Financial adviser background checks

Bhanu Balasubramniana, Eric R. Briskera, Suzanne Gradishera,*
aDepartment of Finance, The University of Akron, College of Business Administration,

Akron, OH 44325, USA

Abstract

Using the 2009 National Financial Capability Survey, we identify demographic characteristics
associated with financial adviser users who conduct adviser background checks and/or consider more
than one adviser before making a choice, and if these activities improve their trust in financial
advisers. We find that very few financial adviser users check backgrounds, but there is a positive
relationship between adviser background checks and trust levels. Overall, these findings indicate that
having a reliable background check system in place, allowing financial consumers to conduct adviser
background checks in an easy and efficient manner, will help improve trust in financial advisers.
© 2014 Academy of Financial Services. All rights reserved.

Jel classification: D18; D14; G18

Keywords: Consumer protection; Personal finance; Government policy and regulation; Financial advisers

1. Introduction

We investigate characteristics associated with financial consumers who conduct financial
adviser background checks and/or consider more than one financial adviser before making a
choice, and if these activities improve the level of trust financial consumers have in their
financial advisers. Consumers typically search for information on products and services
before they buy or sell them. When a product is more expensive, the time and cost associated
with the information search usually increases. Financial decision-making is complex because
consumers must understand the risks associated with their financial products and have the
ability to project future economic scenarios and possible outcomes (Lin and Lee, 2004). As

* Corresponding author. Tel.: �1-330-972-6330; fax: �1-330-972-5970.
E-mail address: smg16@uakron.edu (S. Gradisher)

Financial Services Review 23 (2014) 305–324

1057-0810/14/$ – see front matter © 2014 Academy of Financial Services. All rights reserved.



many retirement plans continue to transition from employer-managed defined benefit plans
to employee-managed defined contribution plans, consumers are forced to make their own
financial decisions. Consumers can make these financial decisions themselves, or they can
rely on financial advisers to assist them.

As financial products and decision-making become more complex, an increasing propor-
tion of financial consumers depend on the advice of financial professionals or advisers.
Previous literature from the late 1990s and early 2000s reports that between 21% and 25%
of households use financial advisers (Elmerick, Montalto, and Fox, 2002; Lin and Lee,
2004). However, the 2009 Financial Industry Regulatory Authority (FINRA) Investor Edu-
cation Foundation’s National Financial Capability Survey (NFCS) shows that 56.7% of
households responding to the survey used a financial adviser between 2004 and 2009. As an
increasing number of consumers rely on financial advisers in their financial decision-making,
there has been an increase in the number of people offering financial adviser services, making
it imperative that financial consumers spend time in selecting a financial adviser who can best
serve their needs.

Consumers have the ability to verify that financial advisers are licensed or registered,
determine if they have been involved in professional misconduct, and ensure that they have
adequate education and professional experience to give a reasonable assurance about the
adviser’s competence, conduct, and reliability.1 Some of this information can be obtained
from the Securities and Exchange Commission (SEC). Organizations such as AARP,2

individual state agencies,3 and the Certified Financial Planner (CFP) Board4 provide infor-
mation regarding how to conduct background checks and what should be considered. Despite
the availability of this information to facilitate financial adviser background checks, we find
that only 14.2% of financial consumers responding to the NFCS survey who have used
financial advisers have checked the background of a financial adviser in the last five years.
However, we find that nearly 46% responded that they considered more than one adviser
before making a choice, indicating they see the importance associated with choosing a
competent financial adviser.

To the best of our knowledge, there is no academic study regarding financial adviser
background checks. Our study fills this gap while also adding to the growing body of
literature pertaining to financial advisers and financial consumer decision-making (Elmerick
et al., 2002; Finke, Huston, and Waller, 2009; Lachance and Tang, 2012; Ligon, 2003; Lin
et al., 2004; Jones, Lesseig, and Smythe, 2005). Using the NFCS survey, we identify the
characteristics of consumers who have conducted a background check of a financial adviser
in the last five years and/or considered more than one financial adviser before making a
choice. We also consider if these activities improve the level of trust financial consumers
have in their financial advisers.5 Throughout our analysis we consider five different types of
financial advisers to include Debt Counselors, Savings and Investments, Mortgage and Loan,
Insurance, and Tax Planning advisers.

Our article is structured as follows. We provide an overview of the background check
systems available for financial consumers in Section 2. We explain the NFCS survey and
methodology used in our analysis in Section 3, and present empirical results in Section 4. We
summarize and conclude in Section 5.

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2. Background information

Based on the Investment Advisers Act, 1940, Sec. 211 (g) (1), investment advisers are
required to register with the SEC, or with state agencies, based on the level of assets they
manage. These registered investment advisers have fiduciary responsibilities meaning they
must act in the best interest of their clients, and disclose any conflict of interest they may
have to their clients. What is troubling is that not all types of financial professionals
performing financial services have fiduciary responsibilities. For example, registered invest-
ment advisers have fiduciary responsibilities, but brokers do not have this higher fiduciary
standard, only a relatively lower “suitability” standard.6 Some financial planners, such as a
Certified Financial Planner (CFP) have fiduciary responsibilities, but several other similar
designations do not. The Dodd-Frank Act, 2010, Section 913 requires the SEC to consider
changes in how different financial professional designations are required to have fiduciary
responsibilities with their clients. The SEC is currently in the process of making new rules
for retail investors.7 Many financial consumers are not aware of these subtle legal differ-
ences. For example, Bernard Madoff was a registered broker-dealer having a significant
amount of assets under management, but he did not register as an investment adviser until
the SEC conducted an investigation in 2004. The Madoff fraud case highlights the fact that
many investors blindly trusted him, not realizing he had no fiduciary responsibility towards
them. This highlights how background verification of financial advisers is an important
activity for financial consumers.

The costs associated with using financial advisers are usually transaction fees or fees
related to total assets under management. Because the financial adviser serves as an agent for
the consumer (principal) there is also an agency cost associated with using financial advisers.
Finke et al. (2009) categorize transaction costs and asset management fees as direct cost and
the additional agency monitoring costs as indirect costs. Jensen and Meckling (1976)
subdivide the agency costs into monitoring costs, bonding costs, and residual losses.8

Monitoring costs are incurred by the financial consumer (principal) when they go through the
process of checking the background of financial advisers (agents) they are considering,
and/or consider more than one financial adviser, before establishing a contractual relation-
ship with a single adviser. We will refer to these activities as pre-selection monitoring to
indicate they occur before choosing a financial adviser. Theory predicts that the financial
consumer (principal) will stop monitoring their financial adviser (agent) when the marginal
costs of monitoring equal the marginal benefits. If the cost of searching for a financial adviser
is likely to be too high in terms of money, time, and effort it is very likely that pre-selection
monitoring will not be completed.

Prior literature considers who actually uses financial advisers and what types of informa-
tion is used by financial consumers when making financial decisions. Elmerick et al. (2002),
using the 1998 Survey of Consumer Finances, find people with higher education, income,
and personal net worth are more likely to use a financial adviser to assist them in making
financial decisions. Lin and Lee (2004), using the 2000–2001 MacroMonitor dataset, find
that education, income, risk tolerance, and the dollar amount of investment positively
influence financial consumer’s information search behavior to include what specific infor-
mation sources (i.e., internet, professional advisers, etc.) are used in helping make financial

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decisions. Lachance and Tang (2012), using the 2009 NFCS survey, examine determinants
of trust in financial professionals and the impact that trust has on the use of financial advisers.
They find that trust declines with age and increases with willingness to take investment risk;
having some financial literacy increases trust but having too much decreases trust; and that
trust and cost are the two most important determinants of financial advice-seeking behavior.

