Financial Audit Dashboard for ERPNext

Advanced Financial Audit & Analysis Dashboard

A standalone Frappe app that provides financial auditors with a comprehensive, real-time overview of any ERPNext business — with AI-powered analysis, advanced fraud detection, and bilingual (Arabic/English) support.

Overview

Financial Audit Dashboard is a universal, plug-and-play auditing tool that works on any Frappe/ERPNext site regardless of chart of accounts, customization level, or installed apps. It dynamically discovers all transaction types, custom doctypes, accounting dimensions, and installed applications — giving auditors a complete 360-degree financial picture in a single page.

Key Highlights

30+ Data Sections covering every aspect of financial operations
12 KPI Cards with real-time financial metrics
5 Interactive ECharts (monthly trends, daily sales, expense breakdown, cash flow, Benford's Law)
20+ Data Tables with drill-down links
Secure AI Analysis — 3 providers (Puter free, OpenAI, Custom/Self-hosted) with server-side anonymization
PDF Export — one-click print-optimized audit reports
Custom Dashboard Layouts — show/hide sections with saved preferences
Bilingual Interface — Arabic (RTL) and English (LTR) based on user language
Advanced Audit Analytics — DuPont, CCC, Benford's Law, Duplicate Payments, Concentration Risk
Dynamic Discovery of custom doctypes, custom fields, and accounting dimensions
Works on ANY ERPNext site — no hardcoded account names

Advanced Audit Analytics

DuPont Analysis & Financial Ratios

Return on Equity (ROE) decomposition: Profit Margin x Asset Turnover x Equity Multiplier
Cash Conversion Cycle (CCC): DSO + DIO - DPO
Liquidity Ratios: Current Ratio, Quick Ratio, Cash Ratio
Working Capital metrics with health indicators

Fraud Detection

Benford's Law Analysis: First-digit distribution test with Chi-square statistic (critical value 15.51 at alpha=0.05, 8df) for both sales and purchase invoices
Duplicate Payment Detection: Same amount to same supplier within 7 days — automatic flagging
Weekend & Month-End Transactions: Unusual timing patterns that may indicate manipulation or weak controls

Concentration Risk

Customer Concentration: Top customer and Top 5 customer revenue dependency analysis
Supplier Concentration: Top supplier and Top 5 supplier purchase dependency analysis
Risk Level Assessment: Automatic high/medium/low classification

Year-over-Year Comparison

Revenue, gross profit, expenses, net profit, and invoice count growth rates
Same-period comparison with prior year

Screenshots

Access the dashboard at: /app/financial-audit

Dashboard Layout

Section	Description
KPI Cards	12 color-coded metrics (revenue, profit, margins, AR/AP, cash, inventory)
Balance Sheet	Summary by root type (Assets, Liabilities, Equity, Income, Expense)
Income Statement	Hierarchical P&L with expandable accounts
Charts	Monthly trends, daily sales, expense pie, cash flow bars, Benford's chart
Transaction Analysis	GL & Stock Ledger voucher type discovery
Customer/Product/Supplier	Top performers with collection rates
Returns Analysis	Sales & purchase returns by party
Aging Reports	AR/AP aging with color-coded badges
Bank & Cash	Individual account balances
Payment Analysis	By mode of payment and type
Inventory	Warehouse valuation + stock movement + dead stock detection
DuPont & CCC	Advanced financial ratios with visual metrics
Benford's Law	First-digit fraud detection chart with Chi-square test
Duplicate Payments	Automatic detection of suspicious duplicate payments
Concentration Risk	Customer & supplier dependency analysis
Weekend Transactions	Unusual timing pattern detection
AI Analysis	Secure AI report with anonymization (Puter/OpenAI/Custom)
PDF Export	One-click print-optimized full audit report
Custom Layout	Show/hide any section with persistent preferences

Bilingual Support

The dashboard automatically detects the user's language setting (frappe.boot.lang) and renders the entire interface in:

Arabic (العربية) — RTL layout, Cairo font, full Arabic labels
English — LTR layout, all labels translated

All charts, tables, KPIs, section titles, AI analysis, and risk assessments are fully bilingual.

