In the run-up to automation, artificial intelligence (AI) is remarkably transforming several industry-specific processes with added value. For financial services, AI is set to automate tedious tasks such as financial spreading that involves intensive data entry and analysis. With algorithmic advancements, financial firms can combine traditional OCR scanning services with machine learning algorithms. Read on to explore how AI OCR for financial spreading to strengthen and accelerate credit risk management processes while developing significant analytics capabilities.
AI OCR for Financial Spreading: How it works
Financial spreading lays the foundation for critical business decisions for banking and financial services. For financial firms, reviewing data and spreading financial information is an imperative way to aid strategic decision making and risk assessment. However, the traditional practice of manually handling financial statements entails erroneous entries, high operational costs, and unreliable analysis.
The combination of artificial intelligence services and Optical Character Recognition (OCR) systems present an automated, efficient, and more intelligent way of maintaining financial databases. AI-powered OCR can not only digitize scanned financial copies but also turn them into readable and searchable datasets available across channels.
In contrast to conventional OCR systems that are restricted to minimum fonts and formats, AI OCR for financial spreading are able to-
a) Identify and extract text from unstructured images and formats
b) Digitize invoices, bills, financial reports, and
c) Archive voluminous documents with editable and searchable functionalities, and
d) Understand and translate data in multiple languages to ease analysis.
At Oodles, we employ various machine learning libraries and frameworks along with Python programming to build AI OCR for financial spreading.
Here’s a step-by-step guide on how we achieve 99% precision in capturing and extracting complex financial data by building deep learning models-
Step 1: Installing the open-source OCR engine, Tesseract
Tesseract is an OCR software run by Google that infers text from scanned images with features like character classification, segmentation, and layout analysis. The latest Tesseract tool comprises of new neural network subsystems that use LSTM to recognize texts, columns, images, and layout from inputs.
Step 2: Preprocessing Images for Tesseract
In the next step, we need OpenCV, an open-source computer vision and machine learning library to preprocess input images and improve model performance. The library is a storehouse of different techniques like changing image colorspaces, geometric transformations, thresholding, smoothing, and more to process different images.
Learn more: AI OCR for Financial Spreading Strengthens Risk Management
