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Financial Document Analysis with Lexa

Transform complex financial documents into structured, analyzable data in seconds. Lexa’s AI-powered parsing extracts critical financial metrics, tables, and insights with highest accuracy.

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Why Lexa for Financial Analysis?

99.8% Table Accuracy

Preserve complex financial tables and calculations

Regulatory Compliance

SOC 2 compliant with enterprise security

Multi-Format Support

Process 10-K filings, earnings reports, and prospectuses

Real-Time Processing

Extract insights from 100+ page reports in under 30 seconds

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Supported Financial Documents

  • SEC Filings (10-K, 10-Q, 8-K, proxy statements)
  • Earnings Reports and quarterly statements
  • Annual Reports with complex layouts
  • Financial Statements (income, balance sheet, cash flow)
  • Research Reports from analysts
  • Prospectuses and offering memoranda
  • Credit Reports and risk assessments

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Quick Start: Parse SEC 10-K Filing

Transform a complex SEC filing into structured data: 
from cerevox import Lexa

# Initialize client
client = Lexa(api_key="your-api-key")

# Parse SEC 10-K filing
documents = client.parse("tesla_10k.pdf")
doc = documents[0]

# Extract financial metrics
revenue_chunks = doc.search_content("revenue|total revenue")
profit_chunks = doc.search_content("net income|profit")

print(f"Document: {doc.filename}")
print(f"Pages: {doc.total_pages}")
print(f"Tables found: {len(doc.tables)}")
print(f"Financial metrics extracted: {len(revenue_chunks + profit_chunks)}")

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Advanced Financial Analysis Patterns

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Extract Financial Tables

Process complex financial statements and preserve structure: 
from cerevox import AsyncLexa
import pandas as pd

async def extract_financial_tables(filing_path):
    async with AsyncLexa(api_key="your-api-key") as client:
        documents = await client.parse(filing_path)
        doc = documents[0]
        
        # Convert tables to pandas DataFrames
        financial_tables = []
        for table in doc.tables:
            try:
                # Convert table to structured data
                df = pd.DataFrame(table.to_dict())
                
                # Identify table type based on content
                table_type = identify_table_type(df)
                
                financial_tables.append({
                    'type': table_type,
                    'data': df,
                    'page': table.page_number,
                    'rows': len(df),
                    'columns': len(df.columns)
                })
                
            except Exception as e:
                print(f"Table processing error: {e}")
                continue
        
        return financial_tables

def identify_table_type(df):
    """Identify financial table type based on content"""
    columns_text = ' '.join(df.columns.astype(str)).lower()
    
    if 'revenue' in columns_text or 'income' in columns_text:
        return 'income_statement'
    elif 'assets' in columns_text or 'liabilities' in columns_text:
        return 'balance_sheet'
    elif 'cash flow' in columns_text or 'operating activities' in columns_text:
        return 'cash_flow'
    else:
        return 'other_financial'

# Usage
tables = await extract_financial_tables("company_10k.pdf")
for table in tables:
    print(f"Found {table['type']} with {table['rows']} rows")

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Financial Metrics Extraction

Build a comprehensive financial metrics extractor: 
import re
from typing import Dict, List

class FinancialMetricsExtractor:
    def __init__(self, api_key: str):
        self.client = AsyncLexa(api_key=api_key)
        
        # Define financial metric patterns
        self.metric_patterns = {
            'revenue': [
                r'total revenue[:\s]+\$?([\d,\.]+)',
                r'net sales[:\s]+\$?([\d,\.]+)',
                r'revenue[:\s]+\$?([\d,\.]+)\s*(million|billion)?'
            ],
            'profit': [
                r'net income[:\s]+\$?([\d,\.]+)',
                r'profit[:\s]+\$?([\d,\.]+)',
                r'earnings[:\s]+\$?([\d,\.]+)'
            ],
            'assets': [
                r'total assets[:\s]+\$?([\d,\.]+)',
                r'assets[:\s]+\$?([\d,\.]+)'
            ],
            'debt': [
                r'total debt[:\s]+\$?([\d,\.]+)',
                r'long.term debt[:\s]+\$?([\d,\.]+)'
            ]
        }
    
    async def extract_metrics(self, document_path: str) -> Dict:
        """Extract financial metrics from document"""
        async with self.client:
            documents = await self.client.parse(document_path)
            doc = documents[0]
            
            metrics = {}
            content = doc.content.lower()
            
            for metric_type, patterns in self.metric_patterns.items():
                values = []
                for pattern in patterns:
                    matches = re.findall(pattern, content, re.IGNORECASE)
                    values.extend(matches)
                
                # Clean and convert values
                cleaned_values = []
                for value in values:
                    if isinstance(value, tuple):
                        value = value[0]  # Extract number from regex group
                    