Installation

Prerequisites

Frappe Bench (v15+)
ERPNext (v15+)
Python 3.10+
Node.js 18+

Install Steps

# 1. Get the app
bench get-app https://github.com/ahmedemamhatem/financial_audit.git

# 2. Install on your site
bench --site your-site.com install-app financial_audit

# 3. Build assets & migrate
bench build && bench migrate

# 4. Restart
bench restart

Access

Navigate to: /app/financial-audit

Required roles: System Manager or Accounts Manager

Architecture

financial_audit/
├── pyproject.toml                    # Package configuration (flit_core)
├── MANIFEST.in                       # Package manifest
├── requirements.txt                  # Dependencies (none external)
├── financial_audit/
│   ├── __init__.py                   # App version (0.0.1)
│   ├── hooks.py                      # App metadata & required_apps
│   ├── modules.txt                   # Module registration
│   └── financial_audit/
│       ├── doctype/
│       │   ├── financial_audit_settings/   # AI provider config (Single DocType)
│       │   └── ai_audit_log/               # AI request audit trail
│       └── page/
│           └── financial_audit/
│               ├── financial_audit.json   # Page definition & roles
│               ├── financial_audit.py     # Backend API + AI proxy (~1,500 lines)
│               ├── financial_audit.js     # Frontend dashboard (~2,200 lines)
│               └── financial_audit.css    # RTL/LTR responsive styles (~1,000 lines)

Total: ~4,700+ lines of code | Zero external dependencies

Data Sections (30+ Endpoints)

All data is fetched in a single API call via get_financial_audit_data(filters).

Financial KPIs

#	Metric	Source	Description
1	Revenue	GL Entry (root_type=Income)	Total income for period
2	COGS	GL Entry (account_type=Cost of Goods Sold)	Cost of goods sold
3	Gross Profit	Calculated	Revenue - COGS
4	Gross Margin	Calculated	Gross Profit / Revenue %
5	Total Expenses	GL Entry (root_type=Expense)	All operating expenses
6	Net Profit	Calculated	Revenue - Total Expenses
7	Net Margin	Calculated	Net Profit / Revenue %
8	AR Outstanding	GL Entry (account_type=Receivable)	Accounts receivable balance
9	AP Outstanding	GL Entry (account_type=Payable)	Accounts payable balance
10	Cash Balance	GL Entry (account_type=Bank/Cash)	Total liquid assets
11	Inventory Value	Bin table	Total stock valuation
12	Invoice Counts	Sales/Purchase Invoice	Transaction volume

Analysis Sections

#	Section	Key	Query Strategy
1	Income Statement	`income_accounts`, `expense_accounts`	GL + Account (root_type, is_group=0)
2	Monthly Trends	`monthly_trends`	GL grouped by YEAR/MONTH
3	Top Customers	`top_customers`	Sales Invoice GROUP BY customer
4	Top Products	`top_products`	SI Item GROUP BY item_code
5	Top Suppliers	`top_suppliers`	Purchase Invoice GROUP BY supplier
6	AR Aging	`ar_aging`	GL (account_type=Receivable)
7	AP Aging	`ap_aging`	GL (account_type=Payable)
8	Daily Sales	`daily_sales`	SI GROUP BY posting_date
9	Expense Breakdown	`expense_breakdown`	GL (root_type=Expense) GROUP BY parent_account
10	Cash Flow	`cash_flow`	Payment Entry GROUP BY month + type
11	Inventory	`inventory_by_warehouse`	Bin JOIN Warehouse
12	GL Voucher Discovery	`gl_voucher_summary`	DISTINCT voucher_type from GL Entry
13	Stock Voucher Discovery	`stock_voucher_summary`	DISTINCT voucher_type from Stock Ledger
14	Stock Movement	`stock_movement`	Top 20 items by value change
15	Balance Sheet	`balance_sheet`	GL grouped by root_type (cumulative)
16	Bank Balances	`bank_balances`	Individual bank/cash accounts
17	Sales Returns	`sales_returns`	Credit notes by customer
18	Purchase Returns	`purchase_returns`	Debit notes by supplier
19	Journal Entries	`journal_entries_summary`	JE grouped by voucher_type
20	Payment Modes	`payment_modes`	PE grouped by mode + payment_type
21	Stock Ageing	`stock_ageing`	Dead stock detection (oldest items)
22	Custom Doctypes	`custom_doctypes_analysis`	Dynamic discovery of ALL submittable doctypes
23	Installed Apps	`installed_apps`	Site app registry with versions

