Fintech: Overview, Payments, and Regulation: Key Considerations in Fintech

FinTech: Overview, Payments, and Regulation
Key Considerations in FinTech
Professor Christopher Geczy PhD

FinTech: Some Key Considerations
• A window into the millennial heart and mind

• Issues of trust
• Trust in the financial industry
• Choice architecture
• Know your customer
• Social media and what can be known
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Millennials Attitude Towards Financial Advice

Millennials
• Millennials (aka “Gen Y”) are individuals born 1977-1995 (roughly 20 to 40

years old)
• 25% of U.S. population (83 million) (U.S. Census Bureau, 2015)
• Globally - 40% of the adult population (2015)
• “Diverse, expressive and optimistic” (Nielsen's characterization, for its
marketing clients)
• Median income
• $25K for younger Millennials (18-27)
• $48K for older Millennials
• 25% married compared with 42% of Boomers at same age
• More racially diverse than previous generations
Source: Neilsen, “Millennials: Breaking the Myths” (2014) and Deloitte, “Millennials and Wealth Management” (2015).
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Millennials
• Wealth accumulation: A greater percentage of double millionaires are

Millennials (14.7%) than Gen X (12.9%) and Young Boomers (11.8%)
Source: Neilsen, “Millennials: Breaking the Myths” (2014) and Deloitte, “Millennials and Wealth Management” (2015).
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Generational Wealth Distribution
Source: Neilsen, “Millennials: Breaking the Myths” (2014).
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Millennials: Attitudes to Financial Advice
• Optimism about their future

• Half think there will be no money for them from Social Security when they
retire
• 6% expect to receive SSRI at levels available to current retirees
• According to a 2014 U.S. Trust survey, two-thirds of Millennials see their
investment decisions as a way to express their social, political or
environmental values
• According to a Sept. 2015 survey by Financial Advisor and Private Wealth
magazines, about one-third of advisors are not very likely to recommend an
impact investment, one-third might recommend, and the remaining third are
very likely to*
• 68% of advisors report client inquiries about impact investing
* Tom Kostigen, “Impact Investing Survey Results,” Financial Advisor (Nov. 10, 2015).
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Millennials
• Only 68% of Millennials (here 1981-1996) felt confident to handle their

family’s money
• 73% for Gen X (1965-1980)
• 86% for Boomers (1946-1964)
• 88% for Silent Generation (Before 1946)
• Over 80% of millennials own a smartphone globally
• 90% of Millennials check their smartphones within the first 15 minutes of
waking up
• Most common activity: checking social networks
• 54% have started or plan to start their own business
• 27% self-employed US Trust “Insights on Wealth and Worth” (2016 and 2018)
Deloitte “Millennials and Wealth Management Trends and Challenges of the New Clientele”
Federal Reserve Bank of St. Louis Center for Household Financial Stability
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Millennials
• However, from the Federal Reserve Ban of St. Louis…

• 60% of U.S. Millennials have zero stock market exposure
• If they hold, they hold only $7,600, lower than Gen X at the same age
• Millennials households - average net worth of $90,000 in 2016
compared to $130,000 for Gen X in 2001
• 81% of Millennial workers don’t believe Social Security will be there for them
• 51% expect nothing from Social Security (Pew Research Center)
• More believe in UFO’s than believe that Social Security will persist
(Peterson Institute)
• And yet they’re optimistic about the future and want to matter in the world
US Trust “Insights on Wealth and Worth” (2016 and 2018)
Deloitte “Millennials and Wealth Management Trends and Challenges of the New Clientele”
Federal Reserve Bank of St. Louis Center for Household Financial Stability
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• A characteristic of Millennials is relatively low trust in other people
Source: Pew Research Center, “Millennials in Adulthood:

Detached from Institutions, Networked with Friends” (March 7, 2014).
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• A characteristic of Millennials is relatively low trust in other people

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• They are optimistic about their financial future

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• They are optimistic about their financial future

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Millennials: Attitudes to Financial Apps
• They use mobile devices for financial services
Source: Federal Reserve Board of Governors, “Consumers and Mobile Financial Services,” March (2016)
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Risk Aversion

