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Capital Preferences has posted a comment to the Australian Treasury Department’s Quality of Advice Review expressing our perspective on the replacement of the best interest obligations with the obligation to provide ‘good advice.’ Our responses to the questions posed are shown in full below:

1) Do you agree that advisers and product issuers should be able to provide to personal advice to their customers without having to comply with all of the obligations that currently apply to the provision of personal advice?

The current industry methods of profiling consumers do not provide consumers sufficient protections or a common foundation for “good advice” to be delivered. By comparison with the medical field, which uses highly precise diagnostic tools, the financial advice industry uses no science or math-based consumer diagnostic tools before delivering advice. One of the implications is that the industry has no method to determine the decision-making quality or consistency of the consumer in an advice context.

The industry’s current consumer profiling methods (questionnaires or conversations) are imprecise, have a weak academic foundation and have no scientific accuracy/definition of what they are attempting to measure.

The removal of prescriptive advice regulations is ONLY acceptable if the regulatory authority requires the industry to adopt more precise, more repeatable, and more scientific profiling methods, such as those found in economics, namely REVEALED PREFERENCE methods.

Currently the industry has low confidence in its profiling methods, yet few participants will voluntarily undertake the cost of change without regulatory support for a deeper understanding of the clients and higher standards of profiling.

We provide a more detailed comment in question #2 below, including references to the academic body of work from the economic and decision science fields that underpin our position.

2) In your view, what impact does the replacement of the best interest obligations with the obligation to provide ‘good advice’ have on:

a) the quality of financial advice provided to consumers?

b) the time and cost required to produce advice?

“‘Good advice’ is advice that would be reasonably likely to benefit the client, having regard to the information that is available to the provider at the time the advice is provided.”

Quality of Advice Review, Consultation Paper, Aug 2022. page 8, item 3: “How should personal advice be regulated?”

The proposed principles-based approach to personal advice delivery will mean less energy and expense tied up in compliance, affording more room for innovation to bring quality advice to more Australians. However, there is great risk in the ambiguity of what ‘good advice’ means. Rather than try to prescribe what exactly good advice means, we think better to focus on the key dependency in the definition of ‘good advice’ — the information that is available to the provider. The natural question to ask is, what duty does the provider have to collect information that ensures an accurate and precise understanding of the consumer, since that is the foundation of ‘good advice’?

We argue here that the industry and its regulators should embrace state-of-the-art methods to understand consumers (i.e. to collect information from them), as a key safeguard to ensure provision of ‘good advice’ consistently, affordably and for a wide swath of Australians. Absent formation of new norms on what it means to truly understand consumers, the industry could too easily settle for a very modest level of information collection using the outdated consumer profiling methods that are currently widely used in Australia, and thereby fall far short of what the good advice duty hopes to achieve.

Look to the Medical Profession as a North Star

We believe the industry and regulators should look to the evolution of the medical field and profession as a useful analogue. Regulators don’t prescribe specifically what “good medicine” means; however, there have emerged a set of norms and expectations around patient diagnostics as a crucial component and safeguard in delivering good medicine.

That makes perfect sense—whatever its specifics from situation to situation, ‘good medicine’ is dependent on an accurate and precise diagnostic assessment of a patient’s condition. It’s easy to take this for granted, but this diagnostic foundation is critically important to a high functioning, “good medicine” healthcare system.

Moreover, looking at the medical field’s evolution, it’s easy to see what constitutes a ‘good understanding’ of a patient’s condition has not stood still over time—there have been enormous advances in patient diagnostics across the 20th and into the 21st century, from Xray machines to blood tests to MRI scans to genetic sequencing. These are dramatic improvements over use of just stethoscopes, thermometers and physician-patient dialogue (which still have their role), and have led to new norms of what ‘good medicine’ represents in 2022. Use of poor or outdated diagnostic methods surely harms the likelihood of delivering what we would today consider good medical outcomes.

Yesterday’s Consumer Diagnostics Will Not Yield ‘Good Advice’

Likewise, ‘good advice’ is dependent on an accurate and precise diagnostic assessment of consumers. That assessment includes not just goals and financial situation, but today should include each consumer’s range of preferences – encompassing their risk preferences, social preferences, and guaranteed income preferences, among others.

Today, the industry continues to rely on “stated preferences” diagnostics to collect this information—e.g., risk tolerance questionnaires developed long ago without the benefit of the recent 20+ years of advances in decision science and behavioral economics. Moreover, these questionnaires are often delivered (and biased) by human advisers, who consider them little more than “tick the box” exercises needed to comply with regulations at the outset of an advice relationship. And when it comes to preferences aside from risk, such as social preferences for ESG investing purposes, most advisers rely on client dialogue—highly prone to inaccuracy, bias and noise.

Twenty years of behavioral economics has shown that consumers are not very good at stating their preferences. Continuing to rely on these old methods would be like physicians today eschewing MRI and blood tests for the good old “tell me what hurts” bedside quiz. They’d risk losing their medical license.

