COMPANY PROFILE

CHALLENGES

John Hancock, a large insurance provider, uses ANTstein™ to automate data extraction for claims

processing, eliminate manual tasks andincrease business productivity.

TECHNOLOGY/OPERATIONAL CHALLENGES

Documents had to be read manually and keyed into their downstream systems for further processing

Extracted data primarily handwritten (cursive) and signature, had to be verified for the accuracyof information capture

Corporate headquarters:Boston, Massachusetts, United States

Global employees: 8,000 (2018)

Revenue:US $531 Million

SOLUTIONThe AntWorks Solution

The documents were first cleaned up with the pre-processing engine then classified using the DocID engine (document indexing)

Cursive handwriting was extracted dynamically from the documents and reinforced using advanced, deep learning techniques

Exceptions were handled using the innovative quality check screen, which apart fromcorrection of data, enabled the system to learn with both assistive and adaptive machine learning

Finally, this clean data was fed directly into the downstream systems using API integrations

The large volume of policy management documents received needed to be manually handled

Policy documents held vast amounts of unstructured data, specifically handwritten text in bold and cursive

Signatures needed to be identified and validated with other documents to ensure authenticity

AntWorks was not only able to process unstructured data that included handwritten fields (both block & cursive), but also check-boxes and signatures within the policy documents and contextualise the extracted fields.

KEY OUTCOMES

ANTstein, AntWorks’ flagship integrated automation platform (IAP), has a unique ability to read cursive writing and offers:

The AntWorks Advantage

Noise reduction, using an image enhancement engine that is unaffected by watermarks, blurs or overlaps A foundation in fractal science that requires a smaller training data set

Learning based on pattern recognition and not on an absolute font set or pixel matching

FOR HANDWRITTEN CURSIVE RECOGNITION(accuracy increases with more samples and training)

65%+ACCURACY

75%REDUCTION

IN MANUAL WORKFORCEfor the tedious process of data extraction

INCREASED ACCURACYin extracting data from process documents

EASIER workflow management

LOWER turn-around time

HIGHER business productivity

High Level – Process Flow

Pre-processing

Fetch document from Client repository and enhance the

Image quality for only the “Poor Quality” documents

Document Identification

Classifies documents into respective document categories.

A “Quality Check” screen is provided to validate the

classifications

Solution Approach To Meet Client Objectives

BENEFITS

Delivery Design forNon-Linear Profitability

Optimise Time and Benefit Trade Off

Create a Scalable Content Capture Framework

Data Capture

From unstructured documents containing Printed / Handwritten /

Checkbox / Signature content

Data Enrichment

Validates and applies necessary business rules to captured data

Quality Audit

Allows user to validate the output data and complete the

transaction

Output

Final data, post-validation exported in any required format

(xls, csv, xml & json) or APIprovided for target systems to

consume the data

• Maximise Data Capture Accuracy using technology intervention

• Automate Classification using Learning Algorithms and Fractal Networks

• Utilise Machine Learning and AI to achieve data certainty in Extraction

• Analyse operational data to identify 80:20 benefit rule

• Design Implementation plan to maximise and optimise booking of

benefits while managing risks

• Shape long-term partnership for mutual benefit

THANK YOU

To find out how you can leverage ANTstein solutions for your organisation, contact us at

Hello@Ant.Workswww.Ant.Works