In this article we extend this prior literature by examining the characteristics associated
with financial consumers who are willing to incur pre-selection monitoring costs before
establishing a contractual relationship with a financial adviser, and if pre-selection monitor-
ing improves the level of trust financial consumers have in their financial advisers. We
consider two pre-selection monitoring activities associated with choosing a financial adviser:
(1) conducting financial adviser background checks, and (2) considering more than one
financial adviser before making a choice.

2.1. Current system of background check

Elmerick et al. (2002) show that 50% of households use stock brokers, 25% use financial
planners, 6% use accountants, 4% use bankers, and 2% use attorneys for financial advice.
Standards of professional conduct exist for CPAs, attorneys, insurance agents, CFPs with
fiduciary responsibilities, and financial brokers without fiduciary responsibilities.9 Zweig and
Pilon (2010) point out that according to FINRA, there are more than 95 professional
designations for financial advisors, and 115 others not tracked by FINRA. Ligon (2003)
predicts that in the long run these various qualitative credentials will distinguish themselves
through performance.

Brokers must be registered with the Security and Exchange Commission (SEC) and
maintain membership with the FINRA. FINRA is committed to investor protection and
market integrity through effective and efficient self-regulation of the securities industry.
They accomplish this through enforcing rules governing the activities of security firms and
brokers, enforcing educational standards such as the Series 6 licensing examinations, pro-
moting market transparency, and educating investors. FINRA also maintains the Broker-
Check10 system that can be used to check background information on brokers. BrokerCheck
has information on 1.3 million current and former FINRA-registered brokers, 17,400 current
and former FINRA-registered brokerage firms, �441,000 current and former investment
adviser representatives, and 45,700 current and former investment adviser firms.

For financial advisers, the SEC adopted rule 204A-1 requiring SEC-registered investment
advisers to adopt and enforce codes of ethics11 that establish standards of conduct expected
of supervised persons and reflect the adviser’s fiduciary duties. The SEC offers the Invest-
ment Adviser Public Disclosure (IAPD)12 website that can be used by consumers to check
the backgrounds of registered investment advisers and the firms with which they are
associated. The SEC also maintains the Investment Advisers Registration Depository
(IARD) jointly with the North American Securities Administrators Association (NASAA).
FINRA is also responsible for maintaining the IARD website, and distributes information on
financial advisers from the IARD database through their BrokerCheck system.

Out of 50 states, only 21 states have enacted regulations, or issued special notices,
regarding the use of professional designations by registered investment advisers.13 The SEC

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permits financial advisers to satisfy their filing and registration obligations under state and
federal law using a single electronic filing available at their website.14 The NASAA also
advises that consumers should check with state regulators15 when conducting background
checks of financial advisers. This would require the consumer to check 50 different regu-
lators to verify whether the financial adviser has any complaints filed anywhere in the United
States. Considering the numerous and varied databases of financial professionals and firms
that are maintained, it becomes apparent how time consuming it would be to complete a
thorough background check of financial advisers.

In a September 19, 2013, Wall Street Journal article, Daisy Maxey (Maxey, 2013) reports
that there are several online directories that aggregate basic financial adviser background
information from FINRA, IAPD, and various state databases, but she warns about the
“impartiality, conflict of interest, and possibility of abuse” that may exist in these online
directories because of financial advisers having the ability to pay money to have their names
included. In addition, it is possible for financial advisers to influence the priority of search
results on these websites. Financial consumers must pay a fee to access these aggregated
background check lists, and it is possible they are getting inaccurate or incomplete infor-
mation from these unofficial lists.

Besides the fragmentation of data for background verification among the SEC, FINRA,
and the various states, Eaglesham and Barry (2014a), in their Wall Street Journal article,
point out that securities brokers and investment advisors fail to disclose their personal
bankruptcies and other criminal charges. Such critical information is not recorded in
BrokerCheck. Numerous educational and job designations mislead financial consumers to
trust financial advisors, which is exacerbated particularly among senior citizens. Improving
the financial literacy of consumers is also emphasized in the Dodd-Frank Act, 2010 along
with establishment of institutions for consumer protection such as the Consumer Financial
Protection Bureau (CFPB). We suggest including financial adviser background checks as
part of the financial literacy campaign.

3. FINRA survey and methodology

3.1. FINRA 2009 financial capability survey

We use the 2009 FINRA Investor Education Foundation National Financial Capability
Survey (NFCS)16 that was developed in consultation with the U.S. Department of Treasury
and the President’s Advisory Council on Financial Literacy (FINRA, 2009). The FINRA
Investor Education Foundation (FINRA Foundation) conducted the online survey of 28,146
respondents (�500 respondents per state and the District of Columbia) over a five-month
period between June and October of 2009. The survey provides an unprecedented level of
data pertaining to financial behaviors across all 50 states and the District of Columbia.

The first survey question we consider is if the respondent had sought any advice from a
financial adviser in one of five specific areas within the past five years. The five specific
adviser areas included are Debt Counseling, Savings and Investments, Mortgage or Loan,
Insurance, and Tax Planning. Possible responses included: YES, NO, Do not know, and

309B. Balasubramnian et al. / Financial Services Review 23 (2014) 305–324



Prefer not to say. After limiting our sample to 27,273 respondents (from a total of 28,146
surveyed) who answered either YES or NO, we find that 15,466 of the respondents (56.7%)
used at least one of the five types of financial advisers in the past five years.

Focusing on these respondents who have used a financial adviser in the past five years, we
consider the survey questions that ask if they have ever checked with a state or federal
regulator regarding the background, registration, or license of a financial professional, and if
they typically consider more than one financial adviser before making a choice. Possible
responses to these questions include Yes and No only. We then consider how these respon-
dents answer the question of if they trust financial professionals and accept what they
recommend with possible responses being on a 1 (Strongly Disagree) to 7 (Strongly Agree)
scale with 4 (Neither) being the middle option. From the 15,466 respondents who have used
a financial adviser in the past five year, we have a final sample of 15,188 respondents who
also answered these additional questions of interest that we will use throughout our analysis.

3.2. Methodology

Using a univariate analysis, we first identify the percentage of respondents in several
demographic characteristic categories who have used a financial adviser within the past five
years and have checked the background of a financial adviser, considered more than one
adviser before making a choice, and have done both. We report these results for respondents
who have used at least one type of financial adviser (FA User) and for each specific type of
financial adviser (Debt Counselor, Savings or Investments, Mortgage of Loan, Insurance, or
Tax Planning) separately. The demographic characteristics considered include Gender, Age,
Ethnicity, Education, Marital Status, Income, Employment, and Region. These demographic
characteristics have been shown to impact personal financial behavior and the probability of
using a financial adviser when making financial decisions (Barber and Odean, 2001; Elm-
erick et al., 2002).

