Real Cognitive Automation by Deep Text UnderstandingNico Lavarini

Autonomous Decision-Making: Assessing the Technology and its Impact on Industry and SocietyRüschlikon, October 25, 2017

▪ LARGEST EUROPEAN VENDOR of Text Analytics & Cognitive Computing

▪ PUBLIC COMPANY (EXSY) with offices and R&D labs in Europe and USA

▪ PATENTED TECHNOLOGY

▪ The technology of choice

FOR ENTERPRISES in all sectors

and FOR GOVERNMENTS

About us

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COGITO leverages Natural Language Understanding by means of Semantic Technology, to process documents and drive high ROI

Cognitive capabilities on artificial intelligence algorithms that mimic the human ability

10+ years of large projects worldwide Domain- and Application-independent semantic platform Full set of unstructured information (80% of total) management features

Categorization, Text Analytics, Tagging and Ontology Matching Extensive Knowledge Representation, Search Relationships, Sentiment analysis

What we do

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To what purpose

Knowledge Management

• Automatic Classification

• Content Enrichment

• Advanced Analytics

Cognitive Automation

• Customer Support & Conversation

• Knowledge-based Business Processes

Corporate Intelligence

• Competitive Intelligence

• Operational Risk Mitigation

For what customersGOVERNMENT

FEDERAL AGENCIES

MEDIA & PUBLISHING

LIFE SCIENCE & PHARMA

BANKING & INSURANCE

TELCO & OTHERS

OIL & GAS

MANUFACTURING

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What we do differently

From Information

to KnowledgeFull Semantic Understanding

Language Knowledge

Graphs

Cognitive Computing Platform

The COGITO Technology

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Background

RPA• Clerical Process Automation

by SW robots• Rule-based, non-disruptive

Cognitive Computing• Simulating human abilities• Computer Vision, Speech

Recognition, Predictive Analysis, IoT• Text Mining, NLP, Machine Learning,

Sentiment/Emotion, Reasoning

COGITO

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From Text Analytics to Cognitive Automation with RPA Forrester on RPA: “design the system to potentially link with cognitive

platforms.”

Started from simple cognitive processes (email routing, customer care, document categorization)

Moving to Process Restructuring with RPA with full Cognitive Computing deep impacts

Cognitive Technology and RPA

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Process Class for Process Automation

Basic RPA

• transactional work activities• rule-based / repetitive in nature• (screen-scraping, macros, workflows

and basic design capabilities)

Enhanced• recognition of unstructured data• adapting to the business

environment

Cognitive

• decision support• advanced decision algorithms• interlinked with artificial

intelligence, NLP, analytics

Improves cost/speedData Information

+ Reasoning +ValidationInformation Knowledge

+Decision AutomationKnowledge Understanding

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RPA vs Cognitive Automation

Task Type

High VolumeLow Complexity

RoutineStructured Data

High ComplexityNon-Routine

Decision-supportingUnstructured Data

Operational Mode

Instruction-based Needs data and human training

Disruption in Job Definition

Low to medium High

Investment

Lower CostShorter Time

High ROI

Higher CostLonger TimeHighest ROI

Cogn

itive

Aut

omat

ion

RPA

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Case: Claims Management

•Motor, P&C in general – medical info, claim data, etc.

•High payouts, frauds, skill and turnover of Claims Managers

Case

•Health: automatic claim validation Accept/Reject (Decision Automation)

• Injury: from 1h to 1min for Medical Report analysis Price Range

Results

•Time saving, cost saving (FTEs and leakage), cohesion and coherence

•Decision automationImpacts

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Case: Property Risk Evaluation

•Risk Engineers often evaluate and rate Risk Factors from 3rd party Risk Reports

•Risk Selection & Pricing, non standard ratingsCase

•Cost effectiveness, volume management and focus on direct Risk EngineeringResults

•Actual Risk to Price compliance•Reduction of SubjectivityImpacts

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Case: Contract Matching

•Claim/Contract, Master/Local, Instruction/Policy

•Match, misalignments, leakages, high volumes, language barriers, local conventions

Case

• Speed up Claim Adjudication, reduce Misalignment/Leakage among contracts or UW InstructionsResults

•Reduce reserving, restructure contract processes, evaluate consistencyImpacts

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Case: Intelligence

• Smart Alerting for Intelligence Analysts• Daily job is information search and filtering (from

IP theft to Brand Management to Security)Case

• From continuous Pull to Push and alerting –Improved work quality

• Big Data sustainable impactResults

• Time Saving, consistency, 24/7 availability• Applications to Investment Management, Business

and Competitive Intelligence• Global awareness, hidden Signals, links

Impacts

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Time Saving & Cost Effectiveness – 24/7 availability Insurance: increase capacity/reduce reserving Reduce Risk/Leakage/Inconsistency

Coherence and cohesion – remove error-prone processes Human focus on more complex and bracing tasks added value

Note: Global quality evaluation is complex (cognitive biases) Inter-Rater Agreement (sometimes 60%) Intra-Rater Agreement (consistency over time)

General impacts

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Highlights

• Tradeoff of expertise transfer to machines• Human acceptance (steals my job, discredits my

expertise)• Action responsibility for machine-driven decisions

On Automation

• Support to decisions and not necessarily unassisted automation

• Decision | Validation | Alerting

On Decisions

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Outlook

“Zero-FTE Back Office”? (McKinsey: 30% can be automated) Better customer/broker experience direct business benefit Biggest impact is Business Process transformation: Getting prepared

Setting expectations Empowering Employees Education/training/dissemination/sponsoring/Focus groups

Knowledge Transfer is Key

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info@expertsystem.com

Nico LavariniChief Scientist

nlavarini@expertsystem.com (+39) 329 8607 804