Advanced Audit Analytics

#	Section	Key	Description
24	Working Capital Metrics	`working_capital_metrics`	DSO, DPO, DIO, CCC, Current/Quick/Cash Ratio, DuPont ROE
25	YoY Growth	`yoy_growth`	Revenue, gross profit, expense, net profit, invoice count growth
26	Benford's Law	`benfords_law`	Chi-square first-digit analysis for sales & purchase invoices
27	Duplicate Payments	`duplicate_payments`	Same amount + same supplier within 7 days detection
28	Concentration Risk	`concentration_risk`	Top 1 & Top 5 customer/supplier revenue/purchase concentration
29	Weekend Transactions	`weekend_transactions`	Weekend and month-end transaction pattern analysis

Universal Compatibility

The dashboard is designed to work on any ERPNext site without modification:

How It Works

Feature	Approach
Account Discovery	Uses `root_type` (Asset/Liability/Equity/Income/Expense) and `account_type` (Receivable/Payable/Bank/Cash/COGS) — never hardcoded account names
Currency	Auto-detected via `erpnext.get_company_currency()`
Company	Falls back to user default or Global Defaults
Fiscal Year	Date range filters — no fiscal year dependency
Language	Auto-detected via `frappe.boot.lang` — Arabic (RTL) or English (LTR)
Custom Doctypes	Dynamically discovers ALL `is_submittable=1` doctypes with `company` field
Custom Fields	Detects custom fields added to GL Entry
Accounting Dimensions	Discovers configured accounting dimensions
Installed Apps	Lists all installed apps with versions
Transaction Types	Discovers via `DISTINCT voucher_type` from GL Entry & Stock Ledger

Supported Chart of Accounts

Standard ERPNext (any country)
Custom chart of accounts
Multi-company setups
Any language/locale

AI-Powered Analysis

The dashboard includes secure AI analysis with three provider options — managed via Financial Audit Settings (/app/financial-audit-settings).

AI Providers

Provider	API Key	Data Flow	Best For
Puter (Free)	Not required	Server builds anonymized prompt → Browser calls Puter AI	Demos, testing, small teams
OpenAI	Required	Fully server-side (anonymize → prompt → API call → response)	Production, enterprise
Custom Endpoint	Required	Same as OpenAI but to your own URL	Self-hosted LLMs, data residency

How It Works

Click the "AI Analysis" button (or "تحليل ذكي" in Arabic)
Backend fetches financial data, anonymizes all identifiable names (customers, suppliers, products, bank accounts → generic labels like "Customer A", "Supplier 1")
Prompt is built server-side with anonymized data
For OpenAI/Custom: backend calls the AI API directly and returns the response
For Puter: anonymized prompt is sent to the browser, which calls Puter AI client-side
AI returns a comprehensive financial report in the user's language
Every request is logged in AI Audit Log with user, timestamp, status, and provider

Data Anonymization

Before any data reaches an AI provider, the following identifiers are replaced:

Original	Anonymized As
Customer names	Customer A, Customer B, ...
Supplier names	Supplier 1, Supplier 2, ...
Product names	Product A, Product B, ...
Bank account names	Bank Account 1, Bank Account 2, ...
Cost center names	Cost Center 1, Cost Center 2, ...
Company name (optional)	The Company

All financial amounts, ratios, and percentages remain intact for accurate analysis.