We then estimate a multinomial logit regression model using the same sample of respon-
dents who used at least one type of financial adviser to determine what demographic
characteristics impact the probability of a financial adviser user checking the background of
financial advisers. The dependent variable, Financial Adviser Background Checks, is set
equal to 1 if the financial adviser user checked the background of a financial adviser in the
past five years, and 0 otherwise. We also estimate a multinomial logit model to determine
what demographic characteristics impact the probability of a financial adviser user consid-
ering more than one adviser before making a choice. In this model, the dependent variable,
Considered �1 Financial Adviser, is set equal to 1 if the financial adviser user considered
more than one financial adviser before making a choice, and 0 otherwise. The demographic
characteristics included are Female, Age, Black (Non-Hispanic), Education, Marital Status,
Income, Employment, and Region. The categories within each demographic characteristic
(i.e., high school within the education demographic characteristic) are set equal to 1 if the
respondent identifies themselves to be in that categorical group, and 0 otherwise. Positive
(negative) coefficient estimates indicate that demographic characteristic is more (less) likely
to check the background of their adviser, or consider more than one financial adviser before
making a choice, compared to the reference category.

310 B. Balasubramnian et al. / Financial Services Review 23 (2014) 305–324



We also consider the likelihood of checking the background of a financial adviser, or
considering more than one financial adviser before making a choice, based on the type of
financial adviser the survey respondent has used in the past five years while controlling for
demographic characteristics. This is accomplished by estimating a similar multinomial logit
model for both Financial Adviser Background Checks and Considered �1 Financial Adviser
where we include independent dummy variables for Debt Counseling, Savings or Invest-
ments, Mortgage or Loan, Insurance, and Tax Planning that are set equal to 1 if the
respondent used that type of financial adviser in the past five years, and 0 otherwise. We
include the demographic characteristic variables as control variables in this model.

Finally, we consider the impact that conducting a background check on financial advisers,
or considering more than one financial adviser before making a choice, has on the level of
trust survey respondents have in their financial advisers. We use a Tobit regression model
where the dependent variable, Trust in Financial Adviser, is a categorical variable ranging
from 1 (Strongly Disagree) to 7 (Strongly Agree) based on the survey respondent’s answer
to the question “I would trust financial professionals and accept what they recommend.” The
independent variable of interest in the first Tobit model is Background Check that is set equal
to 1 if the respondent checked the background a financial adviser in the past five years, 0
otherwise. We also estimate a second Tobit model where the independent variable of interest
is Considered �1 Financial Adviser that is set equal to 1 if the respondent considered more
than one financial adviser before making a choice, 0 otherwise. We also include the
demographic characteristic variables as control variables in both models as it would be
expected that these characteristics would impact trust levels in financial advisers.

4. Empirical results

4.1. Summary statistics

We present the summary statistics in Table 1. Of the 15,188 surveyed who responded that
they used the services of a financial adviser in the last five years (FA Users), only 14.2%
claimed to have checked the background of a financial adviser (% Checked Background)
during that same time period. However, 45.9% of financial adviser users considered more
than one adviser before making a choice of who they used (% Considered �1 Adviser).
There were 10.5% of financial adviser users who did both of these pre-selection monitoring
activities (Do Both). We also provide these details for each category of financial adviser that
was used such as Debt Counseling, Savings and Investments, Mortgage or Loan, Insurance,
and Tax Planning. Higher percentages of financial consumers using Debt Counselors and
Tax Planning services engage in pre-selection monitoring than those using other financial
adviser types.

In Table 2 we present the number of financial adviser users (#) and percentages that
checked financial adviser backgrounds (% BG Check), considered more than one adviser
(% �1 Adviser), and that did both (Do Both) in each demographic characteristic. The
demographic characteristics considered include Gender, Age, Ethnicity, Education, Marital
Status, Income, Employment, and Region. Out of 8,115 (7,073) females (males) who have

311B. Balasubramnian et al. / Financial Services Review 23 (2014) 305–324



used a financial adviser, 12.7% (15.9%) conducted background checks, 42.9% (49.5%)
considered more than one adviser, and 9% (12.3%) did both, indicating that males engage in
more pre-selection monitoring when choosing a financial adviser compared with females.
Fewer financial adviser users in both the youngest (18–24) and oldest (65�) age groups
engage in the pre-selection monitoring, while financial consumers between 35 and 54 engage
in the most pre-selection monitoring when choosing a financial adviser. Even though there
are fewer Blacks (3,477) than Whites (11,711) using financial advisers, a much higher
percentages of Blacks (18.2%) conduct pre-selection monitoring compared with Whites
(13%). There is a monotonically increasing percentage of financial consumers engaging in
pre-selection monitoring across higher levels of both education and income. There is a larger
number of Married financial adviser users (9,542) compared with single users (3,092), but
there is a larger percentage of Single users (15.2%) conducting pre-selection monitoring.
There are a larger number of financial adviser users who have full-time employment, but a
much higher percentage of users who are self-employed engage in pre-selection monitoring
(19.9%). Results are mixed across the geographical regions with a higher percentage of
financial adviser users in the Northeast who check backgrounds (16.7%), a higher percentage
in the south (47.6%) and west (48.1%) who consider more than one adviser, and the highest
percentage in the northeast who do both (12.7%).

We present the same information for each demographic characteristic for each financial
adviser type in Table 3. The results are generally consistent across the different financial
adviser types when considering Gender, Ethnicity, Education, Marital Status, Income,

Table 1 Panel A: Presents the analysis of the 15,188 survey respondents who have used a financial adviser
in the last five years

Panel A YES NO

Have you ever checked with state or federal regulators regarding the
background, registration, or license of a financial professional?

14.2% 84.3%

Did you meet with or talk to more than one adviser before making a choice? 45.9% 45.7%

Panel B: Presents the results for each of these adviser types separately and whether the respondents verified
the background and considered more than one adviser (Do Both)

Panel B

Variable # % Checked
Background

% Considered
�1 Adviser

Do Both

FA User 15,188 14.2% 45.9% 10.5%
Debt Counselor 2,720 20.4% 54.0% 16.3%
Savings or Investments 8,647 17.5% 49.8% 13.1%
Mortgage or Loan 7,294 15.2% 49.0% 11.4%
Insurance 9,291 16.6% 50.3% 12.6%
Tax Planning 5,060 21.0% 52.7% 16.0%

We report the percentage of survey respondents that answered YES or NO to the specified question. FA User
indicates the survey respondent used at least one or more of the five different financial adviser types to include
Debt Counselor, Savings or Investments, Mortgage or Loan, Insurance, and Tax Planning. In Panel A, we present
the percentage of respondents who used financial adviser and checked the background of their adviser (%
Checked Background) or considered more than one adviser (% Considered �1 Adviser).

