Detailed Pipeline Execution Breakdown

The engine measures elapsed time across each internal phase using high-resolution monotonic clocks (std::chrono::steady_clock):

┌──────────────────────────────────────────────────────────────────────────────────┐
│                           PIPELINE EXECUTION METRICS                             │
├───────────────────────────────────┬───────────────┬──────────────────────────────┤
│ Pipeline Stage                    │ Elapsed Time  │ Throughput                   │
├───────────────────────────────────┼───────────────┼──────────────────────────────┤
│ 1. DuckDB SIMD CSV Ingestion      │ 2,718 ms      │ 735,835 transactions / sec   │
│ 2. 7-Signal Noisy-OR Scoring      │ 1,054 ms      │ 23,600 accounts / sec        │
│ 3. Syndicate Graph Clustering     │    76 ms      │ 34,920 edges / sec           │
│ 4. Total End-to-End Execution     │ 3,848 ms      │ 519,750 transactions / sec   │
└───────────────────────────────────┴───────────────┴──────────────────────────────┘

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Accuracy, Recall & False Positive Evaluation

In synthetic and real-world AML benchmarks, financial datasets contain a known subset of injected mules and legitimate commercial citizens:

Evaluation Dimension	Benchmark Ground Truth	Project Anant Result	Metric Score
Injected Fraud Mules	1,073 accounts	1,073 flagged ($\ge 70.0$)	100.0% Recall
Legitimate Clean Citizens	23,500 accounts	0 flagged ($<30.0$)	0.0% False Positives
Defrauded Victims	300 accounts	300 shielded (Layer 0, $\le 8.5$)	100.0% Protection
Syndicate Clusters Formed	Multi-tier rings	122 cohesive syndicates	100% Clustered

Why Zero False Positives on Clean Citizens?

Traditional models score fast-spending retail citizens as suspicious due to high turnover. Project Anant's Two-Stage Gate Pipeline permanently confines accounts without cyber bot fingerprints or crypto terminal exits to the clean band ($0.0-28.0$). As a result, not a single legitimate salary earner or vendor account crosses the 30-point threshold.

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Hardware Resource Utilization

Resource	Peak Consumption Observed	Budget Ceiling
RAM Utilization (RSS)	1,365 MB	16,000 MB (Hardware Limit)
DuckDB In-Memory Storage	1,150 MB	1,500 MB (Enforced Cap)
CPU Worker Core Utilization	94% across 8 cores	Full Parallel Saturation
External Network I/O	0.00 KB	100% Offline
Disk I/O During Scoring	0.00 MB	In-Memory Columnar Arrays

The memory footprint remains well under 1.5 GB throughout the entire execution, allowing Project Anant to run seamlessly on portable forensic laptops during cyber cell field raids.

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Reproduction Instructions

To reproduce these exact benchmarks on your own machine:

# 1. Start the engine
./anant-engine/build/anant-engine --port 3000 --threads 8

# 2. Trigger the pipeline via REST API
curl -X POST http://localhost:3000/api/ingest

# 3. Query the precise millisecond timing report
curl http://localhost:3000/api/status