Adp, Inc.

Arjun Hegde

Anuradha Verma

Kenneth G Kingery

Harshvardhan Prasad

Ajit Sri Kumar

Michael Oliver Plonski

Nagamalleswara Bodeddula

Hongguo An

Praison Selvaraj

Akshay Chopra

Sanjay Kutty

Ravi Kumar Tadysetty

Pablo Budke

Ravi Lankalapalli

Harsh Sharma

Rajat Gupta

Srinivas Goda

Madhusudhan Kakurala

Margaret Aronson-Olague

Atul Bochare

Nikhil Choudhari

Deepak Gupta

Abhishek Jain

Ram Sama

Maria Rios

Michael Pionski

Jie Huang

Azhar Khan

Srinivas Kulkarni

Gopal Prasad

Dhirendra Kumar

Vijaya Mahesh Sayi

Srikanth Chintamaneni

Christine Wymore

Fayaz Shaik

An illustrative embodiment provides a computer-implemented method for processing wage garnishment court orders includes obtaining a court order for wage garnishment for a user. The method also includes predicting values for a first subset of a number of fields for executing wage garnishment according to the court order using a rule engine. The method also includes predicting values for a second subset of the number of fields using a machine learning engine, wherein the second subset of the number of fields includes missing fields for which the rule engine could not provide a prediction. The machine learning engine includes one of a plurality of classification algorithms. The one of the plurality of classification algorithms selected to use for the machine learning engine for predicting field values is selected according to at least one of accuracy, speed, size of model, and explainability.

Wage garnishments processing using machine learning for predicting field values

A method, computer system, and computer program product are provided for processing an output of batch processed information. A document storage system receives the output of batch processed information from a number of company portals, websites, and online systems of organization. The document storage system separates the output into individual documents and individual forms. The document storage system indexes the individual documents and forms according to metadata. The metadata includes structural attributes extracted from the individual documents and forms, and company relevant parameters identified from business intelligence for the organization. The document storage system stores the individual documents and forms in association with the metadata. Responsive to storing the individual documents and forms, the document storage system generates an event message. The event message comprises information about the storing of the individual documents and forms. The document storage system publishes the event message to a message pipeline. The document storage system notifies a subscribed client device about the event message, including a notification of availability of the individual documents and individual forms separated from the output.

Method and system for interactive search indexing

Training an artificial intelligence system to process agency notices. The process identifies historical data that includes historical text generated from optical character recognition performed on historical images of the agency notices and historical metadata for items in the historical images of the agency notices. The process generates annotations for the historical text. The annotations identify the items in the historical text and locations of the items in the historical text. The process trains the artificial intelligence system using the historical data and the annotations.

Agency notice processing system

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Pasadena, CA, United States of America

Arjun Hegde