Applied Intelligence Observations on the use of RPA and AI in the Public Sector
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Applied Intelligence Observations on the use of RPA and AI in the Public Sector
AI – CORE COMPONENTS
SENSE COMPREHEND ACT LEARN
See Make Connections Physical Support Structured decision making
Computer Vision Graph Analysis Robotics Machine learning
Read Decision Support Automate Unstructured decision making
Natural Language Processing Analyse, Predict, Recommend Robotic process automation Deep learning
Hear
Audio Processing
Copyright © 2021 Accenture All rights reserved. 2
Applied Intelligence Observations on the use of RPA and AI in the Public Sector
AI-Powered Virtual Workforce
What if a virtual workforce could
run part of your operations?
An AI-powered, virtual workforce, able to emulate many types of human worker
activities, can complement the human workforce by:
- automating high-volume complex* repetitive tasks,
- augmenting decision-making with collective experience and data insights,
- scaling new, innovative business services.
* Involving semi-structured or unstructured content, interactions, judgement calls
Copyright © 2021 Accenture All rights reserved. 4
AI-Powered Bots have an Increasing Number of Human-Like Skills
DISCOVER AND READ AND UNDERSTAND
AUTOMATE PROCESSES CONTENT
Record, edit and replay processes Interpret the content of a
by having a robot interact with document, image or video
applications’ graphical user Group documents by category
interfaces
Recognize text and visual
Mimic human interactions markings in content
Applicable on any system Extract and validate key
information
ROBOT
DIALOGUE INTEGRATE IN ENTERPRISE
Interact by using natural language OPERATIONS
speech and text Schedule and execute multiple
Understand context and ask robots
follow-up questions Monitor performance and
escalations, provide full auditability
Copyright © 2021 Accenture All rights reserved. 5
SVA – AUSTRIAN
SOCIAL SECURITY
Classification of scanned letters
For this client, 500,000 letters are categorized
manually each year. The letters are scanned into
PDFs, and with OCR the corresponding text is
extracted. This text is classified into one of 56
categories. Based on this categorization, further
processing is carried out by the responsible
employees.
• 500,000 LETTERS
• AUTOMATED TEXT CLASSIFICATION
• AVERAGE ACCURACIES OF 83%
Copyright © 2021 Accenture All rights reserved. 6
MAJOR INSURANCE
COMPANY
Classification of emails with high
precision requirements
Client required classification of 4M annual incoming
customer emails in 3 different languages. Because of
far-reaching consequences in case of error, the client
required a precision of 98%. The model was trained on
400,000 emails. Language was detected, and OCR
was applied on the email’s attachments. This enables
fully automatic classification of 4M emails each
year, with high accuracy.
• 3 DIFFERENT LANGUAGES WITH
AUTOMATIC LANGUAGE DETECTION
• ACCURACY OF 98%+
• 4 MILLION MESSAGES CAN BE
PROCESSED EACH YEAR
Copyright © 2021 Accenture All rights reserved. 7
EUROPEAN LAND REGISTRY
DEEP LEARNING PROVES IT’S THE
SMART WAY TO SEE HOW THE LAND
LIES
We helped a European land registry build a proof of concept to show how deep learning
could help their surveyors transform the laborious process of updating land records. By
applying advanced deep learning algorithms to satellite imagery, we were able to train a
model capable of alerting surveyors to undocumented changes with over 90% accuracy—in
close to real time. In a single pilot study it uncovered over 16 extensions and over 22
structures the authorities knew nothing about—amounting to over €6 million in uncharged
land tax.
OVER 90% ACCURACY
AUTOMATION Uncovered OVER spotting undocumented
of annual surveys €6 MILLION in tax revenues changes
Copyright © 2021 Accenture All rights reserved. 8
Clinical chart review solution overview
Multiple Data Sense Comprehend Act
Files & Formats
1 Optical Character 2 Text Analytics & 3 Machine Learning 4 Interactive User
Recognition Natural Language Platform Interface
JPEG
Processing
PNG
Labs
XP
PDFs
Claims
S
Auths
Converts unstructured Leverages admin & clinical Deduces the correct A human reviews the
documents (e.g. PDFs, JPEG, knowledge libraries with 1B answer and continually output, validates the
Medical
XP
Records
CCD-A
TXT, etc.) to a structured
format that computer
clinical combinations to
identify key words / phrases
adapts the algorithm based
on patterns of successes
results, and creates
actions for operations
S applications can read such as names, diagnoses, etc. and failures teams if necessary
Learn
Provides feedback loops to machine
learning to enhance the model based on
human validation
Taking a Holistic Approach to Augment Employees
Identifying right approach:
Program Governance
1. Have an organisation-wide strategy for AI. Don’t do it
piecemeal.
2. Think of the downstream impacts.
Discovery Implementation Run 3. What benefits will be achieved?
Automation 4. What processes do I target to achieve maximum benefit?
Strategy
5. Should those process be transformed or tweaked before
automation?
Center of Excellence
6. What are the technical and data protection implications?
Change Management
7. What are the optical and change management implications
of using AI?
Technology Innovation
Copyright © 2021 Accenture All rights reserved. 10Intelligent Technology + Human Ingenuity Scaling Responsible AI AI Virtual Workforce daniel.sheils@accenture.com
You can also read
