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IN-D PayGen

eMulya Fintech Pte Ltd

Payments
Payment Initiation Services
Automate payment processing by converting incoming paper-based payment initiation requests to SWIFT messages
Payments
Payment Initiation Services
Automate payment processing by converting incoming paper-based payment initiation requests to SWIFT messages

Using computer vision, natural language processing and machine learning, IN-D PayGen extracts payment data from paper-based documents/fax, reducing the risk of errors due to rekeying, improved efficiency, reduces operational overheads and creates a seamless customer experience. ​AI models are trained on multiple document formats, resulting in low implementation time, and the technology can be extended to other use cases such as digitizing KYC documentation, reading paper based financial statements for credit profiling, resulting in superior decision support. ​

Traditional banks have a high cost of operations and sub-optimal business processes, mostly due to manual operations. It’s important that they control these costs to stay competitive against neobanks that answer the customers new needs and propose a seamless, contextual experience for payment initiations. More than that, digitalization is key when it comes to using digital data for better customer insight that translates to improved customer service.

Improve Customer Experience

IN-D PayGen converts any paper-based payment instruction into digital SWIFT messages with very high accuracy using natural language processing (NLP), resulting in straight through processing. By sending the converted payment directly to GPP verification flow, the application cuts time and error rates, and improves the customer experience by reducing friction in digital journeys that require physical payment documents handling.

Reduce Operational Cost

Converting paper documents to digital payment instructions using AI based NLP with full auditability removes human intervention and manual errors. The app capability to read various document layouts decrease the number of samples required for AI training, thus accelerating time to deployment.

Smarter decision support beyond payments

Make decision systems smarter by generating insights from data in archived documents. The application be extended to several use cases, automating KYC processes by reading ID documents and read back, and financial statements to perform credit analysis and decisioning.

Company Verified

General information

Commercial model
Refer
Integration
Pre-integrated products
Fusion Global Pay Plus
doneAvailable for all 3rd party systems
Implementation
Cloud Hosted
Website
Support
Privacy policy
Business countries
India
Malaysia
Singapore
United Arab Emirates
United Kingdom of Great Britain
United States of America
Data processing countries
Singapore

How it works

The solution is based of micro service architecture (with both SaaS and on-premise deployment options) with REST API as per Open API standard allowing seamless integration. IN-D’s core document to data engine supports functionalities like document layout understanding, document domain intelligence etc., thus requiring a shortened implementation timelines of only 2-4 weeks and few sample documents for training.

eKYC

KYC OR Digital Identity Verification Checks during Customer Onboarding: classifying IDs, extracting data then verifying it from government database, cross validation across IDs, face matching and liveliness check, Geo Tagging and OTP Intimation by email. A self-training module is available to allow training of any Govt Issued ID in any country.

Contracts

Expedite reading through multipage unstructured Contracts, Agreements, RFPs etc. Bulk document upload and document parsing to identify various logical and structural sections of the document. Read and highlight specific data or relevant sections for ease of management. A query based self-training option is available to train for additional areas of interest.

Accounts Payable

Automation for vendor invoices processing: bulk invoice upload (supports multi page invoices), header and line-item data extraction, intuitive validation interface for correcting low confidence extractions, data feed for ERP, RPA, and any other downstream applications.

Handwriting Recognition

Data Extraction from handwritten forms: character and word wise Data Extraction from structured handwritten forms, intelligent character recognition (ICR) for digitization of extracted data, named entity recognition (NER) based corrections for improved accuracy (name of Person, City, etc.), check box reader to read gender, Salutation etc., signature image extraction
How it works

How it looks

KYC, Insurance Claims Administration, Credit Analysis, Accounts Payable

IN-D Solves all these as Document-to-Data-to-Decision Problems

Review documents

Once the document has been uploaded and processed, you can review it and correct any errors if needed.

One core engine

IN-D Addresses Key Adoption Challenge by Common Core to Solve Critical Industry Problems

Seamless, zero cost integration

IN-D allows you to control your process design while adding cognitive capabilities
screenshot container
KYC, Insurance Claims Administration, Credit Analysis, Accounts PayableReview documentsOne core engineSeamless, zero cost integration

KYC, Insurance Claims Administration, Credit Analysis, Accounts Payable

IN-D Solves all these as Document-to-Data-to-Decision Problems

Resources

IN-D PayGen
FACTSHEET
In-d Paygen