                    # Remove commas and convert to float
                    try:
                        clean_value = float(value.replace(',', ''))
                        cleaned_values.append(clean_value)
                    except ValueError:
                        continue
                
                metrics[metric_type] = cleaned_values
            
            return {
                'document': document_path,
                'metrics': metrics,
                'summary': self._summarize_metrics(metrics)
            }
    
    def _summarize_metrics(self, metrics: Dict) -> Dict:
        """Generate summary statistics"""
        summary = {}
        for metric_type, values in metrics.items():
            if values:
                summary[metric_type] = {
                    'count': len(values),
                    'max': max(values),
                    'min': min(values),
                    'avg': sum(values) / len(values)
                }
        return summary

# Usage
extractor = FinancialMetricsExtractor("your-api-key")
results = await extractor.extract_metrics("annual_report.pdf")

print(f"Revenue mentions: {len(results['metrics']['revenue'])}")
print(f"Profit data points: {len(results['metrics']['profit'])}")

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RAG for Financial Q&A

Build a financial document Q&A system: 
from cerevox import AsyncLexa
import openai
from typing import List

class FinancialRAGSystem:
    def __init__(self, cerevox_api_key: str, openai_api_key: str):
        self.cerevox_client = AsyncLexa(api_key=cerevox_api_key)
        openai.api_key = openai_api_key
        self.document_chunks = []
    
    async def ingest_documents(self, financial_docs: List[str]):
        """Ingest and chunk financial documents"""
        async with self.cerevox_client:
            documents = await self.cerevox_client.parse(financial_docs)
            
            # Create vector-ready chunks
            all_chunks = documents.get_all_text_chunks(
                target_size=1000,  # Optimal for financial context
                tolerance=0.15
            )
            
            # Add metadata for better retrieval
            for i, chunk in enumerate(all_chunks):
                chunk_with_metadata = {
                    'id': f'chunk_{i}',
                    'content': chunk,
                    'document': documents[i // len(all_chunks) * len(documents)].filename,
                    'embedding': await self._get_embedding(chunk)
                }
                self.document_chunks.append(chunk_with_metadata)
        
        print(f"Ingested {len(self.document_chunks)} chunks from financial documents")
    
    async def _get_embedding(self, text: str):
        """Get embedding for text chunk"""
        response = await openai.Embedding.acreate(
            model="text-embedding-ada-002",
            input=text
        )
        return response['data'][0]['embedding']
    
    async def query_financials(self, question: str, top_k: int = 5):
        """Answer questions about financial documents"""
        # Get question embedding
        question_embedding = await self._get_embedding(question)
        
        # Find relevant chunks (simplified - use proper vector DB in production)
        relevant_chunks = self._find_similar_chunks(
            question_embedding, 
            top_k
        )
        
        # Build context for LLM
        context = "\n\n".join([
            f"Document: {chunk['document']}\nContent: {chunk['content']}"
            for chunk in relevant_chunks
        ])
        
        # Generate answer
        response = await openai.ChatCompletion.acreate(
            model="gpt-4",
            messages=[
                {
                    "role": "system",
                    "content": "You are a financial analyst assistant. Answer questions based on the provided financial document context. Be precise and cite specific numbers when available."
                },
                {
                    "role": "user",
                    "content": f"Context:\n{context}\n\nQuestion: {question}"
                }
            ]
        )
        
        return {
            'answer': response.choices[0].message.content,
            'sources': [chunk['document'] for chunk in relevant_chunks],
            'relevant_chunks': len(relevant_chunks)
        }
    
    def _find_similar_chunks(self, query_embedding, top_k):
        """Find most similar chunks (simplified implementation)"""
        # In production, use a proper vector database like Pinecone or Weaviate
        import numpy as np
        
        similarities = []
        for chunk in self.document_chunks:
            similarity = np.dot(query_embedding, chunk['embedding'])
            similarities.append((similarity, chunk))
        
        # Sort by similarity and return top k
        similarities.sort(key=lambda x: x[0], reverse=True)
        return [chunk for _, chunk in similarities[:top_k]]

# Usage
rag_system = FinancialRAGSystem(
    cerevox_api_key="your-cerevox-key",
    openai_api_key="your-openai-key"
)

# Ingest financial documents
await rag_system.ingest_documents([
    "tesla_10k_2023.pdf",
    "tesla_q3_earnings.pdf",
    "tesla_annual_report.pdf"
])

# Ask questions
result = await rag_system.query_financials(
    "What was Tesla's revenue growth in 2023?"
)

print(f"Answer: {result['answer']}")
print(f"Sources: {result['sources']}")

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Real-World Financial Use Cases

