fintech and the roots of household financial healthprofessor ken singleton lendit financial health...

1 ©copyright Kenneth J. Singleton

@Copyright Kenneth J. Singleton

FinTech and the Roots of Household

Financial Health

Professor Ken Singleton

LendIt Financial Health 2019 @copyright Kenneth J. Singleton


(Weak) Financial Health: Symptoms

Ineffective management of day-to-day financial life +  Delinquent on bills +  Incurring high balance/transactional fees Weak resilience in the face of life’s inevitable “shocks” +  Insufficient savings (no emergency funds) +  Overly indebted Limited Capacity to seize opportunities to enhance financial security and mobility


Assistance Where Others See(k) Fees Top 3 banks collected $7.7B in fees in 2017

Advances: Direct to Consumer PFM’s Through Employers


Getting at the Roots of Financial Health Challenging Considerations:

² Extracting “economic rents” from those we most want to empower?

² Risks of unintended consequences?


Getting at the Roots of Financial Health

Government policies/Regulation Psychological factors (Preferences) Education/Mentoring/Training

Roots (Causes):

Challenging Considerations:


² Minimizing the risks of unintended consequences/behaviors?


Overcoming Behavioral Frictions…

Loss Aversion? Present Bias/Inattention? Mental Accounting?


Self-Efficacy (Beliefs About Oneself) Matters

Education/Mentoring/Training Causes (Roots):


Self-Efficacy (Beliefs About Oneself) Matters

Education/Mentoring/Training

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13−20 20.2−21.3 21.4−22.6 22.7−24 24.2−28Average Pearlin score prior to 2010

Late debtLate bills

Financial delinquency rates and self-efficacy

Figure 2. Financial delinquency rates and self-efficacy. We plot the average delinquencyrates, as well as standard error bars, for individuals in each quintile of self-efficacy. We report therange of self-efficacy scores for each quintile. Financial delinquency is measured as the fraction ofrespondents with late debt (red dashed line) or late bill (black solid line) payments. These fractionsare measured in the full sample for late bill payments and in the subsample of individuals withdebt outstanding in the current or prior interview for late debt payments. (Color figure can beviewed at wileyonlinelibrary.com)

We further examine the relationship between self-efficacy and delinquencyby estimating equation (1). The regression results are displayed in Table III.The results show a strong negative correlation between a person’s self-efficacyearlier in life and his subsequent likelihood of missing debt and bill payments.The estimate in the first specification shows that, without control variables,the probability of missed debt payments declines by 9.59 percentage points(p < 0.01) when moving from the bottom of the self-efficacy distribution to thetop (from 0th to the 100th percentile). After conditioning on demographic- andpreference-related control variables, the estimated effect is −8.66 percentagepoints (p < 0.01). Finally, with additional controls for education, income, andnet worth, the self-efficacy coefficient is −6.82 percentage points (p < 0.01).For a one-standard-deviation increase in self-efficacy (0.28), this coefficient es-timate implies a 1.91 percentage point decline in loan default. This decline ismeaningful, as it amounts to 14% of the average loan default rate in the sam-ple (14.1%). As shown in the remaining columns of Table III, we also observe anegative and significant relationship between self-efficacy and delinquency onbills. In the specification without controls, the coefficient on self-efficacy indi-cates a 9.63 percentage point decline (p < 0.01) in the delinquency rate for a 100percentile change in the Pearlin score. Control variables reduce the magnitudeof this estimate, to −7.31 (p < 0.01) with demographic and preference con-trols and to −5.24 (p < 0.01) with education, income, and net worth controlsas well. This last coefficient estimate implies that a one-standard-deviation

Ø  Less likely to default on loans or fall behind on bill payments è Lower bankruptcy rates.

Ø  Greater use of traditional credit products. Ø  More likely to save for emergencies.

Kuhnen & Melzer (2018)

Causes (Roots):


How do we create a “Digital Mentor”? Source: FINRA Foundation and CFA Institution 2018

© FINRA Foundation and CFA Institute, 2018. All Rights Reserved.

Almost a third of millennials with taxable accounts were under 21 years old when they started investing.

￭ Millennials with taxable accounts are significantly more likely to have started investing before their 21st birthday than taxable account holders from other generations.

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￭ Interestingly, millennials are more likely than Gen Xers or baby boomers to report their parents talking to them about investing before 18 years old (42% versus 29% and 21%, respectively). This could be a recall difference, but it may also reflect the democratization of investing and parents’ role in mediating it.

Millennials(n=601)

Gen Xers(n=505)

Baby Boomers(n=509)

Percentage Under 21 When Starting to Invest (among Those with Taxable Accounts) (n=1,615)

31% 14% 9%

If parents or family members talk to them about investing before age 18, millennials are even more likely to invest before age 21. 50%

Before 18 years old

23%

As an adult

12%

Did not speak to parents

Parent or other family members talk about investing…

Q7. At about what age did you start investing (outside of a basic retirement account)? (BASE: millennials, Gen Xers and baby boomers with taxable accounts; n=1,615)


Our Conditions and Financial Health

Weak/Thin Credit File Limited (or no) job security/unstable income Limited (or no) stable/affordable housing Chronic disease

Branches (Conditions):

The Future of Work Housing Over the Life-Cycle

Financing Medical Care


Part-time Work is Increasing Real Cost of Living is Increasing

The Nature of Work is Evidently Changing… Finding enough work is the #1 pain point!


Steady Job Search App

Increase income through part-time work in your geographic area and fields of interest Track earning and hours, as Steady recommends good work options for you. Learn from the Steady Community Set goals/plans for the future

Series A 8/18 $35M post valuation


Housing Over Our Life-Cycle

Afford. Housing: Haven Connect Co-Living: StarCity




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The real estate tech landscape is changing fastSince this map was made in March 2018, there have already been significant updates and startup formation in the real estate tech space.




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The real estate tech landscape is changing fastSince this map was made in March 2018, there have already been significant updates and startup formation in the real estate tech space.

Liquidity from Housing:


Oldest at which 50% of the babies born in 2007 are predicted to be alive.

Best Practice Life Expectancy

Source: Human Mortality Database Berkeley Planning for the 100 Year Life, the Age of Longevity

Patient Financial Aid:

Lower costs for self-insured employers:


Moving Towards the Roots of Financial Health

Weak Credit File Limited (or no) job security/unstable income Limited (or no) stable/affordable housing Chronic disease

Conditions (Branches)

Technological Constraints Government Policies/Regulations Psychological Factors Education/Mentoring/Training

Causes (Roots)

fintech and the roots of household financial healthprofessor ken singleton lendit financial health...

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