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Protecting Older Investors From Financial Fraud

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In the U.S., older adults lose an estimated $3 billion to financial scams each year. That figure should be astonishing — but the unfortunate truth is that our culture has become complacent about the financial risks we face in old age. The simple use of the language “grandma scam” or “grandparent scam” attests to the fact that elder financial abuse has become normalized in our increasingly digital world.

But while aging may be inevitable, financial fraud in old age isn’t. Our research team wanted to understand: why do older adults tend to be more susceptible to fraudulent situations, and how can we prevent this phenomenon?

Why are older populations more susceptible to financial fraud?

Financial decisions are already some of the most stressful we make. These complex decisions become increasingly difficult to navigate as we age. Elderly individuals in the U.S. alone lose an estimated 3 billion dollars a year to financial scams. 

But older populations aren’t more susceptible to financial fraud simply because of naivité or their limited technological literacy. The thing that makes older adults vulernable to scams is the way that our brains change as we get older.

The behavioral science literature offers some key insights into why we become more susceptible to financial fraud as we age. In general, research has found that older adults rely more on System 1 thinking — that’s the fast, automatic, and unconscious kind — than they do on to System 2 thinking, which leverages fluid intelligence to reason and think through problems deliberately.

Older adults also find it more difficult to navigate a wide array of choices. Overwhelmed by the options available to them, older adults tend to rely on more cognitive heuristics, which in turn leads to worse decision-making.2 Decreased numeracy (or fluency with numbers) also increases the risk that senior face.3 

Missing the big picture

But there’s another important reason that older populations may be more susceptible to financial fraud. This is because they are prone to paying close attention to details, while ignoring the bigger picture — a phenomenon that is also known as low-level construal. This predisposition to think about details in the present (see the availability heuristic and salience bias) can increase vulnerability in financial decision-making environments. 

This is particularly troubling because many fraudulent emails leverage the urgency to “act now.” For example, a foreign lottery scam might include a message like the following: “Send a deposit of $200 within the next 24 hours to claim your lottery earnings.” This brings the pressure to act quickly front and center. Biases towards present actions are amplified when the consequences of certain behaviors will be felt much later into the future.

Perhaps then, prompting abstract thinking, or “high-level construal,” would promote critical thinking about financial decisions in older populations. Our hypothesis was that incorporating personal financial risk assessment questionnaires into investment decision-making environments would effectively promote abstract thinking, ultimately reducing financial fraud susceptibility amongst older adults (aged 60+).

Testing our hypothesis

Our experiment involved 102 North American respondents divided into equally sized younger (18-25) and older (60+) cohorts. The participants were randomly assigned to a treatment or control group. Both groups were shown an email about an investment opportunity, which incorporated many of the common red-flags of fraudulent pitches. 

After reviewing the email, the respondents assessed the pitch along several dimensions including appeal, willingness to invest, and perceived risk. The intervention for the treatment group was a Personal Financial Risk Assessment, which the respondents completed before viewing and assessing the email.

Fraudulent investment opportunity provided to participants in the experiment. According to the US Federal Trade Commission, financial scams are increasingly occurring online.

How a simple mind hack points to the potential of reducing susceptibility

When comparing the treatment and control groups we found a significant effect of the intervention on susceptibility scores in those aged 60 and over. The control group who did not complete the personal financial risk assessment was much more likely to consider the financial opportunity to be appealing, trustworthy, and they expressed a greater willingness to invest. Furthermore, there was no effect of the intervention on the 18–25-year-old group, suggesting the intervention specifically targeted the cognitive deficits associated with old age.

This graph reports the effects of a Personal Financial Risk Assessment intervention on susceptibility to financial scamming in 18-25-year-olds versus 60+-year-olds. This intervention was effective in reducing susceptibility (p < .05), but only in the 60+-year-old group, suggesting that it may specifically target decision deficits linked to cognitive decline.

Key Takeaways

As the world becomes increasingly digital, and populations age, financial scams may become more common. The more tools we can provide to vulnerable populations to protect themselves from “falling for it”, the fewer victims there will be to face the negative consequences.

Overall, the key things to take away from this article are the following:

  • Although people of any age can fall victim to financial fraud, older individuals are at the greatest risk due to changes in cognitive functioning.
  • Biases towards “detail-oriented” thinking vs “big-picture thinking” in older populations increase overall susceptibility to financial fraud.
  • Interventions, such as personal risk assessments which prompt “big picture thinking” encourages critical thinking, and can, in turn, reduce susceptibility to financial fraud.
  • Our findings support existing literature, which demonstrates that older adults strategically change their preferred decision modes from being deliberative to more heuristic-based in order to compensate for cognitive declines in everyday functioning.

References

  1. Peters, E., Finucane, M. L., MacGregor, D. G., & Slovic, P. (2000). The bearable lightness of aging: Judgment and decision processes in older adults. The aging mind: Opportunities in cognitive research, 144-165.
  2. Besedeš, T., Deck, C., Sarangi, S., & Shor, M. (2012). Decision-making strategies and performance among seniors. Journal of economic behavior & organization, 81(2), 524-533.
  3. Best, R., & Charness, N. (2015). Age differences in the effect of framing on risky choice: A meta-analysis. Psychology and Aging, 30(3), 688–698. https://doi.org/10.1037/a0039447
  4. Denburg, N. L., Tranel, D., & Bechara, A. (2005). The ability to decide advantageously declines prematurely in some normal older persons. Neuropsychologia, 43(7), 1099–1106. https://doi.org/10.1016/j.neuropsychologia.2004.09.012
  5. Grable, J. E., McGill, S., & Britt, S. (2011). Risk Tolerance Estimation Bias: The Age Effect. Journal of Business & Economics Research (JBER), 7(7). https://doi.org/10.19030/jber.v7i7.2308
  6. Kannadhasan, M. (2015). Retail investors’ financial risk tolerance and their risk-taking behaviour: The role of demographics as differentiating and classifying factors. IIMB Management Review, 27(3), 175–184. https://doi.org/10.1016/j.iimb.2015.06.004
  7. Perez, A. M., Spence, J. S., Kiel, L. D., Venza, E. E., & Chapman, S. B. (2018). Influential Cognitive Processes on Framing Biases in Aging. Frontiers in Psychology, 9, 661. https://doi.org/10.3389/fpsyg.2018.00661
  8. Reed, A. E., & Carstensen, L. L. (2012). The Theory Behind the Age-Related Positivity Effect. Frontiers in Psychology, 3. https://doi.org/10.3389/fpsyg.2012.00339
  9. Roalf, D. R., Mitchell, S. H., Harbaugh, W. T., & Janowsky, J. S. (2012). Risk, Reward, and Economic Decision Making in Aging. The Journals of Gerontology: Series B, 67B(3), 289–298. https://doi.org/10.1093/geronb/gbr099
  10. Sumit Agarwal, John C. Driscoll, Xavier Gabaix, & David Laibson. (2009). The Age of Reason: Financial Decisions over the Life Cycle and Implications for Regulation. Brookings Papers on Economic Activity, 2009(2), 51–117. https://doi.org/10.1353/eca.0.0067
  11. Weller, J. A., Levin, I. P., & Denburg, N. L. (2011). Trajectory of risky decision making for potential gains and losses from ages 5 to 85. Journal of Behavioral Decision Making, 24(4), 331–344. https://doi.org/10.1002/bdm.690

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