AI · Financial Intelligence · Annual Reports

Annual Reports.
Analysed by AI.
Understood in Minutes.

ARAWAI — Annual Report Analysis with AI — is a next-generation platform that applies large language models and financial modelling to transform complex annual reports into precise, actionable intelligence.

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100+ Financial Metrics
AI Powered Analysis
<5 min Full Report Insight
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What is ARAWAI?

From Dense Disclosure
to Clear Intelligence

Annual reports are among the most information-dense documents in finance — running to hundreds of pages of financial statements, management commentary, risk disclosures, and governance data. Extracting meaningful insight from them is time-consuming, error-prone, and demands deep expertise.

ARAWAI changes this. By combining state-of-the-art large language models with rigorous financial frameworks — including DuPont decomposition, ratio analysis, and narrative sentiment assessment — ARAWAI delivers the depth of a seasoned analyst in a fraction of the time.

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Annual Report Input

Upload any publicly available annual report PDF

AI Analysis Engine

Multi-model LLM processing with financial frameworks

Actionable Insights

Structured outputs: ratios, trends, risks, commentary

Core Capabilities

Everything You Need to
Analyse Any Annual Report

ARAWAI brings together financial expertise and AI into one seamless workflow, covering every dimension of corporate performance analysis.

DuPont Financial Decomposition

Automatically breaks down return on equity into its constituent drivers — profitability, asset efficiency, and financial leverage — revealing the true sources of performance.

Comprehensive Ratio Analysis

Liquidity, solvency, profitability, and efficiency ratios computed automatically from the financial statements, with peer-context commentary.

Narrative Sentiment Analysis

Applies NLP to management commentary, risk disclosures, and strategic statements — detecting tone shifts, confidence indicators, and emerging concerns.

Multi-Year Trend Tracking

Compare financial performance across multiple reporting periods to identify trajectory, momentum shifts, and structural changes in the business.

Risk & Red Flag Detection

Flags financial red flags — rising leverage, deteriorating margins, liquidity stress, off-balance-sheet exposure — so nothing slips through the cracks.

Structured Report Output

Generates a structured analytical report with executive summary, financial tables, and interpretive commentary — ready for presentation or further research.

Process

How ARAWAI Works

A three-step workflow that takes you from raw corporate disclosure to deep financial insight.

01

Upload the Annual Report

Provide the URL or upload the PDF of any publicly listed company's annual report. ARAWAI supports reports from major global exchanges.

02

AI Processes the Document

The platform extracts, structures, and analyses the financial statements and narrative sections using advanced language models and financial frameworks.

03

Receive Actionable Analysis

Within minutes, receive a comprehensive analytical output covering ratios, trends, risks, and qualitative commentary — structured for decision-making.

Why ARAWAI

Built for Financial
Professionals and Researchers

Investment Analysts

Accelerate fundamental research across your coverage universe. Analyse a report in minutes, not hours.

Corporate Finance Teams

Benchmark competitors, track industry trends, and support M&A diligence with structured comparative analysis.

Academic Researchers

Scale financial statement studies across large firm samples without manual data extraction or transcription.

Credit & Risk Professionals

Identify early warning signals and deteriorating credit metrics across your portfolio with consistent methodology.

Business Students

Build financial analysis skills by seeing professional-grade breakdowns of real company reports, with explanations.

Auditors & Consultants

Perform rapid initial diagnostics on client financials before engaging in deeper qualitative work.

Ready to See ARAWAI in Action?

Access the live tool and upload your first annual report today — no setup required.

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Project Team

Meet the Researchers

ARAWAI was developed by accounting and finance academics at Bayes Business School, City St George's, University of London.

Professor Pawel Bilinski

Prof. Pawel Bilinski

Professor of Accounting

Director of the Centre for Financial Analysis and Reporting Research (CeFARR) and Research Director of the Faculty of Finance. Editor, Journal of Business Finance and Accounting.

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Dr Qirong Song

Dr Qirong Song

Lecturer in Accounting

Research interests include financial reporting, disclosure quality, and the intersection of accounting and capital markets. Member of Bayes Accounting Group.

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Dr Gitae Park

Dr Gitae Park

Lecturer in Accounting

Specialist in empirical accounting research with a focus on financial reporting, auditing, and corporate governance. PhD from Lancaster University.

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Dr Guangyu Li

Dr Guangyu Li

Lecturer in Accounting

Research interests span financial accounting, earnings quality, and the application of machine learning to accounting and finance problems.

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Omid Nouri

Omid Nouri

PhD Student in Accounting

Doctoral researcher in the Accounting Group at Bayes Business School, exploring the intersection of financial reporting, AI, and corporate disclosure.

Contact

Get in Touch

Have questions about ARAWAI, interested in collaboration, or want to discuss enterprise access? We'd love to hear from you.

Email pawel.bilinski.1@city.ac.uk
Affiliation Bayes Business School, City St George's, University of London

We typically respond within two business days.