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1. Investment Research Automation

async def automated_investment_research(company_docs):
    """Automate investment research from company filings"""
    
    async with AsyncLexa(api_key="your-api-key") as client:
        documents = await client.parse(company_docs)
        
        research_data = {}
        for doc in documents:
            # Extract key investment metrics
            research_data[doc.filename] = {
                'revenue_growth': extract_growth_metrics(doc, 'revenue'),
                'profit_margins': extract_margins(doc),
                'risk_factors': doc.search_content('risk factor|material risk'),
                'management_discussion': extract_md_a(doc),
                'financial_highlights': extract_highlights(doc)
            }
        
        return research_data

def extract_growth_metrics(doc, metric):
    """Extract growth metrics from financial documents"""
    # Search for year-over-year comparisons
    growth_patterns = [
        f'{metric}.*increased.*(\d+\.?\d*)%',
        f'{metric}.*growth.*(\d+\.?\d*)%',
        f'{metric}.*up.*(\d+\.?\d*)%'
    ]
    
    growth_data = []
    for pattern in growth_patterns:
        matches = re.findall(pattern, doc.content, re.IGNORECASE)
        growth_data.extend(matches)
    
    return growth_data

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2. Risk Assessment Pipeline

class FinancialRiskAnalyzer:
    def __init__(self, api_key: str):
        self.client = AsyncLexa(api_key=api_key)
    
    async def analyze_risk_factors(self, financial_docs: List[str]):
        """Comprehensive risk factor analysis"""
        
        async with self.client:
            documents = await self.client.parse(financial_docs)
            
            risk_analysis = {}
            for doc in documents:
                # Extract risk sections
                risk_chunks = doc.search_content("risk factor|risks|uncertainties")
                
                # Categorize risks
                categorized_risks = self._categorize_risks(risk_chunks)
                
                # Calculate risk scores
                risk_scores = self._calculate_risk_scores(categorized_risks)
                
                risk_analysis[doc.filename] = {
                    'total_risk_mentions': len(risk_chunks),
                    'risk_categories': categorized_risks,
                    'risk_scores': risk_scores,
                    'high_priority_risks': self._identify_high_priority(risk_chunks)
                }
            
            return risk_analysis
    
    def _categorize_risks(self, risk_chunks):
        """Categorize financial risks"""
        categories = {
            'market_risk': ['market', 'competition', 'demand'],
            'operational_risk': ['operations', 'supply chain', 'manufacturing'],
            'financial_risk': ['liquidity', 'credit', 'debt', 'cash flow'],
            'regulatory_risk': ['regulation', 'compliance', 'legal'],
            'technology_risk': ['cyber', 'technology', 'data breach']
        }
        
        categorized = {cat: [] for cat in categories}
        
        for chunk in risk_chunks:
            chunk_lower = chunk.lower()
            for category, keywords in categories.items():
                if any(keyword in chunk_lower for keyword in keywords):
                    categorized[category].append(chunk)
        
        return categorized

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3. Earnings Call Analysis

async def analyze_earnings_transcripts(transcript_files):
    """Analyze earnings call transcripts for sentiment and insights"""
    
    async with AsyncLexa(api_key="your-api-key") as client:
        documents = await client.parse(transcript_files)
        
        earnings_insights = {}
        for doc in documents:
            # Extract Q&A sections
            qa_sections = doc.search_content("questions and answers|q&a")
            
            # Management guidance
            guidance = doc.search_content("guidance|outlook|forecast")
            
            # Key metrics mentioned
            metrics = extract_financial_metrics(doc.content)
            
            earnings_insights[doc.filename] = {
                'qa_insights': len(qa_sections),
                'forward_guidance': guidance,
                'key_metrics': metrics,
                'sentiment_indicators': analyze_sentiment(doc.content)
            }
        
        return earnings_insights

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Performance Benchmarks

Lexa delivers exceptional performance for financial document processing:

Processing Speed

30 seconds average for 100+ page 10-K filing

Table Accuracy

99.8% accuracy on complex financial tables

Batch Throughput

500+ documents/hour with async processing

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Integration Examples

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Pinecone Vector Database

import pinecone
from cerevox import AsyncLexa

# Initialize Pinecone
pinecone.init(api_key="your-pinecone-key", environment="your-env")
index = pinecone.Index("financial-docs")

async def index_financial_documents(document_paths):
    async with AsyncLexa(api_key="your-api-key") as client:
        documents = await client.parse(document_paths)
        
        # Get optimized chunks for financial documents
        chunks = documents.get_all_text_chunks(
            target_size=1000,  # Good for financial context
            tolerance=0.1
        )
        
        # Create embeddings and upsert to Pinecone
        vectors = []
        for i, chunk in enumerate(chunks):
            vector = {
                'id': f'financial_chunk_{i}',
                'values': get_embedding(chunk),
                'metadata': {
                    'content': chunk,
                    'document_type': 'financial',
                    'source': documents[i // len(chunks) * len(documents)].filename
                }
            }
            vectors.append(vector)
        
        # Batch upsert to Pinecone
        index.upsert(vectors=vectors)
        
        return f"Indexed {len(vectors)} financial document chunks"

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Security & Compliance

Lexa is SOC 2 Type II certified and provides enterprise-grade security for sensitive financial documents.
  • Data Encryption: End-to-end encryption in transit and at rest
  • Access Controls: Role-based access with audit logging
  • Compliance: SOC 2, GDPR, and financial industry standards
  • Data Residency: Control where your financial data is processed

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Next Steps

Ready to transform your financial document analysis?

Get Started

Process your first financial document in 3 minutes

API Reference

Explore advanced parsing methods

Vector Database Guide

Build financial RAG applications

Performance Tips

Optimize for large financial datasets