AI Analysis Covers

Financial Health Score — Rating out of 100 with multi-factor weighted scoring
DuPont Analysis — ROE decomposition with component analysis
Cash Conversion Cycle — DSO/DPO/DIO impact on liquidity
Fraud & Risk Analysis — Benford's Law, duplicate payments, weekend transactions
Concentration Risk — Customer and supplier dependency
Year-over-Year Comparison — Growth and contraction trends
Cash Flow Analysis — Liquidity and obligation coverage
Inventory Analysis — Dead stock and management issues
Returns Analysis — Return rates and profitability impact
SWOT Analysis — Financial strengths, weaknesses, opportunities, threats
Actionable Recommendations — 10 prioritized suggestions
Early Warning Signs — Future problem indicators

AI Settings

Configure at /app/financial-audit-settings:

Setting	Description
Enable AI Analysis	Master on/off toggle
AI Provider	Puter (Free) / OpenAI / Custom Endpoint
API Key	Encrypted storage (required for OpenAI/Custom)
Model Name	Default: gpt-4o-mini
Max Requests Per User Per Day	Rate limit (default: 20)
Anonymize Data	Replace names with generic labels before AI
Anonymize Company Name	Also replace the company name

PDF Export

Generate professional, print-ready audit reports with a single click.

How It Works

Click the "Export PDF" button (or "تصدير PDF" in Arabic)
All collapsed sections automatically expand for a complete report
Frappe UI chrome (navbar, sidebar) is hidden for a clean layout
Browser print dialog opens — save as PDF or send to printer
Dashboard restores to its previous state after printing

PDF Features

Full color preservation — KPI colors, badges, and charts print exactly as displayed
Optimized layout — 6-column KPI grid, 4-column metrics, proper table sizing
Smart section control — hidden sections (via Custom Layout) stay hidden in PDF
Automatic filename — includes company name and date range
All sections expanded — no collapsed/hidden content in the export

Custom Dashboard Layouts

Personalize which sections appear on your dashboard — preferences are saved per browser.

How It Works

Click the "Customize Layout" button (or "تخصيص العرض" in Arabic)
A dialog shows checkboxes for all 27 dashboard sections
Toggle sections on/off — use Show All, Hide All, or Reset for bulk actions
Click Save — preferences persist across sessions via localStorage

Layout Features

27 toggleable sections — every data table, chart, and analytics section
Instant apply — sections show/hide immediately after saving
Persistent preferences — saved per browser, survives page refreshes and logouts
PDF-aware — hidden sections stay hidden in PDF exports too
Smart divider — advanced audit divider auto-hides when all advanced sections are hidden
Bilingual dialog — fully translated in Arabic and English

Filters

Filter	Type	Default	Description
Company	Link (Company)	User's default company	Filter all data by company
From Date	Date	Year start	Period start date
To Date	Date	Today	Period end date

Changing any filter automatically refreshes all sections, charts, and KPIs.

Visual Components

Charts (Apache ECharts 5.5)

Chart	Type	Description
Monthly Trends	Bar	Revenue vs Expenses by month
Daily Sales	Line	Sales volume with area fill
Expense Breakdown	Donut	Top 10 expense categories
Cash Flow	Bar	Receipts vs Payments by month
Benford's Law	Bar + Line	First-digit distribution vs expected

Badges

Badge	Colors	Usage
Aging	Green (0-30d) / Yellow (31-60d) / Red (60+d)	AR/AP aging, stock ageing
Account Type	Blue (Bank) / Green (Cash)	Bank balances section
Payment Type	Green (Receive) / Red (Pay)	Payment modes section
Risk Level	Green (Low) / Yellow (Medium) / Red (High)	Benford, duplicates, concentration

Collection Rate Bar

Visual progress bar showing customer payment collection percentage:

Green: 70%+ collection rate
Yellow: 40-69% collection rate
Red: Below 40% collection rate

