eMulya Fintech Pte Ltd
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.