Use Cases - CAS Parser

Portfolio Tracking Apps

Challenge

Users have investments across multiple platforms (Zerodha, Groww, Kuvera, etc.). Manually entering transaction history is tedious and error-prone.

Solution

Let users upload their CAS PDF to import their entire portfolio history in seconds.

Implementation

# User uploads CAS PDF
cas_data = casparser.parse(pdf_file, password)

# Import to your database
for folio in cas_data['mutual_funds']:
    for scheme in folio['schemes']:
        for txn in scheme['transactions']:
            db.insert_transaction({
                'user_id': user.id,
                'date': txn['date'],
                'scheme': scheme['scheme'],
                'amount': txn['amount'],
                'units': txn['units'],
                'nav': txn['nav']
            })

Benefits

70% faster onboarding — Import years of data in seconds
100% accurate — No manual entry errors
Better UX — Users see complete portfolio immediately

Customers

Angel One, Neo Wealth, Dezerv

Wealth Management Platforms

Challenge

Advisors need to understand client portfolios before making recommendations. Clients often have scattered investments across 5-10 platforms.

Solution

Request CAS via KFintech API, parse automatically, and generate consolidated reports.

Implementation

# Step 1: Request CAS generation
casparser.generate_cas(
    email=client.email,
    pan=client.pan,
    from_date="2020-01-01",
    to_date="2024-12-31"
)

# Step 2: Client receives CAS in email, uploads to portal
# Step 3: Parse and analyze
cas_data = casparser.parse(pdf_file, password)

# Generate asset allocation report
allocation = calculate_allocation(cas_data)
recommendations = generate_recommendations(allocation, client.risk_profile)

Benefits

Comprehensive view — All investments in one place
Faster advice — Automated portfolio analysis
Better decisions — Data-driven recommendations

Customers

Grip Invest, KFintech

Tax Filing Platforms

Challenge

Calculating capital gains requires transaction-level data. Users don’t have organized records.

Solution

Parse CAS to extract all buy/sell transactions, calculate FIFO/LIFO gains automatically.

Implementation

cas_data = casparser.parse(pdf_file, password)

# Extract equity transactions
equity_txns = []
for account in cas_data['demat_accounts']:
    for equity in account['equities']:
        for txn in equity.get('transactions', []):
            equity_txns.append({
                'isin': equity['isin'],
                'date': txn['date'],
                'type': txn['type'],
                'quantity': txn['quantity'],
                'price': txn['price']
            })

# Calculate capital gains
gains = calculate_capital_gains(equity_txns, method='FIFO')

Benefits

Accurate ITR — No missed transactions
Time savings — Automated gain calculation
Audit trail — Complete transaction history

Credit Underwriting

Challenge

Verifying borrower’s financial status requires authentic portfolio data. Self-reported data is unreliable.

Solution

Request CAS directly from CDSL via OTP, verify authenticity, assess creditworthiness.

Implementation

# Step 1: Request OTP from CDSL
session = casparser.cdsl_fetch(pan=borrower.pan, dob=borrower.dob)

# Step 2: Borrower enters OTP
cas_urls = casparser.cdsl_verify(session_id, otp)

# Step 3: Parse and assess
cas_data = casparser.parse_from_url(cas_urls['detailed'])

# Calculate credit score factors
total_portfolio_value = cas_data['summary']['total_value']
equity_exposure = calculate_equity_percentage(cas_data)
loan_eligibility = assess_eligibility(total_portfolio_value, equity_exposure)

Benefits

Tamper-proof — Direct from depository
Real-time — Instant verification
Accurate — No self-reporting bias

Financial Research

Challenge

Analyzing investment trends requires aggregated portfolio data. Individual CAS files are unstructured.

Solution

Parse thousands of CAS files, aggregate data, identify patterns.

Implementation

# Batch processing
results = []
for cas_file in cas_files:
    data = casparser.parse(cas_file, password)
    results.append({
        'total_value': data['summary']['total_value'],
        'equity_count': len(data['demat_accounts'][0]['equities']),
        'mf_count': sum(len(f['schemes']) for f in data['mutual_funds']),
        'age_group': classify_age(data['investor']['name'])
    })

# Aggregate insights
avg_portfolio_value = statistics.mean(r['total_value'] for r in results)
popular_stocks = Counter(e['isin'] for r in results for e in r['equities']).most_common(10)

Benefits

Large-scale analysis — Process thousands of CAS files
Market insights — Identify trends and patterns
Research reports — Data-driven publications

Robo-Advisors

Challenge

Providing personalized advice requires understanding current portfolio allocation.

Solution

Parse CAS, analyze allocation, suggest rebalancing.

Implementation

cas_data = casparser.parse(pdf_file, password)

# Current allocation
current = {
    'equity': calculate_equity_value(cas_data),
    'debt': calculate_debt_value(cas_data),
    'gold': calculate_gold_value(cas_data)
}

# Target allocation (based on risk profile)
target = {'equity': 0.6, 'debt': 0.3, 'gold': 0.1}

# Rebalancing suggestions
suggestions = generate_rebalancing_plan(current, target)

Benefits

Personalized advice — Based on actual holdings
Automated rebalancing — Data-driven suggestions
Better outcomes — Optimized portfolios

Insurance Platforms

Challenge

Assessing insurance needs requires knowing existing coverage and assets.

Solution

Parse CAS to extract insurance policies and portfolio value, recommend coverage.

Implementation

cas_data = casparser.parse(pdf_file, password)

# Extract existing insurance
existing_coverage = sum(p['sum_assured'] for p in cas_data['insurance'])

# Calculate recommended coverage
portfolio_value = cas_data['summary']['total_value']
recommended_coverage = portfolio_value * 10  # 10x rule

# Gap analysis
coverage_gap = recommended_coverage - existing_coverage

Benefits

Accurate needs — Based on real assets
Gap identification — Underinsured users
Cross-sell — Targeted recommendations

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​Portfolio Tracking Apps

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​Customers

​Wealth Management Platforms

​Challenge

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​Customers

​Tax Filing Platforms

​Challenge

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​Credit Underwriting

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​Financial Research

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​Robo-Advisors

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Quickstart

API Reference

Portfolio Tracking Apps

Challenge

Solution

Implementation

Benefits

Customers

Wealth Management Platforms

Challenge

Solution

Implementation

Benefits

Customers

Tax Filing Platforms

Challenge

Solution

Implementation

Benefits

Credit Underwriting

Challenge

Solution

Implementation

Benefits

Financial Research

Challenge

Solution

Implementation

Benefits

Robo-Advisors

Challenge

Solution

Implementation

Benefits

Insurance Platforms

Challenge

Solution

Implementation

Benefits

Next Steps