Michaël Boeckx, Chief Technological Officer at NRB, presents a number of real life use cases of AI implementations. NRB has realised for customers in various sectors, such as fraud detection in the insurance sector, water leakage detection for a public utility company, quality monitoring systems for a manufacturing company or an intelligent system for the optimization of public glass recycling. NRB presents its co-creation approach to all companies that are interested to explore the business case for deploying AI projects.

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WELCOME

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z Data & AI @NRB
Michaël Boeckx, CTO NRB
Business transformation powered by
technology

3. AN EXAMPLE OF AN ALTERNATIVE CHAPTER PAGE
AI uses cases @ NRB
Driving business growth through technology
Michael Boeckx, CTO
Michael.boeckx@nrb.be

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CLIENTUSECASES
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Utilities :
Predicting power
production and water
leakage
3Manufacturing:
Production Quality
Public & social :
Optimising glass
recycling
Insurance :
Fraud detection
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2Finance :
Audit
&
Compliance
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Insurance
Fraud detection
Description
 Uncovering and identifying hidden relationships and patterns among a
very large number of disparate data points
 AI can also classify potential fraud patterns to reduce false positives and
highlight the most alarming patterns
CLIENTUSECASES
Commercial Value
 Increase product profitability
 Better tariff segmentations
 Workforce shift to high value
labour
Impact
 Reduces operational cost linked to
fraud.
 Allows automated incident approval
 Identifing complex fraud systems
Business Domains
 Operations
 Sales Marketing
 Legal

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Description
 Analyse custom contracts, policies or other legal documents to speed
up processing and reduce errors
 Extract data to proactively manage a contract from initiation through
award, compliance and renewal
Finance
CLIENTUSECASES
Commercial Value
 Better comply with regulator
requirements
 Faster « go to market »
 Customer satisfaction
Impact
 Create faster results on easier cases
and red flag potential complex cases
 Be able to validate compliance and
asses risk faster.
Business Domains
 Operations
 Legal
Audit and compliance

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Description
 Identify defects by matching production patterns to defect models
using cognitive and human expertise. Linking it to SCADA KPI analysis.
Finance
CLIENTUSECASES
Commercial Value
 Detect anomalies faster and on
all items rather than on a
sample
 Perform Quality root-cause-
analysis
 Predict and automate line
interventions
Impact
 Reduce cost and time associated
with manual visual inspection
 Increase consistency of defect
detection and classifications
 Easily expand the library of defect
images and algorithms to
accomodate new product variations
Business Domains
 Production
 Planning
 Quality
Audit and compliance
Manufacturing
Production Quality

8. 8
Description
 Use Historical data to predict power usage
 Use weather forcast to predict power production from sun and wind
 Be more correct on windmill and solar panels maintenance cycles
CLIENTUSECASES
Commercial Value
 Cost reduction
 Production optimisation
 Maintenance cycles
Impact
 Reduce wasted resources and
unnecessary emissions
 Reduces need to buy power from
other supliers because of
underproduction
Business Domains
 Windmill farm: real-time anaylyses of
SCADA and environmental data to predict
and balance
 Solar farm: Identifying performance
problems and pinpoint route cause analysis.
Utilities
Predicting Power
production and usage

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Description
 Leakage and fraud detection through IoT, Intelligent meters, and
SCADA
 AI combines historical and current data analytics and algorithms to
detect anomalies and corrective actions.
NRW:
Non Revenue Water
Net Grid loss detection
CLIENTUSECASES
Commercial Value
 Revenu growth
 Optimise social responsibility
Impact
 Sustainable use of water
 Optimisation of production
Business Domains
 Operations
 Planning of interventions
 Maintenance

10. 10
Description
 Optimise truck glas collection rounds in a city to be sure the 1700 glas
collectors are full before emptiying using realtime data from IOT
collectors and historical data based truck data.
CLIENTUSECASES
Commercial Value
 Less noise overhead
 Less carbon emissions
 Less traffic impact
 Reduced cost
Impact
 The immediate cost reduction in KM
driven by trucks will result in
increased overall efficiency and
reduced ecological and social
footprint.
Business Domains
 Operations
 Planning
 Finance
Public et social
Optimisation of collection
and recycling of glass