Bank statement OCR (Optical Character Recognition) technology automates the data extraction process from bank statements, transforming images or PDF documents into machine-readable data, thus providing a more efficient and error-free alternative to manual data entry. This technology captures essential information such as account numbers, transaction details, and balances, and is utilized across various industries for income verification, customer onboarding, and financial reconciliation. Several methods for implementing bank statement OCR exist, including simple tools like Excel's "Get Data" feature and online converters, which are easy to use but may present security risks and lack precision. More sophisticated solutions involve Python scripts and Large Language Models (LLMs) like GPT-4, which offer customization and powerful data structuring capabilities but require technical expertise and incur privacy concerns. AI-based Intelligent Document Processing (IDP) solutions, such as Nanonets, provide a comprehensive, secure, and scalable option, seamlessly integrating with financial systems and offering advanced features like multi-language processing and data validation, making them an ideal choice for automating bank statement workflows.