Predictive analytics and machine learning has led to new methods for modeling human behavior and cognition. These methods are collectively known as behavioral analytics, focusing on how and why individuals and groups take actions and respond to them. This presentation discusses how financial services organizations are learning to leverage behavioral analytics for a broad set of applications that span customer insight, fraud detection, compliance, and market/investment intelligence.
RABBIT: A CLI tool for identifying bots based on their GitHub events.
Behavioral Analytics for Financial Intelligence
1. John Liu PhD, CFA
Behavioral Analytics for
Financial Intelligence
2. 2
Machine Learning
platform that
understands
human
communication
Nashville
Washington
New York
London
Goldman Sachs
Credit Suisse
In-Q-Tel
Silver Lake
National Security
Financial Services
Healthcare
Data Science
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3. Financial Services
FX Rate
Manipulation
• Collusion and
manipulation
• "Quid-pro-
quo”
• Clustered Bid/
Ask quotes
• Order
execution
around the
benchmark
• Trading abuse
• Deal related
language
• Abusive/dirty
references
• Improper
placement and
execution
Wall Cross
Violations
• Insider trading
• M&A leakage
• Personal trade
execution
• Companies on
Restricted list
• Email from/to
sensitive
countries
• Bribery & gift
language
• Presence of
attachments
• Deal related
language &
order execution
• Public info to
enhance KYC
profiles
• Email to/from
economically
sanctioned
countries
Conduct Risk
Anti-Bribery
Anti-Money
Laundering
Unauthorized
Trading
3
Electronic Communications Surveillance
5. • Abundance of data available that captures
human behavior
• Understanding human behavior makes for
better decision making
• Behavioral analytics transforming financial
industry
5
3 Big Ideas
6. BIG data: recording the annals of human behavior
(YouTube collects 500TB/day, mostly about cats)
Data, Data, Data
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7. Types of Data
Structured Data = Any data that can be stored in a table
Unstructured Data = Everything else!
Categories of Unstructured Data:
• Text (emails, im chats, tweets, legal docs, etc…)
• Image (Facebook, Instagram, webpages)
• Video (Youtube, security cameras, drones)
• Audio (phone calls, voicemail, open mic)
• GPS/IoT streams
• Romance novels
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8. What is Behavioral Analytics?
8
We are creatures
of habit.
Definition: Behavioral analytics quantify the effects of
psychological, social, cognitive, and emotional factors
on human decision-making
9. Behavioral Analytics Purpose
9
Events, Relationships and Anomalies
(Friends, Supply Chain, Cyber)
Eat
Burp
Sleep
Process Mining: Habits and Behavior
(Shopping Habits, Buying Cycle)
Preferences: Likes and Dislikes
(Advertising, Regulatory Reporting)
10. Behavioral Analytics
10
Descriptive
Did
Identify & Track
Behavior
Anomaly
Detection
Fraud, Cyber,
Insider
Predictive
Will Do
Credit Scoring
Risk
Management
CRM & Cross-
Selling
Diapers & Beer
Prescriptive
Want To Do
Behavior
Modification
Gamification
Fixing Cognitive
Heuristics/Biases
17. 17
Why
do
we
call
it
a
“Knowledge
Graph”?
Paul
Tudor
Jones
Michael
Cardillo
Galleon
Tech
Fund
Kris
Chellam
Krish
Panu
Ian
Horowitz
Stan
Druckenmiller
Leon
Shaulov
P&G
TransacHons
GS
TransacHons
Rajat
Gupta
Richard
SchuKe
Fantasy
Football
League
Wharton
School
Proctor
&
Gamble
Goldman
Sachs
SpotTail
Capital
Advisors
McKinsey
&
Co.
Intel
Corp.
Anil
Kumar
Gary
Cohn
David
Loeb
Lunch
Fund
Galleon
Group
LLC
Rajiv
Goel
P
&
G
Board
PorQolio
Manager
Raj
Rajaratnam
Voyager
Capital
Partners
20. Human Resources
• Goal: Talent recruitment, retention, placement,
performance evaluation, promotion, compensation,
workforce planning
• Focus: identifying qualities of high performers, changing
outcomes (promote the best, encourage the rest)
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21. Talent Analytics Example
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• Employee Retention: Real cost of losing a high-skill
employee can often be > 1.5-2.0x annual salary
Source: Dave Weisbeck
22. Risk & Compliance
• Goal: Financial, Legal, and Regulatory risk measurement
and management
• Data Sources: internal communications (text, voice),
client interactions, social media, HR, CRM, expenses, etc
• Event and Behavioral Anomaly Detection
• AML
• SAR
• Unauthorized Trading
• Manipulation
• Misconduct
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23. Time Series Outlier Detection
Box-Jenkins (ARIMA – autocorrelation)
• Pros: seasonality, stationarity detection
• Cons: subjectivity in parameter initialization
Holt-Winters (Exponential Smoothing)
• Pros: seasonality (single), computation
• Cons: subjectivity in parameter initialization
Seasonal Hybrid (Generalized) ESD
• Pros: seasonality (LOESS), local & global anomalies
• Cons: low recall
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26. Market Intelligence
• Goal: Identifying and modifying individual and collective
investor behavior for individual or collective gain
• Data Sources: news, SEC filings, corporate releases,
public and private databases, electronic and written
communications, audio/video, social media, Bud Fox
11/6/15 26
GekkoisticAltruistic
27. Behavior Modification
• After watching which movie would investors express
greater risk tolerance in portfolio activities?
