baynota
Capital
AI-Powered Financial Audit Intelligence

The Intelligence Layer for Financial Assurance.

Internal Use / Confidential

An AI-powered platform for discrepancy detection, risk flagging, and predictive audit modelling. Ingests 100% of financial records — not a 5% sample. Built for auditors. Built for the CFO.

60–70%
Audit prep time saved
100%
Transactions scanned
3 layers
Rules · Stats · ML

Decision-support intelligence — not a replacement for statutory audit.

Audit Intelligence — Expected vs. Reported
Live

Anomaly Distribution — Q4 YTD

Expected
Anomaly
JanFebMarAprMayJunJulAug

Revenue (Mar)

$12.4M$13.8–14.5M
−$1.7MHIGH

Marketing Exp.

$3.1M$1.9–2.2M
+$0.9MMED

Vendor Overlap

14 flags0–2
+12HIGH

JE Clustering

842 JEsNormal dist.
ClusterELEV
2,847 transactions · 3 detection layers · v2.418 flags escalated

Time saved

60–70%

Population scanned

100%

Revenue Recognition Drift
Journal Entry Clustering
Vendor Relationship Flags
Isolation Forest · One-Class SVM
Z-Score Outlier Detection
Predictive Financial Ranges
60–70% Time Reduction
Explainable Risk Scoring
Full Population Coverage
Revenue Recognition Drift
Journal Entry Clustering
Vendor Relationship Flags
Isolation Forest · One-Class SVM
Z-Score Outlier Detection
Predictive Financial Ranges
60–70% Time Reduction
Explainable Risk Scoring
Full Population Coverage
Revenue Recognition Drift
Journal Entry Clustering
Vendor Relationship Flags
Isolation Forest · One-Class SVM
Z-Score Outlier Detection
Predictive Financial Ranges
60–70% Time Reduction
Explainable Risk Scoring
Full Population Coverage
Problem Statement
Astransactionvolumesscale,traditionalauditsbreakdown.

Financial audits today are time-consuming, manual, and heavily dependent on human judgment. Despite the availability of digital records, auditors still spend a significant portion of their time on sampling — missing the systemic issues that matter most.

The Sampling Trap — Visualised

95%

Unchecked Risk

Gold dots represent what is actually reviewed in a typical manual audit

01

The Sampling Trap

Manual audits review only ~5% of transactions. Finding a needle in a haystack is impossible if you only look at 5% of the hay. Systemic issues are missed entirely.

95%

Unchecked Risk

02

Unscalable Reconciliation

Manual reconciliation of large financial datasets is slow and error-prone as company data grows exponentially. Traditional processes do not scale efficiently.

10×

Volume Growth

03

Reactive, Not Proactive

Traditional audits look backward at what happened, rarely utilising predictive analytics to understand what financial figures should have been.

0%

Predictive Coverage

How the System Works
Fromrawdatatorisksignalinfivesteps.
How It Works — Five Steps
01

Automated Ingestion

Upload structured financial ledgers and statements via CSV. No heavy ERP integration required — instant time-to-value across GL, AP, AR, payroll exports, and policy files.

Amulti-layeredapproachtoaccuracy.

Three interlocking detection layers work simultaneously across the full transaction population — not a sample. Every flagged item is accompanied by the reason, expected range, confidence level, and risk classification.

I

Layer I

Rule-Based Checks

  • Debit-credit mismatches
  • Inconsistent balances
  • Missing or duplicate transactions
  • Invalid account behaviour
II

Layer II

Statistical Analysis

  • Z-score & IQR outlier detection
  • Trend deviation analysis
  • Seasonal pattern breaks
  • Full population coverage
III

Layer III

Machine Learning Models

  • Isolation Forest
  • One-Class SVM
  • Time-series forecasting
  • Explainable AI outputs

Underpinning All Three Layers

Explainability

Every flag includes the reason for flagging, the expected vs. reported values, a confidence level, and a risk classification of Low / Medium / High — aligned with audit documentation standards.

Market Opportunity
RidingthewaveofAIadoptioninfinance.

The Total Addressable Market for Auditing Services is moving toward $448B by 2032–35. AI in Accounting alone is projected to reach $96B by 2033 at a 39% CAGR — providing a massive ceiling for software adoption.

Key Impact — Core Value Proposition

0–70%

Reduction in audit preparation time

Market Segments — Size & Growth

AI in Accounting

by 2033

$4.9B

$96B

39%

AI in Audit

by 2033

$1.4B

$24B

20–30%

Financial Audit Software

by 2032

$12B

$25B

8–9%

Auditing Services (TAM)

by 2035

$243B

$448B

4–5%

Regional Adoption

North America

Current Stronghold

SOX / GAAP compliance

Europe

Strong Enterprise

IFRS reporting standards

Asia-Pacific

Fastest Growth

India, China, SE Asia

LatAm / MEA

Early Opportunity

Modernisation wave

Competitive Advantage
Differentiatedbyfinanciallogic.
01

Predictive Framework

We don't just find errors — we forecast expectations. The system asks 'What should this number be?' based on historical patterns, then surfaces the variance.

02

Finance-First Design

Built for auditors and CFO offices, not data scientists. Outputs are aligned with standard audit workflows, terminology, and before/after reporting conventions.

03

Explainability

We provide the 'Why' behind every risk flag. Reason, expected range, confidence level, and risk classification accompany every flagged item.

04

Lightweight Onboarding

CSV-based ingestion means instant time-to-value. No heavy ERP integration projects. Upload ledger data and receive audit-grade intelligence immediately.

The Landscape — Where We Fit

Competitor type 1

AI-First Platforms

MindBridge, Oversight Systems, Inflo

Strong on anomaly detection, but often lack explicit expected-value modelling in reports.

Competitor type 2

Automation Tools

DataSnipper, Fieldguide, AuditBoard

Excellent for workflow and data extraction, but focused on process automation rather than deep predictive intelligence.

Competitor type 3

Big Four Internal

KPMG, EY, Deloitte, PwC

Proprietary tools not commercialised as SaaS. Impact measurement still cautious. Not available to the broader market.

Roadmap & Vision
Roadmaptocontinuousintelligence.

Phase 1

Current
  • Core rule engine
  • Basic anomaly detection
  • CSV ingestion & reporting

Phase 2

Next
  • Predictive financial modelling
  • Risk scoring framework
  • Enhanced explainability

Phase 3

Vision
  • Industry benchmarking
  • Direct ERP integrations
  • Continuous audit intelligence

Long-Term Vision

“To become the intelligence layer for financial assurance, enabling faster, smarter, and more transparent audits across global businesses.”

Disclaimer: This platform is designed as a decision-support and risk-flagging tool. It does not replace statutory audits or professional judgment.

Bring Audit Intelligence into Your Mandate
Theauditlayershouldbeactivebeforetheroomgetscrowded.

Whether you are an audit firm looking to reduce manual hours and improve coverage, or a CFO office seeking governance certainty — the platform acts as a decision-support engine for every engagement.

DISCLAIMER: This platform is designed as a decision-support and risk-flagging tool. It does not replace statutory audits or professional judgment, but enhances efficiency, coverage, and insight quality. Internal use / confidential.