HDFC CC bill parser

Parse and extract transaction data from HDFC Bank Infinia credit card statements to CSV format.

Note: This tool currently supports only HDFC Infinia credit card statements from September 2025 onwards. Earlier statement formats are not supported.

Features

• Extracts transaction details: date, description, reward points, amount
• Handles both domestic and international transactions
• Supports password-protected PDFs
• Multiple PDFs can be parsed and collated into single CSV
• Summary mode: View spending totals without CSV output
• Category breakdown: Classify spending by categories using a JSON config file

Requirements

• Rust (for building from source)
• HDFC Infinia credit card statements (September 2025+ format)

Installation

git clone https://github.com/joeirimpan/hdfc-cc-parser-rs.git
cd hdfc-cc-parser-rs
cargo build --release

Usage

Basic CSV Export

# Single file
./target/release/hdfc-cc-parser-rs \
  --file statement.pdf \
  --name "YOUR NAME" \
  --password "PDF_PASSWORD"

# Directory of statements
./target/release/hdfc-cc-parser-rs \
  --dir ./statements \
  --name "YOUR NAME" \
  --password "PDF_PASSWORD" \
  --addheaders

Summary Mode

View spending summary without CSV output:

./target/release/hdfc-cc-parser-rs \
  --dir ./statements \
  --name "YOUR NAME" \
  --password "PDF_PASSWORD" \
  --summary

Output:

═══════════════════════════════════════════
              SUMMARY
═══════════════════════════════════════════
Total Spent:          ₹      10000.00
Bill Payment:         ₹      15000.00
Points Earned:                    100
Transactions:                      50

Category Breakdown

Add --categories with a JSON file to see spending by category:

./target/release/hdfc-cc-parser-rs \
  --dir ./statements \
  --name "YOUR NAME" \
  --password "PDF_PASSWORD" \
  --summary \
  --categories examples/categories.json

Output:

═══════════════════════════════════════════
              SUMMARY
═══════════════════════════════════════════
Total Spent:          ₹      10000.00
Bill Payment:         ₹      15000.00
Points Earned:                    100
Transactions:                      50

───────────────────────────────────────────
         CATEGORY BREAKDOWN
───────────────────────────────────────────
Travel                ₹    3000.00  ( 30.0%)
Shopping              ₹    2500.00  ( 25.0%)
Food & Dining         ₹    2000.00  ( 20.0%)
Uncategorized         ₹    2500.00  ( 25.0%)
───────────────────────────────────────────

Categories JSON Format

Create a JSON file mapping category names to merchant patterns:

{
  "Food & Dining": ["SWIGGY", "ZOMATO", "BLINKIT", "DOMINOS"],
  "Travel": ["UBER", "OLA", "IRCTC", "MAKEMYTRIP"],
  "Shopping": ["AMAZON", "FLIPKART", "MYNTRA"],
  "Entertainment": ["NETFLIX", "SPOTIFY", "GOOGLE PLAY"],
  "Utilities": ["JIO", "AIRTEL", "BESCOM"],
  "Finance": ["IGST", "GST", "FINANCE CHARGES"]
}

Patterns are matched case-insensitively against transaction descriptions.

CLI Options

Option	Description
--file <path>	Path to a single PDF statement
--dir <path>	Path to directory containing PDF statements
--name <name>	Cardholder name as it appears on the statement (required)
--password <pwd>	PDF password (if encrypted)
--addheaders	Add CSV header row to output
--sortformat <fmt>	Date format in filenames for sorting (e.g., %d-%m-%Y)
--summary	Show summary only (no CSV output)
--categories <file>	JSON file for category breakdown (requires --summary)

Performance

A similar Python implementation using tabula-py took 70s+ to process 8 PDFs. This Rust implementation processes the same in ~0.02s.

Spending Analyzer

A Python script for detailed spending analysis with visualizations.

Features

• Monthly breakdown by category
• Month-over-month trend analysis
• Category trend comparison (first half vs second half)
• Spending volatility metrics
• Visual charts (bar graphs, stacked category charts)
• Support for custom billing cycle start dates (1-31)

Usage

# Basic analysis (calendar months)
python analyze_spending.py --csv dump.csv --categories categories.json

# Custom billing cycle (e.g., 16th to 16th)
python analyze_spending.py --csv dump.csv --categories categories.json --cycle-start 16

# Skip graph generation
python analyze_spending.py --csv dump.csv --categories categories.json --no-graph

Options

Option	Description
--csv <path>	Path to transaction CSV file (default: dump.csv)
--categories <path>	Path to categories JSON file (default: custom.json)
--output <path>	Output path for graph (default: spending_analysis.png)
--cycle-start <day>	Day of month when billing cycle starts (1-31, default: 1)
--no-graph	Skip graph generation

For --cycle-start values 29-31, the script automatically adjusts for shorter months (Feb uses 28/29, Apr/Jun/Sep/Nov use 30).

Sample Output

Dependencies

pip install matplotlib

Examples

The examples/ folder contains sample files:

• dump.csv - Sample transaction data
• categories.json - Sample category patterns
• spending_analysis.png - Sample generated chart

Analytics with ClickHouse

For advanced analytics on CSV output using clickhouse-local:

# Total points accumulated
cat output.csv | clickhouse-local \
  --structure "tx_date Datetime, tx String, points Int32, amount Float32" \
  --query "SELECT SUM(points) FROM table" \
  --input-format CSV

# Total debits
cat output.csv | clickhouse-local \
  --structure "tx_date Datetime, tx String, points Int32, amount Float32" \
  --query "SELECT SUM(amount) FROM table WHERE amount < 0" \
  --input-format CSV

License

MIT