Time-Varying Risk Aversion
• Depression Baby Affect

• Behavioral characteristics or actions taken/not taken by those who lived
through The Great Depression OR those of their children
• Less tolerant of risk
• Save more
• More introverted
Source: Malmendier and Nagel (2010), “Depression Babies: Do Macroeconomic Experiences

Affect Risk-Taking? Quarterly Journal of Economics, February 2011.
Time-Varying Risk Aversion
• Investor market experience impacts

• Risk aversion
• Risk capacity
• Actual risk taking
• Investors who have earned low stock market returns
• Less likely to take financial risk
• Less likely to participate in the stock market
• When they do, they invest less
• More recent experiences have stronger effects on an investors risk appetite
• Early life experiences especially can have long-lasting (decades) effects
Source: Malmendier and Nagel (2010), “Depression Babies: Do Macroeconomic Experiences
Affect Risk-Taking? Quarterly Journal of Economics, February 2011.
Willingness to Take Financial Risk Depends on Investor Market /
Macro Experience
Older Investors
Participate Relatively
More in the Stock
Market Older Investors
Experienced Greater
Lifetime Return Up Until
Date Indicated
Prior 50-year minus 20-

Older Investors year Stock Market Return
Participate Relatively
Less in the Stock
Market
Source: Malmendier and Nagel (2010), “Depression Babies: Do Macroeconomic

Experiences Affect Risk-Taking? Quarterly Journal of Economics, February 2011.
Investment Company Institute Annual Mutual Fund Tracking
Survey
Reluctance to Take Risk Rose During the Financial Crisis,
Especially Among the Young
Percentage of all US households willing to take "above-average" or "substantial" risk.
Source: ICI Annual Mutual Fund Shareholder Tracking Survey

Survey

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Youn ger than 35 35 to 49 50 to 64 65 or older All mutu al fund - owning households
Source: ICI Annual Profile of Mutual Fund Shareholder, 1998, 2001, 2004, and 2008 - 2018
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Millennials and Social Impact

Millennials
• A generational shift in social attitudes

• Motivated millennials to matter in the world
• Impact investment strategies have risen to high level of importance for
millennial asset owners
• A 2018 U.S. Trust survey found 88% of millennial respondents owned or
are in interested in social impact investments*
• A 2013 survey by the World Economic Forum of 5,000 investors in 18
countries found 36% of millennial respondents felt “improved society
should be business’ top priority
34
Millennials
35
Millennials
36
Millennials
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Socially Responsible Investing
ESG: Environmental, Social and Governance criteria
91% of institutional ESG assets are managed by

public funds (such as SRI mutual funds) and
insurance companies.
Source: US Social Investment Foundation, 2018 Trends Report.
38
Trust in FinTech

Trust and Trust in Algorithms
• Trust in financial advice and in algorithms

• Importance of trust
• Trust and financial advice
• Trust in algorithms and how they fail
40
Components of Trust
1. Three Levels of Trust

1. Trust in Technical Competence and Know How
• “Do I trust that you know what you’re doing?”
2. Trust in Ethical Conduct and Character
• “Do I trust you not to steal money from me?”
3. Trust in Empathic Skills and Maturity
• “If I tell you personal things about myself or my family, I need to
trust that you, the advisor, will handle that well”
41
Components of Trust
• What builds trust?

• Taking time to clearly explain product offerings and reasons for
recommendations
• Clearly explaining fees
• Stay up to date with current products and trends
• Respond quickly to client needs
42
Importance of Trust
Source: Wharton/SSgA Survey