312 B. Balasubramnian et al. / Financial Services Review 23 (2014) 305–324



Table 2 Percent of the 15,188 survey respondents that used at least one of the five types of financial
advisers (FA User) in the last five years and claimed to have checked the background of their adviser (% BG
Check), considered more than one adviser (% �1 Adviser), and did both (Do Both) in each of the
demographic characteristic categories to include Gender, Age, Ethnicity, Education, Marital Status, Income,
Employment, and Region

FA User

# % BG Check % �1 Adviser Do Both

Gender
Male 7,073 15.9% 49.5% 12.3%
Female 8,115 12.7% 42.9% 9.0%

Age
18–24 1,277 13.7% 46.0% 10.2%
25–34 2,688 14.4% 49.0% 10.8%
35–44 2,998 15.0% 49.6% 12.1%
45–54 3,283 14.7% 46.9% 10.9%
55–64 2,601 14.6% 44.1% 10.5%
65� 2,341 12.1% 38.7% 7.8%

Ethnicity
White 11,711 13.0% 43.5% 9.2%
Black 3,477 18.2% 54.4% 14.9%

Education
� High school 250 10.4% 37.2% 7.2%
High school 2,876 10.8% 43.0% 8.0%
Some college 5,163 13.4% 47.0% 10.0%
College graduate 4,135 15.4% 46.3% 11.5%
Post graduate 2,764 17.7% 47.4% 12.9%

Marital status
Married 9,542 14.2% 45.6% 10.4%
Single 3,092 15.2% 48.5% 11.5%
Separated 214 14.5% 50.0% 10.7%
Divorced 1,682 12.4% 44.9% 9.3%
Widowed 658 14.9% 40.4% 10.8%

Income
�$15,000 1,071 11.3% 43.1% 8.7%
$15K–$24,999 1,502 11.6% 43.1% 8.6%
$25K–$34,999 1,697 11.8% 44.1% 8.8%
$35K–$49,999 2,426 13.0% 45.3% 9.7%
$50K–$74,999 3,252 13.7% 45.8% 9.6%
$75K–$99,999 2,081 16.4% 47.4% 12.3%
$100K–$149,000 1,930 16.8% 47.7% 12.6%
$150,000 1,229 19.3% 51.3% 14.5%

Employment
Self employed 1,536 19.9% 53.1% 14.8%
Full-time 6,422 14.2% 46.4% 11.0%
Part-time 1,376 11.4% 44.1% 8.6%
Homemaker 1,205 12.3% 45.4% 9.0%
Student 536 14.9% 49.3% 11.4%
Disabled 462 14.3% 43.9% 8.9%
Unemployed 1,070 14.5% 48.7% 10.6%
Retired 2,581 13.0% 40.4% 8.6%

Region
Northeast 2,637 16.7% 45.3% 12.7%
Midwest 3,655 11.6% 42.0% 8.1%
South 4,927 14.0% 47.6% 10.6%
West 3,969 15.2% 48.1% 11.1%

The five adviser types include Debt Counselor, Savings or Investments, Mortgage or Loan, Insurance, and Tax
Planning.

313B. Balasubramnian et al. / Financial Services Review 23 (2014) 305–324



Table 3 Percent of the survey respondents that used a specific type of financial adviser in the past five years
and claimed to have checked the background of their adviser (% BG Check), considered more than one
adviser (% �1 Adviser), and did both (Do Both) in each of the demographic characteristic categories to
include Gender, Age, Ethnicity, Education, Marital Status, Income, Employment, and Region

Panel A: Debt Counselors and Savings or Investments

Debt Counselor Savings or Investments

# % BG
Check

% �1
Adviser

Do Both # % BG
Check

% �1
Adviser

Do Both

Gender
Male 1,238 23.8% 58.3% 19.6% 4,199 19.4% 53.9% 15.3%
Female 1,482 17.6% 50.4% 13.4% 4,448 15.7% 46.0% 11.1%

Age
18–24 236 26.3% 60.2% 20.3% 694 17.7% 50.4% 13.7%
25–34 660 21.7% 55.2% 18.5% 1,415 20.1% 55.9% 15.4%
35–44 659 20.2% 55.7% 15.8% 1,541 19.8% 54.6% 16.2%
45–54 626 19.0% 51.6% 15.0% 1,770 17.3% 51.4% 13.2%
55–64 350 19.1% 51.7% 14.6% 1,635 17.3% 48.0% 12.4%
65� 189 16.9% 48.7% 12.2% 1,592 13.3% 39.6% 8.7%

Ethnicity
White 1,815 18.0% 49.9% 13.4% 6,779 15.8% 47.1% 11.3%
Black 905 25.4% 62.2% 21.9% 1,868 23.8% 59.5% 20.0%

Education
�High school 55 12.7% 47.3% 5.5% 94 19.1% 43.6% 11.7%
High school 584 16.6% 55.5% 13.4% 1,301 13.8% 46.3% 10.5%
Some college 1,045 17.7% 53.0% 13.9% 2,778 17.0% 51.7% 12.6%
College graduate 692 24.3% 53.9% 19.8% 2,534 18.6% 49.7% 14.1%
Post graduate 344 28.8% 55.8% 23.0% 1,940 19.1% 50.0% 14.5%

Marital status
Married 1,553 20.5% 53.6% 15.9% 5,526 17.2% 49.2% 12.7%
Single 669 22.4% 58.4% 18.8% 1,748 19.2% 53.8% 15.2%
Separated 63 20.6% 60.3% 17.5% 93 21.5% 60.2% 15.1%
Divorced 352 15.3% 48.3% 11.9% 878 15.7% 48.4% 12.1%
Widowed 83 24.1% 44.6% 19.3% 402 16.4% 41.0% 12.2%

Income
�$15,000 255 22.0% 51.4% 16.9% 488 15.6% 48.8% 11.9%
$15K–$24,999 371 15.9% 51.8% 12.7% 663 14.8% 48.7% 11.2%
$25K–$34,999 401 15.5% 55.9% 12.2% 836 14.6% 46.8% 11.4%
$35K–$49,999 497 18.9% 53.3% 15.5% 1,250 17.0% 49.9% 13.0%
$50K–$74,999 600 18.3% 52.5% 14.0% 1,872 17.2% 48.8% 12.3%
$75K–$99,999 311 24.4% 57.6% 19.6% 1,304 19.2% 50.7% 14.5%
$100K–$149,000 201 33.3% 55.2% 26.9% 1,335 18.5% 50.9% 14.2%
�$150,000 84 38.1% 61.9% 32.1% 899 20.5% 53.2% 15.2%

Employment
Self employed 284 26.1% 64.8% 21.1% 929 22.9% 58.2% 17.3%
Full-time 1,257 20.3% 52.4% 16.6% 3,689 17.9% 51.2% 14.2%
Part-time 236 15.7% 52.5% 13.1% 779 14.8% 47.0% 11.4%
Homemaker 205 17.1% 47.8% 11.2% 586 15.0% 49.3% 10.8%
Student 107 28.0% 58.9% 20.6% 320 18.1% 54.1% 13.8%
Disabled 121 17.4% 38.0% 10.7% 170 20.0% 44.7% 10.6%
Unemployed 259 23.2% 62.9% 18.1% 506 19.6% 52.6% 14.0%
Retired 251 17.5% 52.6% 14.7% 1,668 14.7% 42.4% 10.0%

Region
Northeast 436 26.8% 53.4% 21.6% 1,613 21.1% 49.3% 16.2%
Midwest 669 14.5% 51.4% 11.7% 2,116 14.0% 45.4% 9.6%
South 934 21.7% 55.6% 17.2% 2,712 17.3% 52.1% 13.5%
West 681 20.4% 54.8% 16.0% 2,206 18.4% 51.6% 13.9%