Security & Permissions

Page access: Restricted to System Manager and Accounts Manager roles
Backend: Uses @frappe.whitelist() with standard Frappe permission checks
Data isolation: All queries filter by company — multi-tenant safe
AI data anonymization: All customer, supplier, product, and account names are replaced with generic labels before any AI provider receives data
Server-side prompt building: Prompts are constructed on the backend — no raw financial identifiers ever reach the browser for AI purposes
API key encryption: Stored using Frappe's encrypted Password field — never exposed in API responses
Rate limiting: Per-user daily request cap via Redis cache
Audit logging: Every AI request logged with user, company, timestamp, provider, and status via AI Audit Log doctype
Provider flexibility: Use Puter (free) for demos, OpenAI for production, or a self-hosted LLM for full data residency control

Technical Details

Backend (`financial_audit.py`)

Single entry point: get_financial_audit_data(filters) returns all 30+ sections
Universal queries: All SQL uses root_type, account_type, is_group — never hardcoded names
Performance: Single API call fetches everything; SQL queries use proper JOINs and indexes
Advanced analytics: Benford's Law with Chi-square test, DuPont decomposition, CCC calculation
Error handling: get_custom_doctypes_analysis() handles missing doctypes gracefully
Cancelled entries: All queries exclude is_cancelled = 0 and opening entries

Frontend (`financial_audit.js`)

Class-based: FinancialAuditDashboard with clean method separation
Full i18n: FA_TRANSLATIONS dictionary with 200+ keys in Arabic/English
Language detection: Automatic via frappe.boot.lang with t() helper method
ECharts 5.5: Interactive charts with tooltips and responsive sizing
Multi-provider AI: Puter (free), OpenAI, Custom endpoint — server-side anonymization and prompt building
Collapsible sections: Toggle buttons for long tables
PDF export: window.print() with enhanced print CSS, auto-expand, Frappe chrome hidden
Custom layouts: Section registry with localStorage persistence, Frappe dialog UI
Multi-factor health score: Weighted scoring across profitability, liquidity, collection, margins, and advanced metrics

Styling (`financial_audit.css`)

RTL + LTR: Automatic direction based on language
Clean design: Strong font colors, bold weights, clear size hierarchy
Cairo font: Arabic-optimized Google Font
Responsive grid: KPI cards adapt from 6 → 4 → 2 → 1 columns
Print/PDF optimized: Full @media print rules — hides Frappe chrome, expands sections, preserves colors
Breakpoints: 1200px, 768px, 480px

Roadmap

Contributing

Contributions are welcome! Please:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

License

This project is licensed under the MIT License — see the LICENSE file for details.

Author

Ahmed Emam

Email: [email protected]
GitHub: @ahmedemamhatem

Built with love for the Frappe Community

Works on any ERPNext site | Free AI included | Bilingual AR/EN

Name		Name	Last commit message	Last commit date
Latest commit History 25 Commits
financial_audit		financial_audit
.gitignore		.gitignore
MANIFEST.in		MANIFEST.in
README.md		README.md
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt

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Financial Audit Dashboard for ERPNext

Overview

Key Highlights

Advanced Audit Analytics

DuPont Analysis & Financial Ratios

Fraud Detection

Concentration Risk

Year-over-Year Comparison

Screenshots

Dashboard Layout

Bilingual Support

Installation

Prerequisites

Install Steps

Access

Architecture

Data Sections (30+ Endpoints)

Financial KPIs

Analysis Sections

Advanced Audit Analytics

Universal Compatibility

How It Works

Supported Chart of Accounts

AI-Powered Analysis

AI Providers

How It Works

Data Anonymization

AI Analysis Covers

AI Settings

PDF Export

How It Works

PDF Features

Custom Dashboard Layouts

How It Works

Layout Features

Filters

Visual Components

Charts (Apache ECharts 5.5)

Badges

Collection Rate Bar

Security & Permissions

Technical Details

Backend (financial_audit.py)

Frontend (financial_audit.js)

Styling (financial_audit.css)

Roadmap

Contributing

License

Author

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Backend (`financial_audit.py`)

Frontend (`financial_audit.js`)

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