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Importance of Trust

44
Importance of Trust

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Importance of Trust

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Importance of Trust

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Importance of Trust
48
Importance of Knowledge
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Important Aspects of Advisors - Knowledgeable and Friendly
Perceptions of Financial Planner Characteristics:
High and Low Complexity of Financial Planning Scenarios
Financial Planner High-Complexity Financial Low-Complexity Cue Significance
Characteristics Planning __Scenario__ Financial Planning Type Level
__Scenario__
Sample Size Mean
Perceptions Sample
of Financial SizeCharacteristics:
Planner Mean (p-value)
Score
High and Low *
Complexity Score *Scenarios
of Financial Planning
Holds the Certified Financial 63 1.428 63 1.619 Instrumental .170
® Characteristics Planning __Scenario__ Financial Planning Type Level
Planner (CFP ) designation __Scenario__
Has advanced training in a 63 Sample1.539
Size Mean 63Sample Size
1.682
Mean Instrumental .306
(p-value)
specialized field Score * Score *
Uses the latest financial 63 63
Planner (CFP®) designation 1.741 2.079 Instrumental .041**
techniques and models
Has advanced training in a 63 1.539 63 1.682 Instrumental .306
specialized field 63
63
Uses the latest financial 632.015 63 2.079 Affective*** .705
Puts you at ease 1.741 2.079 Instrumental .041**
63
63 2.015 2.079 Affective*** .705
Puts you at ease 63
63 2.047 1.968 Affective*** .626
Is caring
63
63 2.047 1.968 Affective*** .626
Is caring
Was trained at a recognized 63 2.158 63 2.301 Instrumental .431
university Was trained at a recognized 63 2.158 63 2.301 Instrumental .431
university 63
63 63
Is friendly toward you 2.222
63 1.825 Affective*** .014**
Is friendly toward you 2.222 1.825 Affective*** .014**
* (response options: 1= Extremely

* (response options:important;
1= Extremely2important;
= Important; 3 = Neither
2 = Important; important
3 = Neither nornor
important unimportant;
unimportant; 44= =
Unimportant; 5 = Extremely
Unimportant; unimportant)
5 = Extremely unimportant)
** indicates significance at < .05
*** further analysis indicates a significant difference between males and females at the < .05 level. More
*** further analysisspecifically,
indicatesfemale
a significant difference between males and females at the < .05 level. More
respondents considered affective cues significantly more important than males in the low-
specifically, female complexity
respondents considered
scenario. affective
There were no gender cues significantly
differences more important
in the high-complexity than males in the low-
scenario.
complexity scenario. There were no gender differences in the high-complexity scenario.
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__Scenario__
Sample Size Mean
Perceptions Sample
Score
High and Low *
1.682
(p-value)
63
63
63 2.015 2.079 Affective*** .705
Puts you at ease 63
63 2.047 1.968 Affective*** .626
Is caring
63
63 2.047 1.968 Affective*** .626
Is caring
university 63
63 63
63 1.825 Affective*** .014**

3 = Neither nornor
unimportant; 44= =
indicatesfemale
scenario. affective
scenario.
53
__Scenario__
Sample Size Mean
Perceptions Sample
Score
High and Low *
1.682
(p-value)
63
63
63 2.015 2.079 Affective*** .705
Puts you at ease 63
63 2.047 1.968 Affective*** .626
Is caring
63
63 2.047 1.968 Affective*** .626
Is caring
university 63
63 63
63 1.825 Affective*** .014**

3 = Neither nornor
unimportant; 44= =
indicatesfemale
scenario. affective
scenario.
54
__Scenario__
Sample Size Mean
Perceptions Sample
Score
High and Low *
1.682
(p-value)
63
63
63 2.015 2.079 Affective*** .705
Puts you at ease 63
63 2.047 1.968 Affective*** .626
Is caring
63
63 2.047 1.968 Affective*** .626
Is caring
university 63
63 63
63 1.825 Affective*** .014**

3 = Neither nornor
unimportant; 44= =
indicatesfemale
scenario. affective
scenario.
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__Scenario__
Sample Size Mean
Perceptions Sample
Score
High and Low *
1.682
(p-value)
63
63
63 2.015 2.079 Affective*** .705
Puts you at ease 63
63 2.047 1.968 Affective*** .626
Is caring
63
63 2.047 1.968 Affective*** .626
Is caring
university 63
63 63
63 1.825 Affective*** .014**

