AI Innovation: Financial & Healthcare Payments
The technological breakthroughs in Artificial Intelligence (AI) and artificial neural networks (ANN) offer unparalleled benefits to the finance and healthcare payment sectors.
Servion predicts that by 2025, AI will power 95% of all customer interactions. Accenture projects that AI is set to boost profitability by 38% and generate $14 trillion of additional revenue worldwide by 2035.
Artificial Intelligence was built to handle processes that were once fully dependent on human intelligence and cognition. But AI is not as simple as this definition may appear. There are many sub-categories and varying levels of AI technologies, involving:
- Machine learning
- Artificial neural networks (ANN)
- Deep learning
- Convolutional neural networks (CNN)
- Recurrent neural network (RNN)
- Computer vision
AI in Banking and Financial Check Payments
Organizations processing checks will experience the value of deep learning models within OrbNet AI as Anywhere Recognition delivers over 99% read rates with accuracy levels of 99.5%.
The first deployment of the technology is focused on the amount and MICR fields. Optimized models are created for many fields of interest including: amount fields on checks, numeric fields, MICR, amount fields on internal tickets, date, payee, and payor field.
Check fraud detection also benefits from ANN. OrbNet Forensic AI is now available to dramatically improve the detection of counterfeit checks, forged signatures, and altered checks as a way to improve customer protection and reduce fraud losses as long as fraudsters continue to target financial institutions and their customers.
AI in Healthcare
The healthcare industry is primed for AI Innovations. One major application for AI would be to streamline back-end operations/revenue cycle management (RCM). RCM suffers from an overload of paper-based documentation and the lack of interoperability of data between systems. This results in redundant work compounded with heavy manual labor, either internally contracted or via a business process outsourcer (BPO).
Due to outdated data entry platforms, manual processing negatively effects healthcare remittance postability by introducing high error rates causing many exceptions. AI with deep learning models can be applied to electronify paper remittances and explanation of benefits (EOBs/EOPs), correspondence letters (also known as denial letters), and patient payments, eliminating the manual processing of payments.