314 B. Balasubramnian et al. / Financial Services Review 23 (2014) 305–324



Table 3 Continued

Panel B: Mortgage or Loan and Insurance

Mortgage or Loan Insurance

# % BG
Check

% �1
Adviser

Do Both # % BG
Check

% �1
Adviser

Do Both

Gender
Male 3,374 17.4% 52.0% 13.6% 4,347 18.8% 53.6% 14.9%
Female 3,920 13.2% 46.5% 9.4% 4,944 14.7% 47.3% 10.6%

Age
18–24 563 16.0% 47.4% 12.4% 694 16.4% 50.6% 12.5%
25–34 1,622 15.0% 50.6% 11.0% 1,756 16.5% 53.4% 12.8%
35–44 1,718 15.2% 51.3% 12.1% 1,896 18.2% 53.3% 14.5%
45–54 1,574 16.3% 49.9% 12.5% 2,125 16.6% 50.6% 12.6%
55–64 1,058 15.9% 47.3% 12.0% 1,563 17.7% 49.1% 13.1%
65� 759 11.5% 42.6% 6.5% 1,257 13.2% 42.2% 9.0%

Ethnicity
White 5,662 13.4% 46.7% 9.6% 7,116 15.1% 47.8% 11.0%
Black 1,632 21.2% 57.2% 17.5% 2,175 21.6% 58.3% 18.0%

Education
�High school 91 8.8% 35.2% 4.4% 153 11.1% 34.6% 7.2%
High school 1,213 12.4% 45.6% 9.3% 1,701 12.6% 47.5% 9.6%
Some college 2,430 14.4% 50.0% 10.9% 3,168 15.0% 51.0% 11.4%
College graduate 2,160 16.0% 49.9% 12.0% 2,564 18.4% 51.1% 14.3%
Post graduate 1,400 17.9% 49.9% 13.3% 1,705 21.5% 51.8% 15.9%

Marital status
Married 5,014 14.3% 48.4% 10.5% 5,988 16.4% 49.7% 12.4%
Single 1,264 18.2% 52.6% 14.4% 1,789 17.7% 53.7% 13.9%
Separated 103 18.4% 49.5% 14.6% 131 19.1% 50.4% 13.0%
Divorced 684 14.9% 49.1% 11.4% 1,016 15.3% 49.3% 11.5%
Widowed 229 16.2% 43.2% 11.4% 367 17.2% 45.8% 12.5%

Income
�$15,000 331 15.7% 47.7% 11.5% 625 12.8% 47.0% 9.8%
$15K–$24,999 552 13.4% 47.6% 10.3% 897 12.9% 46.9% 9.8%
$25K–$34,999 676 12.9% 46.6% 9.5% 1,019 12.9% 50.2% 10.1%
$35K–$49,999 1,165 13.9% 48.1% 10.6% 1,494 15.7% 49.6% 11.8%
$50K–$74,999 1,684 13.5% 49.3% 9.8% 1,954 16.6% 51.1% 11.9%
$75K–$99,999 1,163 17.3% 48.9% 13.2% 1,305 18.8% 51.5% 14.6%
$100K–$149,000 1,081 16.4% 50.7% 12.3% 1,203 19.5% 49.7% 15.2%
�$150,000 642 19.5% 52.0% 14.6% 794 22.8% 54.7% 17.1%

Employment
Self employed 793 21.2% 54.9% 15.9% 1,037 22.1% 55.5% 16.3%
Full-time 3,552 14.6% 48.5% 11.5% 3,967 17.2% 51.1% 13.7%
Part-time 559 13.1% 48.3% 10.6% 839 13.6% 47.3% 10.6%
Homemaker 647 12.7% 48.5% 9.4% 758 13.5% 49.9% 10.0%
Student 219 18.3% 53.4% 14.6% 293 17.1% 58.4% 13.7%
Disabled 195 18.5% 48.2% 10.8% 310 15.8% 45.5% 9.4%
Unemployed 464 16.4% 51.5% 11.0% 646 16.9% 53.7% 12.5%
Retired 865 13.2% 44.7% 8.3% 1,441 14.6% 44.0% 10.1%

Region
Northeast 1,195 18.6% 48.2% 14.5% 1,535 19.9% 49.1% 15.8%
Midwest 1,689 12.1% 44.6% 8.5% 2,262 13.3% 45.9% 9.3%
South 2,361 15.0% 50.7% 11.6% 3,025 16.5% 52.5% 13.0%
West 2,049 15.9% 51.3% 11.7% 2,469 17.8% 52.3% 13.2%

315B. Balasubramnian et al. / Financial Services Review 23 (2014) 305–324



Table 3 Continued

Panel C: Tax Planning

Tax Planning

# % BG Check % �1 Adviser Do Both

Gender
Male 2,457 23.8% 56.0% 18.8%
Female 2,603 18.3% 49.6% 13.3%

Age
18–24 383 23.0% 53.0% 17.5%
25–34 905 23.5% 56.5% 18.0%
35–44 959 22.7% 55.5% 18.1%
45–54 1,034 21.3% 55.1% 16.6%
55–64 903 19.6% 51.6% 15.0%
65� 876 16.7% 44.1% 11.1%

Ethnicity
White 3,937 18.5% 49.8% 13.4%
Black 1,123 29.6% 62.9% 25.0%

Education
� High school 56 19.6% 33.9% 10.7%
High school 713 19.1% 48.8% 15.1%
Some college 1,522 19.6% 55.0% 14.5%
College graduate 1,521 21.4% 52.5% 16.2%
Post graduate 1,248 23.2% 53.4% 18.2%

Marital status
Married 3,496 19.9% 52.4% 15.0%
Single 867 24.8% 56.2% 19.4%
Separated 58 34.5% 55.2% 25.9%
Divorced 425 20.5% 52.9% 16.2%
Widowed 214 21.5% 43.5% 15.4%

Income
�$15,000 217 23.5% 52.5% 16.6%
$15K–$24,999 331 18.1% 49.2% 13.0%
$25K–$34,999 392 19.9% 51.8% 16.1%
$35K–$49,999 691 20.8% 53.1% 15.5%
$50K–$74,999 1,099 19.4% 52.0% 13.7%
$75K–$99,999 794 21.9% 52.0% 18.0%
$100K–$149,000 830 21.7% 53.9% 17.3%
�$150,000 706 22.9% 55.2% 17.1%

Employment
Self employed 729 22.2% 58.4% 17.7%
Full-time 2,055 22.4% 54.1% 17.7%
Part-time 464 18.8% 49.1% 14.2%
Homemaker 377 15.9% 52.3% 10.9%
Student 151 25.8% 58.3% 18.5%
Disabled 74 28.4% 47.3% 16.2%
Unemployed 286 25.2% 52.8% 19.9%
Retired 924 17.4% 46.6% 12.1%

Region
Northeast 964 24.6% 52.8% 19.2%
Midwest 1,212 16.1% 47.9% 11.3%
South 1,547 21.3% 54.3% 16.7%
West 1,337 22.5% 55.3% 17.1%

Panel A reports results for Debt Counselor and Savings or Investments advisers, Panel B reports results for
Mortgage or Loan and Insurance advisers, and Panel C reports results for Tax Planning advisers.

316 B. Balasubramnian et al. / Financial Services Review 23 (2014) 305–324



Employment, and Region. However, the results are more mixed when considering Age. There
is a higher percentage of the 18–24 age group conducting pre-selection monitoring when
considering Debt Counselors (Panel A). All other results regarding Age are mixed.