3 = Neither nornor
unimportant; 44= =
indicatesfemale
scenario. affective
scenario.
56
__Scenario__
Sample Size Mean
Perceptions Sample
Score
High and Low *
1.682
(p-value)
63
63
63 2.015 2.079 Affective*** .705
Puts you at ease 63
63 2.047 1.968 Affective*** .626
Is caring
63
63 2.047 1.968 Affective*** .626
Is caring
university 63
63 63
63 1.825 Affective*** .014**

3 = Neither nornor
unimportant; 44= =
indicatesfemale
scenario. affective
scenario.
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Constant (Outgoing) Contact
• Trust and Constant Contact

• Schmeiser and Hogarth, (FRB Working Paper, 2013), found that
financial advice increases financial well-being and has a positive effect
on financial behaviors.
• Trust was a function of age and with contact
58
Trust in Algorithms
• Dietvorst, Simmons and Massey (Univ. of PA/Wharton) in Algorithm

Aversion: People Erroneously Avoid Algorithms After Seeing Them Err
(Journal of Experimental Psychology: General, 2014) found that
• People prefer humans over algorithms generally
• Trust in algorithms is lower when humans view them at work
• Driven by when the algorithm was perceived to make a mistake
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Trust in Algorithms
• Five studies on MBA application success or number of airline passengers
Studies 1–4: Participants who saw the

statistical model’s results were less likely
to choose it. Errors bars indicate ±1
standard error. In Study 2, “AAE,” “5-Pct,”
and “20-Pct” signify conditions in which
participants were incentivized either for
minimizing average absolute error, for
getting within 5 percentiles of the correct
answer, or for getting within 20 percentiles
of the correct answer, respectively. AAE =
average absolute error; Pct = percentile.
Berkeley J. Dietvorst, Joseph P. Simmons, and Cade Massey: “Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err”, Journal of
Experimental Psychology: General, 2014.
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Forecasting Performance
Berkeley J. Dietvorst, Joseph P. Simmons, and Cade Massey: “Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err”, Journal of Experimental Psychology: General, 2014.
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Confidence in Forecasting
Difference in Confidence in Model’s vs Human’s Forecasts

Most people do not choose the statistical model unless they are more confident in the model’s forecasts than in the human’s forecasts. Errors bars indicate %1 standard error.
The “Did Not See Model Perform” line represents results from participants in the control and human conditions. The “Saw Model Perform” line represents results from
participants in the model and model-and-human conditions. Differences in confidence between the model’s and human’s forecasts were computed by subtracting participants’
ratings of confidence in the human forecasts from their ratings of confidence in the model’s forecasts (i.e., by subtracting one 5-point scale from the other). From left to right,
the five x-axis categories reflect difference scores of: <–1, -1, 0, +1, and >1. The figure includes results from all five studies.
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Algorithm Trust
• Trust is important
• Algorithm aversion exists (people don’t choose algorithms over humans)
once people see the algorithm in action – even if it beats them!
• They may choose the algorithm if they haven’t seen any results (even if
those results are better than theirs!)
• If people see algorithms make mistakes, they strongly avoid it
• Found that if they could change the algorithm, even a bit, after seeing its
performance, they were more likely to use it
• Combining their judgement with their algorithm helped them feel confident
69
Algorithm Trust
• Dawes, Faust and Meehl (“Clinical Versus Actuarial Judgement,” Science,

1989)
• Actuarial approaches beat clinical in numerous contexts (including
judging personality, parole violation predictions, progressive brain
dysfunction, Hodgkin’s disease diagnoses, college grades, diagnoses of
psychological disorders, psychiatric hospitalization times, and others)
• The combination of actuarial and clinical approaches dominated others
• There were several reasons: consistency, rational and rules-based
weighting of inputs, including of relevant variables, sample biases of
clinical approaches (limited sample, skewed exposures to treated cases
or the sick, and so on)
70
Choice Architecture

Complexity and Choice are Limiting
• Complexity and even the number of choices matters

