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TRANSCRIPT
Behavioural Economics in Fraud – Man vs Machine'
March 2016
Factors in the Business LandscapeThe Facts
Fraud: $16.31 billion in 2014 The Nilson Report, 2015
False Card Declines: $116+ billionJavelin Research, 2016
Global Tech Expenditure: $18.6 billionChartis, 2016
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Factors in the Business LandscapeUnderstanding The Challenge
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New Innovations
New InnovationsDecision Making: Man vs Machine
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AUTO
MATE TH
E TEDIO
US A
CTIVITIES AND RELEA
SE TEAM M
EMBERS
GREA
TER UNDERSTAN
DING AND CH
ALLEN
GE
THE PA
RADIGM
New InnovationsThe approach has to be man and machine – clear roles and responsibilities
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Default Effect
Confirmation Bias
Clustering Illusion
In-‐attentional blindess
AvailabilityBias
Herd Instinct
Social Loafing
Paradox of Choices
Decision Fatigue
Status Quo Bias
The Human Brain is wired to:
¡ Conserve energy¡ Avoid effort & risk ¡ Seek instant gratification
In-‐built biases and fallibilities make us ill-‐
equipped for fraud-‐related decision-‐making
Behavioural Economics & FraudUnderstanding the science and the impact on Fraud operational working practices
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% of Hourly Case
Worked
South African Financial Institution -‐ 2015
Recognition and adoption of preventative work practices can improve productivity
Behavioural Economics & FraudCase Study: Decision Fatigue
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We all do it!Missing an object that it is plain sight, as it is unexpected or not the focus of a search
Behavioural Economics & FraudCase Study: Inattentional Blindness
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Visual Attention Lab – Harvard Medical School
Behavioural Economics & FraudCase Study: Inattentional Blindness
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Problem Analysis & Definition
Data Preparation
Data Analysis & Modelling
Results Analysis &
Implementation
Optimising Data-‐Driven DecisioningEnabling business users to fully harness the power of the machine
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“Machine learning.. the study of algorithms
that can learn from and make predictions on
data.
Algorithms operate by building a model to
make data-‐driven predictions or
decisions”
Machine Learning – Neural Technology What do we mean?
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Self-‐Service
Multi-‐Model
Speed
Paradigm Shift in Machine Learning Making the machines focus on the tedious activity – freeing up the creative human brain
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2 Million transactions processed and modelled in 23 minutes
Easy To Use: A New Perspective Case Study: Easy to use and speed of modelling
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Multi-‐Model Approach: A New Perspective: Case Study: Multi model neural technology based system
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ü Reduced effort and cost for the business
ü Improving accuracy and decision-‐making for the fraud team
Modelling: A New Perspective: Multi Model neural technology based system
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When factories converted from steam power to electricity at the turn of the 20th century, it was not until decades later when the factory layouts were redesigned that productivity
gains were achieved
The same lag occurred with the introduction of computing: “We see the computer age everywhere, except in the productivity
statistics.” Bob Solow, 1987
Now experiments with chess free-‐style competitions show that teaming humans and computers achieves so much more than either
in isolation.
Man vs. Machine
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The Future Of The Analyst: More interesting analytical end to end business support:ü More innovationü More insight
The Future Of Fraud RiskMachine driven solutions:ü More automated decisioningü More scalability
"We should let the human brain do what it is good at, and let computers do what they are good at -‐ marrying the two is a powerful thing.“
-‐ Dr Robert Frey Professor State University Of NY & former MD Renaissance Technologies
The Future of Fraud Prevention
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