Fast Audit AI offers an AI‑powered platform that instantly analyzes a company’s full financial statements to flag anomalies and risk areas, dramatically cutting due‑diligence time from weeks to days and reducing costs by up to 20‑fold. By comparing each line item to a statistically derived cluster of comparable public and private peers, the system delivers actionable insights for auditors, investors, and private‑capital teams, allowing them to focus on high‑value judgment tasks.
Funding
Funding not disclosed
Founders
Product
Problem
Due‑diligence and audit teams must manually review full financial statements, a process that typically takes weeks and incurs high costs, while subtle anomalies can remain undetected. The reliance on manual analysis limits the number of deals that can be evaluated and increases the risk of overlooking fraud or accounting irregularities.
Solution
Detect’s Fast Audit AI platform automates the analysis of complete financial statements by applying proprietary clustering and statistical comparison against peer groups. Each line‑item on balance sheets and income statements is benchmarked to the average of a dynamically generated peer cluster, instantly highlighting deviations that may indicate risk. The system combines rules‑based checks with machine‑learning algorithms to deliver an anomaly assessment within minutes, reducing due‑diligence time from six weeks to near‑real‑time. Auditors, investors, and private‑capital teams receive actionable insights that support professional judgment while freeing resources for higher‑value analysis. By delivering these analytics at a fraction of traditional costs, the platform enables faster deal flow and more thorough fraud detection.
Target Audience
Primary users are audit firms, institutional investors, and private‑capital teams that conduct financial due‑diligence and need rapid, data‑driven risk assessments.
Features
- Proprietary clustering methodology that groups companies by similar financial characteristics using a blend of rule‑based logic and machine‑learning models
- Line‑item‑level statistical comparison to peer cluster averages for both public and private entities, with the ability to upload custom data sets
- Instant anomaly detection dashboard that flags high‑risk items and quantifies deviation magnitude
- Integration of both rules‑based and ML algorithms achieving a 100% detection record for known fraud cases
- Scalable cloud‑based service that supports rapid analysis of multiple targets, accelerating deal screening and audit planning