4.2. Regression results

In Table 4 we present multinomial logit regression results that indicate how the different
demographic characteristics impact the probability of conducting a background check on a
financial adviser in the past five years (left side of Table 4) and the probability of considering
more than one financial adviser before making a choice (right side of Table 4). We find that
Females are 20% less likely to have checked the background of financial advisers compared
with males. We do not observe any significant differences among age groups except in the
65� age group that is, on average, 21.7% less likely to check financial adviser backgrounds
compared with the 18–24 age group. We also find there are no significant differences among
the different education groups except that financial adviser users with post-graduate educa-
tion are 50% more likely to check financial adviser backgrounds compared with those who
did not complete high school. Compared with married financial adviser users, living with
partner (single) users are nearly 20% (14%) more likely to check financial adviser back-
grounds. Financial adviser users with more than $35,000 are more likely to check financial
adviser backgrounds compared with users earning less than $35,000, with this probability
increasing monotonically across higher income levels. All the employment categories are
less likely to check financial adviser backgrounds compared with self-employed users with
full-time users being 37.4% less likely to check financial adviser backgrounds compared with
self-employed users. Finally, financial adviser users living in the Midwest, south, or west are
all less likely to check financial adviser backgrounds compared with those living in northeast,
with those in Midwest region being 30% less likely compared with those in the northeast
region.

The results for the multinomial logit regression that considers the probability of consid-
ering more than one financial adviser are reported on the right side of Table 4. Consistent
with the background check results, females are 20% less likely than males, and Blacks are
over 40% more likely than Whites, to consider more than one financial adviser before making
a choice. There also remains a monotonic increase in the probability of considering more
than one adviser across higher income levels, no statistically significant difference across
education levels, and married couples remain less likely to consider more than one adviser
compared with those that are living with a partner or single. Self-employed financial adviser
users remain more likely to consider more than one adviser which is also consistent with the
background check results. However, we find financial consumers between the ages of 25 and
44 are more likely to consider more than one financial adviser compared with the 18–24 age
group whereas those 65� are almost 22% less likely to consider more than one financial
adviser. This is somewhat different for the 25 to 44 age group, but also points out that the
65� age group is the least likely to conduct any pre-selection monitoring before choosing a
financial adviser. We also find that financial adviser users from the Midwest are less likely
to consider more than one adviser compared with those from the northeast that is consistent
with our background check tests, however, there is no significant difference between the

317B. Balasubramnian et al. / Financial Services Review 23 (2014) 305–324



remaining regions indicating that users in the south and west are just as likely to consider
more than one financial adviser as those in the northeast.

Table 4 Multinomial logit regression using 15,188 survey respondents who have used a financial adviser in
the past five years where the dependent variable, Financial Adviser Background Checks, is set equal to 1 if
the respondent checked the background of a financial adviser in the past five years, 0 otherwise

Financial Adviser Background Checks Considered �1 Financial Advisers

Coefficient z-stat Standard
error

Odds
ratio

Coefficient z-stat Standard
error

Odds
ratio

Constant �1.849*** �7.21 0.256 0.157 �0.152 �0.85 0.179 0.859
Female �0.219*** �4.45 0.049 0.803 �0.227*** �6.31 0.036 0.797
Age group (reference category: 18–24)

25–34 �0.045 �0.42 0.107 0.956 0.175** 2.26 0.078 1.192
35–44 �0.043 �0.40 0.108 0.958 0.163** 2.09 0.078 1.177
45–54 �0.042 �0.39 0.108 0.959 0.057 0.74 0.078 1.059
55–64 �0.070 �0.61 0.115 0.932 �0.056 �0.67 0.083 0.946
65� �0.244* �1.83 0.133 0.783 �0.242** �2.57 0.094 0.785

Black 0.421*** 7.63 0.055 1.523 0.345*** 8.11 0.043 1.413
Education (reference category: Did not complete high school)

High school 0.013 0.06 0.218 1.013 0.137 0.93 0.147 1.147
Some college 0.197 0.91 0.215 1.217 0.261* 1.79 0.146 1.298
College graduate 0.291 1.34 0.217 1.338 0.171 1.16 0.148 1.186
Post graduate 0.405* 1.84 0.220 1.499 0.191 1.27 0.151 1.211

Marital status (reference category: Married)
Living with partner 0.181** 1.98 0.091 1.198 0.181*** 2.60 0.070 1.198
Single 0.128** 2.10 0.061 1.136 0.073* 1.65 0.044 1.076

Income (reference category: Less than $15,000)
$15,000–$24,999 0.119 0.92 0.130 1.126 0.058 0.66 0.088 1.060
$25,000–$34,999 0.186 1.44 0.129 1.205 0.134 1.51 0.089 1.144
$35,000–$49,999 0.296** 2.38 0.124 1.345 0.159* 1.85 0.086 1.172
$50,000–$74,999 0.355*** 2.86 0.124 1.426 0.162* 1.88 0.086 1.176
$75,000–$99,999 0.529*** 4.04 0.131 1.697 0.216** 2.32 0.093 1.241
$100,000–$149,999 0.546*** 4.04 0.135 1.726 0.197** 2.04 0.096 1.218
$150,000 or more 0.685*** 4.80 0.143 1.985 0.360*** 3.45 0.104 1.433

Employment (reference category: Self employed)
Employed full-time �0.468*** �6.21 0.075 0.626 �0.345*** �5.64 0.061 0.709
Employed part-time �0.509*** �4.67 0.109 0.601 �0.275*** �3.45 0.080 0.760
Homemaker �0.309*** �2.68 0.115 0.734 �0.129 �1.50 0.086 0.879
Full-time student �0.287* �1.88 0.152 0.751 �0.167 �1.44 0.116 0.846
Disabled �0.133 �0.87 0.153 0.876 �0.205* �1.76 0.116 0.815
Unemployed �0.219** �1.96 0.112 0.803 �0.122 �1.41 0.086 0.885
Retired �0.244** �2.37 0.103 0.783 �0.277*** �3.54 0.078 0.758

Region (reference category: Northeast)
Midwest �0.357*** �4.79 0.074 0.700 �0.127** �2.35 0.054 0.881
South �0.213*** �3.15 0.068 0.808 0.072 1.41 0.051 1.075
West �0.139** �1.99 0.070 0.870 0.080 1.49 0.053 1.083

The dependent variable, Considered �1 Financial Adviser, is set equal to 1 if the respondent considered more
than one financial adviser before making a choice, 0 otherwise. The independent variables include Female, Age,
Black (Non-Hispanic), Education, Marital Status, Income, Employment, and Region. Positive (negative) coeffi-
cient estimates indicate that demographic characteristic is more (less) likely to check the background of their
adviser, or consider more than one financial adviser before making a choice, compared with the indicated
reference category. ***,**,* denote significance at the 1%, 5%, and 10% levels, respectively.