• Research shows that “too much information” can:
• skew client risk perceptions
• make it harder for clients to come to decisions
• make clients less happy with the outcome of their decisions
• Investors are more likely to invest (take risk) when shown presentations of
performance over longer periods than when presented with a succession of
short-period returns. (Diacon and Hasseldine (2007))
72
• When faced with many choices, investors choose not to participate or buy
• Evidence from 401(k) plans
• For every increase in options by 10, overall participation declined by 2%
73
• Not having an answer leads to declining trust

• When a client asks a financial advisor a complex question or a question
about a complex product/service/solution, if he or she doesn’t know the
answer, he should say so
74
• Not having an answer leads to declining trust

• Bickart et al (2010) conducted a series of studies in which subjects
rated hypothetical advisors on trustworthiness and intention to invest
when them after being presented with three answers
1. Correct and simple
2. Admitting ignorance
3. Appeared obfuscatory
• It is always better for an advisor to admit ignorance than to
obfuscate, whether or not the client perceives that the adviser
hopes to earn a commission by discussing the product
75
Complexity and Advisor Action
76
Complexity and Advisor Action
77
FinTech: Some Conclusions
• It’s here to stay and it’s a part of the way the world will work
• Increasing in size and number of deals
• Global nature
• Millennials matter
• Issues of trust
• Trust in the financial industry
• Trust in algorithms
• Choice architecture
78

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FinTech: Overview, Payments, and Regulation

Key Considerations in FinTech

Professor Christopher Geczy PhD

• A window into the millennial heart and mind

Professor Christopher Geczy PhD

• Millennials (aka “Gen Y”) are individuals born 1977-1995 (roughly 20 to 40

• Wealth accumulation: A greater percentage of double millionaires are

Source: Neilsen, “Millennials: Breaking the Myths” (2014).

• Optimism about their future

• Only 68% of Millennials (here 1981-1996) felt confident to handle their

• However, from the Federal Reserve Ban of St. Louis…

• A characteristic of Millennials is relatively low trust in other people

Source: Pew Research Center, “Millennials in Adulthood:

• A characteristic of Millennials is relatively low trust in other people

Source: Pew Research Center, “Millennials in Adulthood:

• They are optimistic about their financial future

Source: Pew Research Center, “Millennials in Adulthood:

• They are optimistic about their financial future

Source: Pew Research Center, “Millennials in Adulthood:

• They use mobile devices for financial services

• They use mobile devices for financial services

• They use mobile devices for financial services

• They use mobile devices for financial services

Professor Christopher Geczy PhD

• Depression Baby Affect

Source: Malmendier and Nagel (2010), “Depression Babies: Do Macroeconomic Experiences

• Investor market experience impacts

Prior 50-year minus 20-

Source: Malmendier and Nagel (2010), “Depression Babies: Do Macroeconomic

Source: ICI Annual Mutual Fund Shareholder Tracking Survey

Percentage of all US households willing to take "above-average" or "substantial" risk.

Percentage of all US households willing to take "above-average" or "substantial" risk.

Percentage of all US households willing to take "above-average" or "substantial" risk.

Percentage of all US households willing to take "above-average" or "substantial" risk.

Percentage of all US households willing to take "above-average" or "substantial" risk.

Percentage of all US households willing to take "above-average" or "substantial" risk.

Percentage of all US households willing to take "above-average" or "substantial" risk.

Percentage of all US households willing to take "above-average" or "substantial" risk.

Percentage of all US households willing to take "above-average" or "substantial" risk.

Percentage of all US households willing to take "above-average" or "substantial" risk.

Professor Christopher Geczy PhD

• A generational shift in social attitudes

91% of institutional ESG assets are managed by

Professor Christopher Geczy PhD

• Trust in financial advice and in algorithms

1. Three Levels of Trust

• What builds trust?

Source: Wharton/SSgA Survey

Source: Wharton/SSgA Survey

Source: Wharton/SSgA Survey

Source: Wharton/SSgA Survey

Source: Wharton/SSgA Survey

Source: Wharton/SSgA Survey

* (response options: 1= Extremely

* (response options: 1= Extremely

* (response options: 1= Extremely

* (response options: 1= Extremely

* (response options: 1= Extremely

* (response options: 1= Extremely