OCR Manager
Dynamic Documentation With Advanced AI
https://iterate.ai/platforms/interplay
OCR Manager
Dynamic Documentation With Advanced AI
https://iterate.ai/platforms/interplay
OCR Manager
Dynamic Documentation With Advanced AI
https://iterate.ai/platforms/interplay
Goal & Purpose
Automate data extraction and classification from varied and unorganized document sources.
Create custom-trained models for trade finance and other banking document verifications and routing. Verification includes original and document copy classifications as well as seal and signature extractions.
Scanned document texts can be leveraged to train private LLM and GenAI internal search bots and AI agents.
How can dynamic documentation be deployed?
Scanning Identification
Scanning Banking Documents
Building Natural Language Vocabulary
Automatic Routing
Smart Document Data for Claims Reports
Interplay has advanced OCR and data classification, where documents and forms can be scanned, formats recognized, then data is automatically extracted and structured. Written text (sentences and paragraphs) are analyzed and fed into an NLP library for recognition.
Case Study: Tier 1 Bank
Objective:
Automate data extraction and classification from varied and unorganized document sources
Development Requirements:
- Train AI to recognize myriad document sources
- Train extraction points to form structured data models
- Refine models for very high accuracy and security
- Automate data extraction and classification from varied and unorganized document sources
Results:
- Currently deploying to banks and related orgs in Hong Kong
- Data imports to legacy finance systems
Case Study: Int'l Brokerage
Objective:
Custom train models for trade finance and other banking document verifications and routing. Verification includes original VS document copy classifications as well as seal and chop extractions.
Development Requirements:
- Train AI on trade finance specific documents, the AI models comes with millions of pre-trained data points.
- Setup routine and extraction rules
Results:
- Deployed complex document extraction for large banks
- Increased trust in the process
- Eliminated manual human hand-holding for advanced job tasks
Case Study: NLP Cloud
Objective:
Advanced NLP implementation for global cosmetics retailer. Identify 100s of intents for chatbots, customer service, and product recommendations.
Development Requirements:
- Use transfer learning methodologies to create training data
- Identify ‘beauty language’ for relative nouns, verbs, and synonyms
- Incorporate core NLP word cloud for use in several apps
Results:
- Customer service chatbot reduced human workload 38%
- Graceful handoff to humans when needed
- Improved product targeting to each customer
NLP on Claims Texts to Track Trends
By building an NLP word cloud based on the terms and texts of claims, we can model trends, find “hotspots”, and uncover relationships between terms.
Closer Customer Relations
What if AI can automate the estimations process, so customers can have better confidence in the numbers, and adjusters can concentrate on the relationship?
Smarter Operations
What if we could train an AI to “speak the language” of insurance? What if it could understand the 200 most common intentions and questions coming from customers?
RELATED USE CASES
