Description This talk will zero in on how “behavioral insights” and data science can work together to solve big challenges. Data in a vacuum provides a very narrow view of an issue or problem—especially when humans are a part of the equation. And there’s no one-size-fits-all approach when it comes to behavioral intervention. By getting people to work together, and using predictive models, we’ve discovered a more effective way to prompt change and make an impact. These models have provided dramatic results in effecting change in the collection of child support, reducing crime, mitigating risks for social workers, and several other applications. Questions What are two ways behavioral science relates to data science? What is the best way to think about “big data”? How can behavioral insights help us use big data in socially responsible ways?

1. The Last Mile Problem
How Data Science and Behavioural Science can Work Together
IPAC
Toronto
March 4, 2015
James Guszcza, PhD, FCAS, MAAA
Deloitte Consulting
jguszcza@deloitte.com

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Two overdue sciences
“Why do professional baseball executives, many of whom
have spent their lives in the game, make so many colossal
mistakes? …
It takes time and effort to switch from simple intuitions
to careful assessments of evidence.”
— Thaler and Sunstein review of Moneyball
“Many programmes and services are designed not for the
brains of humans but of Vulcans. Thanks in large part to
Kahneman and his many collaborators pupils and acolytes,
this can and will change.”
— Rory Sutherland, Ogilvy & Mather

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Classic examples of data science (aka “Moneyball” aka “big data”)
Predictive models can be used to:
• Hire more effective employees (Moneyball)
• Predict who is most likely to default on a loan (credit scoring)
• Predict who is most likely to crash their car… and how badly (actuarial science)
• Predict next best offer, customer churn, lifetime value (marketing science)
• Predict recidivism (law)
• Identify episodes of waste, fraud, abuse (government)
• Identify unsafe workplaces (risk management)
• Identify physicians at highest risk of being sued for malpractice (risk management)
• Identify divorced parents most likely to lapse on child support payments
• Predict healthcare utilization
• Estimate risk of such disease states as diabetes, hypertension, heart disease
• Predict college grades using high school transcript data
• Forecast movie box office returns, political election results

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Data science – a narrow frame
DATA MODEL
A narrowly technical view of data science:

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Data Science as a Strategic Capability
“All statistics is consulting” – Andrew Gelman
DATA MODELSTRATEGY

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Data Science as a Strategic Capability
“All statistics is consulting” – Andrew Gelman
DATA MODELSTRATEGY
“All models are wrong, some are useful” – George Box

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Our focus: “the last mile problem”
Predictive models can point
us in the right direction…
they can tell us whom to
target…
but they don’t tell us how
to prompt the desired
behaviour change.
MODEL

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Our focus: “the last mile problem”
Predictive models can point
us in the right direction…
they can tell us whom to
target…
but they don’t tell us how
to prompt the desired
behaviour change.
Furthermore:
Behavioural insights without
data analytics motivate
one-size-fits-all intervention
strategies.
MODEL

9. 9 Deloitte Analytics Institute © 2011 Deloitte LLP
Our focus: “the last mile problem”
Predictive models can point
us in the right direction…
they can tell us whom to
target…
but they don’t tell us how
to prompt the desired
behaviour change.
Furthermore:
Behavioural insights without
data analytics motivate
one-size-fits-all intervention
strategies.
MODEL
Can we do better by working
together?

10. Examples

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Yes they did
Motivating example: the 2012 Obama reelection
campaign used predictive models to identify whom to
target.

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Yes they did
Motivating example: the 2012 Obama reelection
campaign used predictive models to identify whom to
target.
It also used behavioural insights to more effectively
act upon the predictive model indications.

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Supporting child support
Predictive models are increasingly
used to guide child support
enforcement officers to non-
custodial parents at highest risk of
lapsing on their child support
payments.
Commitment cards could be field
tested to prompt the desired
behavior change.

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Push the worst, nudge the rest
The city of New York uses predictive models to deploy building
inspectors to the highest-risk buildings.
Behavioural nudge tactics could be employed to ameliorate
lesser risks that don’t merit immediate physical inspections.
… similarly with health / sanitary inspections, tax audits, …

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Let’s keep ourselves honest
Government agencies, tax authorities, insurance companies,
regularly employ statistical fraud detection methods to guide
fraud investigators to the most suspicious cases.
• But many forms of fraud are ambiguous or matters of degree (“soft
fraud”)
• And fraud detection algorithms yield false positives
Behavioural nudge tactics are “soft” interventions that
are well-suited to such ambiguities.
Invoke peer effects and people’s “internal reward
systems”… not just hard incentives…

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Driving behaviour change
Actuaries now use telematics data to better segment and price insurance
policyholders in terms of their utilization and riskiness.
But could the data be used to create new products and services…
… periodic or real-time reports that serve as behavioral nudges …
Ideas
• Detailed feedback reports to help student
drivers learn and older drivers stay behind
the wheel longer and safer
• Feedback prompting carbon footprint
improvements through peer effects

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Personalized health coaching
Lifestyle and medical data can used to
predict individuals’ healthcare utilization
and likelihood of various disease states.
But once we’ve identified the highest
risks, what can be done to change
behaviour?
Health coaches are a promising behavioral
strategy.
Furthermore: analytics could be used
to guide the hiring and matching of
health coaches with patients.