Document intake and review is a critical task for most government agencies, especially when there is a high risk potential for improper payments or outright fraud. In an ideal world, an agency’s finite time would be invested in the review of the more complex documents, and not merely verifying valid records. SoKat can help an agency attain a more ideal allocation of their time and energy, therefore increasing their productivity and decreasing costs, by custom-building an automated document intake and recommendation system. Our system uses Robotic Process Automation (RPA), Machine Learning ML), Natural Language Processing (NLP) and Optical Character Recognition (OCR).
Below are a few of examples of use cases for government agencies, in implementing an automated document intake and recommendation engine:
Use Case #1: Insufficient Documentation Detection: Our algorithm can evaluate a document’s sufficiency with respect to all required information. Furthermore, our algorithm can evaluate whether all required information is legible, using Optical Character Recognition.
Use Case #2: Fraud Detection: By building and employing a predictive risk engine, our AI model detect incorrect or incomplete data in documents.
Use Case #3: Medical Necessity: Our system can use OCR to extract pertinent clinical data from records to automate the process of determining whether claims meet guidelines.
Use Case #4: Incorrect Coding: Our system will not only detect incorrect coding, it can provide the underlying reasons for a mismatch and provide the correct code.