Problem Statement: A certain financial institution faces the challenge of processing a significant number of handwritten banking instruments daily, which necessitates a sizable workforce, posing a challenge to automation efforts.
Our AI-Based Solution: The financial institution employs AI to automate the back-office operations, including clearing all banking instruments and reconciling with the general ledger. The solution uses Optical Character Recognition (OCR)/Intelligent Character Recognition (ICR) technology to extract information from scanned images and validate them with the core banking system, reducing manual effort by 30-40%. The financial institution also tracks the progress using data analytics and KPIs.
Result: The implementation of AI has saved banks almost 50% of their operational expenditure, allowing them to invest in improving their top line.
Machine Learning Models: Pytesseract for MICR (Magnetic Ink Character Recognition) Code extraction from cheque and CNN (Convolutional Neural Network) for Handwriting Extraction & Classification.