It’s clear these old methods are no longer ‘good enough,’ and are unlikely to provide the safeguards that ensure consumers receive ‘good advice’.

State-of-the-Art Consumer Diagnostics Safeguard ‘Good Advice’

A new class of “revealed preference” diagnostics combines decision science, behavioral economics and gamified client experiences to precisely and efficiently (i.e., digitally) measure consumer preferences by the decisions they make. Human advisers aren’t involved in the initial measurement, removing a large source of bias and noise in results. Because these new methods can be delivered digitally, they give a productivity boost for advisers, and open the door to create low-cost digital and hybrid advice experiences grounded in deep consumer understanding. Consumers strongly prefer these methods because they are more engaging and educational than questionnaires.

Moreover, revealed preference methods yield insights about clients that simply aren’t possible with stated preference approaches. For example, revealed preferences can measure a consumer’s loss aversion separately from their risk tolerance, helping advisers and advice firms to detect which clients are likely to feel pressure to sell in down markets. This kind of behavioral “early warning” enables them to proactively engage consumers before they make decisions harmful to their long-term financial wellbeing. Similarly, these methods can measure a consumer’s ‘decision consistency’ – an economically defined gauge of how coherent a client’s risk preferences are, which safeguards advisers from recommending products or giving advice to vulnerable clients.

Finally, these methods enable measurement of the broader canvass of consumer preferences that are central to delivering ‘good advice’ in a modern context. They let us measure ‘social preferences’, which inform whether, how much and what type of ESG investments may be optimal for a consumer. They let us measure guaranteed income preferences, which guide how much of a consumer’s superannuation should be annuitised in a Retirement Income Covenant context. Because the measurement method is mathematical in nature, preferences can be combined across domains (risk, ESG, annuitisation, etc.) and among couples or households, letting the industry provide more tailored personal advice in a consistent fashion, as stated in the Quality of Advice Review. This sort of preference integration isn’t possible with conventional, stated preference approaches.

Good Advice, Delivered Consistently and Broadly to Australians

By embracing modern client diagnostics as part of a good advice duty, the industry stands to reap massive benefits, much as medicine did in adopting the X-ray, blood tests and the MRI. Advisers will spend less time profiling consumers, and gain a much richer understanding of their consumers’ true selves. Together, these benefits will let advisers and advice firms deliver good advice more often, with greater consistency, and to far more Australians from all walks of life.

Our sincere hope is that advice firms will embrace modern methods of understanding their consumers with as much fervor as they welcome the lightening of their regulatory burden via the ‘good advice’ duty.

 

Academic References

Academic research into risk preferences, economic rationality in decision-making, and revealed preferences:

  1. Ahn, D., Choi, S., Gale, D., & Kariv, S. (2014). Estimating ambiguity aversion in a portfolio choice experiment. Quantitative Economics, 5(2), 195–223. https://doi.org/10.3982/qe243
  2. Choi, S., Fisman, R., Gale, D. M., & Kariv, S. (2007). Revealing Preferences Graphically: An Old Method Gets a New Tool Kit. American Economic Review, 97(2), 153–158. https://doi.org/10.1257/aer.97.2.153
  3. Choi, S., Fisman, R., Gale, D., & Kariv, S. (2007a). Consistency and Heterogeneity of Individual Behavior under Uncertainty. American Economic Review, 97(5), 1921–1938. https://doi.org/10.1257/aer.97.5.1921
  4. Choi, S., Kariv, S., Müller, W., & Silverman, D. (2014). Who Is (More) Rational? American Economic Review, 104(6), 1518–1550. https://doi.org/10.1257/aer.104.6.1518

Academic research into social/distributional preferences:

  1. Fisman, R. J., Kariv, S., & Markovits, D. (2007). Individual Preferences for Giving. American Economic Review, 97(5), 1858-1876. https://www.aeaweb.org/articles?id=10.1257/aer.97.5.1858
  2. Fisman, R., Jakiela, P., Kariv, S., & Markovits, D. (2015). The distributional preferences of an elite. Science, 349(6254). https://doi.org/10.1126/science.aab0096
  3. Fisman, R., Jakiela, P., & Kariv, S. (2017). How did distributional preferences change during the Great Recession? Journal of Public Economics, 128, 84-95. https://doi.org/10.1016/j.jpubeco.2015.06.001
  4. Fisman, R., Jakiela, P., & Kariv, S. (2017). Distributional preferences and political behavior. Journal of Public Economics, 155, 1–10. https://doi.org/10.1016/j.jpubeco.2017.08.010
  5. Li, J., Dow, W. H., & Kariv, S. (2017). Social preferences of future physicians. Proceedings of the National Academy of Sciences, 114(48), E10291–E10300. https://doi.org/10.1073/pnas.1705451114