Innovative Audit Solutions

Experience cutting-edge technology for smarter audits and faster fraud detection with Hessq's AI tools.

Scientific and Computacional Basis

This project relies on solid scientific foundations and efficient computational methods to support reliable, data-driven outcomes.

Scientific Research Directions

We explore how quantum computing can enhance scientific auditing by improving data accuracy, traceability, and trust in research and industrial applications.

To automate the detection of inconsistencies, errors, and compliance breaches in large-scale financial or operational records — without the need for exhaustive manual review.

The Challenge:

Traditional audit processes rely on fixed rules and sample-based checks, making them slow, labor-intensive, and prone to oversight. With ever-growing datasets and complex compliance requirements, auditors struggle to:

• Analyze 100% of records in real time

• Detect hidden patterns or subtle anomalies

• Adapt to evolving audit criteria and risk signals

Our Quantum-Inspired Solution:

HessQ applies Quantum-Inspired Optimization (QUBO) and graph-based models to scan massive audit trails intelligently — identifying patterns that indicate risks, fraud, or non-compliance.

Data Modeling

Every financial or operational record is modeled as a point in a multi-dimensional network.

QUBO identifies unusual combinations of entries that deviate from the expected patterns (e.g., duplicate payments, outlier values, mismatched accounts).

Instead of reviewing every entry equally, HessQ ranks entries by risk level, optimizing the audit path like a Traveling Salesman Problem (TSP) — focusing first on the riskiest clusters.

Anomaly Scoring

Dynamic Prioritization

The Result:

Outputs Delivered:

• High-risk records automatically flagged

• Visualization of audit anomaly clusters

• Reduction in manual review workload

• Audit coverage increased to >95% of entries

Why HessQ Outperforms Traditional Models

• Global Pattern Recognition: Captures interactions across data dimensions that rules miss

• Less Human Intervention: Minimizes manual rule-setting and scripting

• Self-Tuning: Adapts dynamically as datasets evolve

• Scales Easily: From small audits to enterprise-wide systems

• More Insight, Less Noise: Focuses on meaningful outliers — not alert fatigue