318 B. Balasubramnian et al. / Financial Services Review 23 (2014) 305–324



Table 5 reports results where we estimate the same multinomial logit model with
additional dummy variables included that indicate if the financial adviser user used that
specific type of financial adviser. This allows us to estimate the probably of checking the
background of a financial adviser (left side of table), or considering more than one financial
adviser (right side of table), based on the type of financial adviser that was used while
controlling for the demographic characteristics included earlier that have already been shown
to impact these same probabilities. There are five different financial adviser types included:
Debt Counseling, Savings and Investments, Mortgage or Loan, Insurance, and Tax Planning.
Consistent with the univariate results reported in Table 1, we find, after controlling for all
demographic characteristics, those using Debt Counseling or Tax Planning services are more
likely to check the backgrounds of their financial advisers. Those using Insurance advisers
are the least likely to check backgrounds, whereas there is no statistically significant
relationship between checking backgrounds and using Mortgage or Loan services. However,
we find that those using Debt Counselors and Insurance advisers as most likely to consider
more than one financial adviser before making a choice. Those using Savings or Investments
and Mortgage or Loan advisers are least likely to consider more than one adviser before
choosing. All of these results are statistically significant at the 1% level.

4.3. Do financial consumers who conduct pre-selection monitoring have higher levels of
trust in their financial advisers?

Next, we consider if those financial adviser users who conduct some form of pre-selection
monitoring before choosing a financial adviser have a higher level of trust in the financial
advisers they use. We use the responses to the survey question: “I would trust financial
professionals and accept what they recommend.” We report the percentage of financial
adviser users who selected each of the available responses to include Strongly Disagree,
Disagree, Neither Agree nor Disagree, Agree, and Strongly Agree in Table 6. Overall, out
of the 15,188 financial adviser users, there are nearly 40% who agree, 34% who neither agree
nor disagree, and over 23% who disagree with this statement (some users did not answer this
question).

In Table 7, we estimate a Tobit regression model using the full sample of 15,188 financial
adviser users to determine the impact that pre-selection monitoring has on the answer to this
question. We control for the all the demographic characteristics used earlier and include
either Background Check (left side of Table 7) as an additional dummy variable indicating
if the user conducted a financial adviser background check, or Considered �1 Financial
Adviser (right side of Table 7) as an additional dummy variable indicating if the user
considered more than one financial adviser before making a choice. Overall, we find many
demographic characteristics are not statistically related to financial adviser trust levels such
as Gender, Education, and Income. Consistent with Lachance and Tang (2012), we find that
trust levels generally decline with age. We also find that Living with Partner couples have
lower trust levels, and people in the Midwest have higher trust levels than those from other
regions. Most importantly, we find a positive and highly significant (significant at the 1%
level) relationship between checking the background of financial advisers and financial

319B. Balasubramnian et al. / Financial Services Review 23 (2014) 305–324



Table 5 Multinomial Logit regression using 15,188 survey respondents who have used a financial adviser in
the past five years where the dependent variable, Financial Adviser Background Checks, is set equal to 1 if
the respondent checked the background of a financial adviser in the last five years, 0 otherwise

Financial Adviser Background Checks Considered �1 Financial Adviser

Coefficient z-stat Standard
error

Odds
ratio

Coefficient z-stat Standard
error

Odds
ratio

Constant �2.760*** �10.43 0.265 0.0633 �0.746*** �4.06 0.184 0.474
Debt Counseling 0.602*** 10.21 0.059 1.825 0.364*** 7.75 0.047 1.439
Savings or Investments 0.485*** 9.02 0.054 1.624 0.221*** 5.96 0.037 1.248
Mortgage or Loan 0.077 1.55 0.050 1.080 0.214*** 5.94 0.036 1.239
Insurance 0.416*** 7.85 0.053 1.516 0.388*** 10.78 0.036 1.474
Tax Planning 0.561*** 11.01 0.051 1.752 0.247*** 6.44 0.038 1.281
Female �0.209*** �4.16 0.050 0.812 �0.234*** �6.44 0.036 0.791
Age group (reference category: 18–24)

25–34 �0.095 �0.87 0.110 0.909 0.119 1.51 0.079 1.127
35–44 �0.020 �0.18 0.110 0.980 0.153* 1.93 0.079 1.165
45–54 �0.009 �0.08 0.110 0.991 0.063 0.79 0.079 1.065
55–64 �0.020 �0.17 0.117 0.980 �0.024 �0.29 0.084 0.976
65� �0.180 �1.32 0.136 0.835 �0.185* �1.93 0.096 0.831

Black (Non-Hispanic) 0.368*** 6.49 0.057 1.445 0.329*** 7.60 0.043 1.390
Education (reference category: Did not complete high school)

High school �0.012 �0.05 0.221 0.988 0.139 0.93 0.149 1.149
Some college 0.110 0.50 0.218 1.116 0.222 1.51 0.147 1.248
College graduate 0.179 0.81 0.220 1.196 0.119 0.80 0.149 1.126
Post graduate 0.254 1.14 0.224 1.290 0.122 0.80 0.153 1.130

Marital Status (reference category: Married)
Living with partner 0.212** 2.28 0.093 1.236 0.200*** 2.84 0.070 1.221
Single 0.156** 2.50 0.062 1.168 0.109** 2.41 0.045 1.115

Income (reference category: Less than $15,000)
$15,000–$24,999 0.107 0.81 0.132 1.112 0.042 0.47 0.089 1.043
$25,000–$34,999 0.149 1.13 0.132 1.160 0.109 1.21 0.090 1.115
$35,000–$49,999 0.234* 1.84 0.127 1.263 0.115 1.32 0.087 1.122
$50,000–$74,999 0.261** 2.06 0.127 1.298 0.106 1.20 0.088 1.112
$75,000–$99,999 0.415*** 3.09 0.134 1.514 0.146 1.53 0.095 1.157
$100,000–$149,999 0.411*** 2.96 0.139 1.508 0.123 1.25 0.099 1.131
$150,000 or more 0.497*** 3.38 0.147 1.643 0.263** 2.46 0.107 1.301

Employment (reference category: Self employed)
Employed full-time �0.344*** �4.44 0.077 0.709 �0.280*** �4.51 0.062 0.756
Employed part-time �0.415*** �3.73 0.111 0.661 �0.206** �2.55 0.081 0.814
Homemaker �0.165 �1.40 0.118 0.848 �0.046 �0.53 0.087 0.955
Full-time student �0.162 �1.05 0.155 0.850 �0.079 �0.67 0.118 0.924
Disabled 0.038 0.25 0.156 1.039 �0.141 �1.19 0.118 0.869
Unemployed �0.108 �0.94 0.114 0.898 �0.052 �0.60 0.088 0.949
Retired �0.145 �1.38 0.105 0.865 �0.209*** �2.63 0.079 0.812

Region (reference category: Northeast)
Midwest �0.372*** �4.91 0.076 0.689 �0.143*** �2.61 0.055 0.867
South �0.203*** �2.93 0.069 0.816 0.070 1.35 0.052 1.072
West �0.120* �1.68 0.071 0.887 0.071 1.32 0.054 1.074

The dependent variable, Considered �1 Financial Adviser, is set equal to 1 if the respondent considered more
than one financial adviser before making a choice, 0 otherwise. The independent variables Debt Counseling,
Savings or Investments, Mortgage or Loan, Insurance, Tax Planning are variables set equal to 1 if the respondents
used that type of financial adviser in the past five years, 0 otherwise. The other independent variables include
Female, Age, Black (Non-Hispanic), Education, Marital Status, Income, Employment, and Region. Positive
(negative) coefficient estimates indicate that demographic characteristic is more (less) likely to check the
background of their adviser, or consider more than one financial adviser before making a choice, compared with
the indicated reference category. ***,**,* denote significance at the 1%, 5%, and 10% levels, respectively.

