Scan and extract data from customer documents faster with OCR/NLP, automate image forensic (comparing customer real-time photos with that provided in documents/IDs). Automate Documents Processing from consumers (e.g. KYC documents) applying for loans/accounts, or businesses applying for different services (e.g. contracts, legal documents)
Understand the Use-case under 5 minutes
Video (3.5 minutes)
Deloitte’s Digital Bank accelerator harnesses the latest technology to reimagine the customer onboarding and account creation processes for financial institutions
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Article (4 minutes)
See how through a cognitive approach, documents processing can be significantly enhanced and cycle times can be reduced, freeing up employees for more complex and value-added tasks.
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Get to know more Business and Technical details about the use-case (15-30 minutes)
More detailed introduction covering business and technical aspects
White Paper (16 minutes)
Presenting a holistic next generation digital customer onboarding experience powered by AI. Examples include: OCR driven data capture, image forensic, facial recognition, real-time compliance check, forgery check, digital signature, & more
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Article (7 minutes)
In this article you will learn more about the OCR technology and how it is eliminating manual data henry and extracting information automatically.
Read
Article (6 minutes)
In this article you will learn more about the buyer’s journey, where AI and automation can help, and the future of loan officers in the digital mortgage age
ReadCase studies, Organizational Aspects, Return on Investment examples
White Paper (23 minutes)
Detailed breakdown of the Cost Savings in each step of the Onboarding process when applying AI and RPA to Streamline Processes, related to: information gathering, data processing, and data validation
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Case Study (8 minutes)
The result was an average onboarding journey for a "non-complex customer" being reduced from five days down to less than one hour on average, leading to a record month in April of 18,000 customers onboarded, up from a fairly static average of 1,500 a month.
ReadMore details on the technical aspects of the use-case
Article + Video (10 minutes)
In this post, we’ll showcase how Drishti Document AI, an intelligent document parsing solution built on AmazonTextract, helps optimize and accelerate customer onboarding journeys.
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AI Service
Amazon Textract is an AI service that automatically extracts text, handwriting and data from scanned documents that goes beyond simple optical character recognition (OCR) to identify, understand, and extract data from forms and tables.
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Technical resources that will help you implement the use-case (notebooks, tutorials..)
AI Service
AWS Textract, AWS Comprehend, Azure Form Recognizer, and Google Document AI are codeless cloud services that can help you extract insights from different types of documents using ML-based OCR and NLP
Github Repo
A curated list of resources for Document Understanding (DU) topics related to Intelligent Document Processing (IDP)
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Tutorial
Tech behind the most used Tesseract Engine, which was upgraded with the latest knowledge researched in OCR. This article will also serve as a how-to guide/ tutorial on how to implement OCR in python using the Tesseract engine
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Article
In this article we will be learning about the task of handwritten text recognition, it's intricacies and how we can solve it using deep learning techniques.
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Article + Github Repo
This blog post walks you through creating an NLP-powered search index with Amazon Textract and Amazon Comprehend as an automated content-processing pipeline for storing and analyzing scanned image documents.
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Hands-on Project
In this deep learning project, you will learn how to build your custom OCR (optical character recognition) from scratch by using Google Tesseract and YOLO to read the text from any images
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Data Sets you can use to build Demos, POCs, or test Algorithms
(FUNSD) comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task.
dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class
Off-the-Shelf Products using AI for Customer Onboarding Automation
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