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Fischer et.al., Risk Analysis, Vol. 31, No. 5, 2011
28. Investor Paradox
A fair coin is flipped:
• If heads, your net worth increases 40%
• If tails, your net worth decreases by 30%
Would you play this game?
How many times would you play?
11/6/15 28
29. Cognitive Error
• Game has positive expectation over short-term, but
negative expectation over the long run
11/6/15 29
0
0.5
1
1.5
2
2.5
1 11 21 31 41
NetWorth
Games Played
Answer: Play Just Once
30. Which Would You Prefer?
A. $1 today?
B. $1.05 a month from now?
What if the choices were:
A. $1 in 24 months from now?
B. $1.05 in 25 months from now?
Most people prefer A in the first instance, B in second.
Example of “hyperbolic discounting”
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31. Cognitive Heuristics and Biases
11/6/15 31
Definition: a systematic pattern of deviation from
norm or rationality in judgment, a mental shortcut
where inferences are drawn in illogical fashion.
32. Example: Alpha Generation
• Goal: identify persistent sources of alpha
• Key is “persistent” as most signals (e.g. sentiment) are
viewed as transitory and unstable over long-term
• Current approaches incorporate emotional, affect
psychology and intent models that aggregate individual
sentiment to identify short-term group trends
• Incorporate models of investor behavior?
32
Alpha: the active return above a benchmark
representing investment skill
33. Sentiment Analysis
“The outlook reflects our expectations that Apple will
continue to maintain and defend its strong market position
in mobile devices and tablets despite seeing an decline in
its addressable market.”
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Source: Yahoo
34. Behavioral Economics
Traditional
• Perfect rationality
• Expected utility theory
• Risk aversion
• Efficient market
hypothesis
• CAPM and APT
Behavioral
• Bounded rationality
• Prospect theory
• Loss aversion
• Adaptive market
hypothesis
• Behavioral Asset Pricing
• Market anomalies
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Empirical evidence diverges greatly from classic theory
Why do market anomalies exist?
36. Drivers of Anomalies
Cognitive Errors
• Conservatism
• Overweight Bayesian priors
• Confirmation
• Selection bias
• Gambler’s Fallacy
• Mean reversion
• Representativeness
• Overweight recent evidence
• Availability
• Familiarity premium
Emotional Biases
• Loss aversion
• Pain > Gain
• Overconfidence
• Underestimate risk
• Endowment
• Sentimental value premium
• Status-Quo
• Regret-aversion
• Herd behavior
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Anomalies are drivers of excess return (alpha)
37. Quantitative Behavioral Finance
• Behavioral asset pricing model:
• This is a linear factor model that includes behavioral
bias sentiment factors
• Exposure to behavioral biases given by weights γi
37
E r[ ]= rf + βj
j=1
k
∑ (rj −rf )+ γi
i=1
m
∑ bi
Expected
Return
Risk-free
Rate
Factor
Beta
Bias
Sentiment
Premium
Risk
Factor
Premium
38. In Search of Alpha
38
1. Context-based analysis to extract features of behavioral
biases observed in investors
2. Combine these features with structured data to
formulate behavioral bias factors
3. Use the factors to fit regression models for asset returns
4. Construct factor portfolios to monetize market
anomalies by overweight/underweight exposure to
specific behavioral biases
Alpha
Factor
Portfolios
Behavioral
Analytics
Data
39. Key Takeaways
• We have too much data, not enough actionable insights
• Humans are prone to cognitive “mistakes” (some more
than others)
• Behavioral analytics can identify and quantify individual
and group cognitive behavior
• The financial industry can benefit from much of the
behavioral analytics insights developed in other
industries
• There is a lot more to do!
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40. Final Words
• We at Digital Reasoning are actively exploring neural
embedding and deep learning methods to enhance
feature extraction and prediction of behavioral biases
• Synthesys Studio: http://beta.digitalreasoning.com
• Book (Q2 2016): Python Machine Learning by Example
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