fin_crawl

Your autonomous analyst for detecting corporate distress.

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FINCRAWL: Autonomous Financial Early-Warning System πŸ“ Project Description

FINCRAWL is an autonomous financial early-warning system that reads SEC filings, corporate disclosures, and financial news to detect signals of distress such as bankruptcy risk, fraud indicators, material weaknesses, and going-concern alerts. Our mission is to make hidden financial risks visible before they turn into market disasters.

πŸ’‘ Inspiration

Every year, companies collapse long before investors fully understand what went wrong. The red flags were always there, but buried deep inside:

200-page 10-K filings and obscure footnotes

Auditor comments and sudden executive resignations

Hidden litigation disclosures and restatements

Most financial analysts simply don’t have the time or tools to parse this immense volume of data.

We asked ourselves:

What if an AI could read every filing, disclosure, and news headline β€” and warn us the moment something looks off?

That realization became FINCRAWL.

🧠 What It Does

FINCRAWL is a full-pipeline financial intelligence platform that performs three critical functions:

1️⃣ Autonomous Ingestion & Normalization

We built crawlers to continuously pull:

SEC EDGAR filings (10-K, 10-Q, 8-K)

Financial news RSS feeds and corporate disclosures

All documents are extracted, cleaned, and normalized into structured JSON for downstream processing.

2️⃣ RAG + Vector Search Engine

We use SentenceTransformers and ChromaDB to convert filings into semantic embeddings.

Our structured Retrieval-Augmented Generation (RAG) system allows analysts to:

Answer complex questions

Summarize material changes over time

Compare historical disclosures

Instantly retrieve risk-related paragraphs

All outputs are source-grounded and fully auditable.

3️⃣ Autonomous Risk Scoring

We designed a hybrid scoring engine that detects red-flag indicators:

β€œGoing concern” warnings

β€œMaterial weakness in internal controls”

Litigation / regulatory risk language

Sudden restatements

Abrupt tone shifts in MD&A

The final risk score is:

Risk Score

𝑀 β„Ž 𝐻 + 𝑀 𝑠 𝑆 + 𝑀 π‘Ÿ 𝑅 Risk Score=w h ​

H+w s ​

S+w r ​

R

Where:

𝐻 H = heuristic score

𝑆 S = semantic anomaly score

𝑅 R = RAG-based red-flag relevance

πŸ–₯️ Analyst Dashboard

A modern React + Tailwind dashboard displays:

Company risk timelines

Highlighted risk excerpts

Vector-search-powered queries

Latest filings and risk scores

It functions like a lightweight financial intelligence terminal.

πŸ› οΈ How We Built It

We engineered the pipeline for simplicity and speed:

Crawl β†’ Parse/Clean Filings β†’ Chunk β†’ Embeddings β†’ ChromaDB Storage β†’ Risk Scoring β†’ FastAPI β†’ React Dashboard

All components run locally using a simple Makefile, enabling rapid development without complex infrastructure.

🚧 Challenges We Ran Into

  1. Parsing Inconsistent Filings

SEC filings have wildly inconsistent formatting (HTML, PDFs, tables as images). Normalizing them into clean text was a major hurdle.

  1. Risk Signals Are Rare

Most filings contain no red flags. We overcame this by using hybrid heuristics + semantic similarity instead of pure classification.

  1. Latency in RAG Queries

Embedding large filings is computationally heavy. We optimized using smart chunking, caching, and Celery workers.

  1. Designing Explainable Risk Output

We needed transparency behind every alert. We implemented snippet highlighting to surface the exact sentence responsible for a flag.

πŸ† Accomplishments We’re Proud Of

Building a functional end-to-end RAG system for complex financial documents

Creating the Explainable Hybrid Risk Score

Designing a polished, interactive analyst dashboard

Shipping a complete, locally runnable intelligence product within hackathon time limits

πŸ“š What We Learned

This project taught us:

How to build a full Retrieval-Augmented Generation pipeline

Advanced techniques for parsing + normalizing financial filings

Real-world vector database applications

Importance of hybrid (rules + ML) scoring in sparse-label environments

Designing explainable, auditable NLP systems

FINCRAWL showed us that AI can dramatically enhance transparency and preempt major financial failures.

πŸš€ What’s Next for FINCRAWL

We aim to evolve FINCRAWL into a production-grade intelligence tool:

Earnings call transcription using Whisper

Global regulatory coverage (SEBI, FCA, ASX)

Advanced anomaly detection (autoencoders)

Multi-agent Analyst Copilot for conversational risk analysis

Real-time Slack/Email alerting system

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