320 B. Balasubramnian et al. / Financial Services Review 23 (2014) 305–324



adviser trust levels. However, we find no significant relationship between considering more
than one financial adviser and trust levels in financial advisers.

In summary, these results may indicate that checking a financial adviser’s background
helps develop trust in that financial adviser. However, simply considering more than one
adviser does not necessarily develop higher trust levels in the financial adviser that is
eventually chosen. Perhaps post-selection monitoring is more important for financial adviser
users who only consider more than one adviser as their only pre-selection monitoring
activity. Overall, our results indicate that having a reliable system in place that allows
financial consumers to conduct background checks of financial advisers in an easy and
efficient manner may help develop trust in financial advisers.

5. Summary and conclusions

Using the 2009 NFCS conducted by FINRA we identify demographic characteristics
associated with financial consumers who conduct prescreening monitoring when choosing
financial advisers. The two prescreening monitoring considered include checking the back-
ground of financial advisers and considering more than one financial adviser before making
a choice. We also test if these prescreening monitoring improve the level of trust financial
consumers have in their financial advisers. We consider five different financial adviser types
throughout our study to include Debt Counseling, Savings and Investments, Mortgage and
Loan, Insurance, and Tax Planning.

We find that only 14.2% of financial adviser users responding to the NFCS survey have
checked the background of a financial adviser in the past five years, but nearly 46% of these
financial adviser users considered more than one adviser before making a choice. Higher
percentages of financial consumers using Debt Counselors and Tax Planning services engage
in pre-selection monitoring than those using other financial adviser types. Based on multi-
nomial logit regression estimates, we find that females are less likely, and Blacks (compared
with Whites) more likely, to conduct pre-selection monitoring. We also find the probability
of conducting pre-selection monitoring is monotonically increasing at higher income levels
above $35,000/year, but decreases significantly for consumers over the age of 65. Finally,
financial consumers who are self-employed, and those living in the northeast (compared with
the Midwest, west, and south) are more likely to conduct pre-selection monitoring.

Table 6 Responses from 15,188 survey respondents who have used a financial adviser in the past five years
to the question regarding their level of trust in financial professionals and if they would accept what they
recommend

I would trust financial professionals and accept what they recommend.

Strongly Disagree 5.4%
Disagree 18.2%
Neither Agree nor Disagree 34.0%
Agree 32.7%
Strongly Agree 6.6%

Total percentages to answer in each category ranging from 1 (Strongly Disagree) to 7 (Strongly Agree), with
the middle category 4 indicating that they neither agree nor disagree.

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Table 7 Tobit regression using 15,188 survey respondents who have used a financial adviser in the past
five years where the dependent variable, Trust Financial Adviser, is a categorical variable ranging from
1 (Strongly Disagree) to 7 (Strongly Agree)

Trust in Financial Adviser Trust in Financial Adviser

Coefficient t-stat Standard
error

Coefficient t-stat Standard
error

Constant 4.642*** 31.97 0.145 4.671*** 32.12 0.145
Background Check 0.108*** 2.60 0.041
Considered �1 Financial Adviser �0.001 �1.30 0.001
Female 0.034 1.14 0.030 0.033 1.11 0.030
Age group (reference category: 18–24) 0 0 0

25–34 �0.194*** �3.01 0.064 �0.194*** �3.00 0.064
35–44 �0.506*** �7.78 0.065 �0.505*** �7.78 0.065
45–54 �0.680*** �10.53 0.065 �0.680*** �10.53 0.065
55–64 �0.776*** �11.28 0.069 �0.776*** �11.28 0.069
65� �0.698*** �8.86 0.079 �0.701*** �8.90 0.079

Black (Non-Hispanic) �0.007 �0.19 0.036 �0.003 �0.09 0.036
Education (reference category: Did not complete high school) 0 0 0

High school �0.051 �0.43 0.118 �0.055 �0.47 0.118
Some college �0.024 �0.21 0.117 �0.027 �0.23 0.117
College graduate �0.008 �0.06 0.118 �0.009 �0.07 0.118
Post graduate �0.021 �0.17 0.121 �0.022 �0.18 0.121

Marital Status (reference category: Married) 0 0 0
Living with partner �0.170*** �2.95 0.057 �0.167*** �2.91 0.057
Single 0.022 0.58 0.037 0.022 0.60 0.037

Income (reference category: Less than $15,000) 0 0 0
$15,000–$24,999 0.011 0.15 0.073 0.011 0.15 0.073
$25,000–$34,999 0.085 1.16 0.073 0.085 1.17 0.073
$35,000–$49,999 0.090 1.27 0.071 0.091 1.28 0.071
$50,000–$74,999 0.114 1.61 0.071 0.115 1.62 0.071
$75,000–$99,999 0.131* 1.70 0.077 0.134* 1.74 0.077
$100,000–$149,999 0.095 1.18 0.080 0.096 1.20 0.080
$150,000 or more 0.124 1.43 0.087 0.128 1.47 0.087

Employment (reference category: Self employed) 0 0 0
Employed full-time 0.190*** 3.74 0.051 0.184*** 3.62 0.051
Employed part-time 0.077 1.16 0.066 0.069 1.04 0.066
Homemaker �0.009 �0.13 0.071 �0.013 �0.19 0.071
Full-time student 0.233** 2.42 0.096 0.229** 2.38 0.096
Disabled 0.009 0.09 0.096 0.008 0.08 0.096
Unemployed 0.112 1.56 0.072 0.108 1.51 0.072
Retired 0.117* 1.78 0.066 0.111* 1.70 0.066

Region (reference category: Northeast) 0 0 0
Midwest 0.118*** 2.62 0.045 0.114** 2.52 0.045
South 0.026 0.60 0.043 0.023 0.53 0.043
West �0.036 �0.80 0.045 �0.037 �0.83 0.045

The independent variable of interest are Background Check that is set equal to 1 if the respondent checked the
background of a financial adviser in the last five years, 0 otherwise, and Considered �1 Financial Adviser, which
is set equal to 1 if the respondent considered more than one financial adviser before making a choice, 0 otherwise.
The other independent variables include Female, Age, Black (Non-Hispanic), Education, Marital Status, Income,
Employment, and Region. Positive (negative) coefficient estimates indicate that variable is more (less) likely to
trust their financial adviser compared with the indicated reference category. ***,**,* denote significance at the
1%, 5%, and 10% levels, respectively.

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When considering the specific financial adviser types, we find that those using Debt
Counseling or Tax Planning services are more likely to check the backgrounds of their
financial advisers, and those using Insurance advisers are the least likely to check back-
grounds. Those using Debt Counselors and Insurance advisers are most likely, and those
using Savings or Investments and Mortgage or Loan advisers are least likely, to consider
more than one financial adviser before making a choice.

We also consider how conducting pre-selection monitoring, controlling for demographic
characteristics, impact the level of trust financial adviser users have in their financial
advisers. We find that there is a positive relationship between financial adviser background
checks and financial adviser trust levels. However, we find no significant relationship
between considering more than one financial adviser and trust levels in financial advisers.
We also find that trust levels generally decline with age, which is consistent with Lachance
and Tang (2012) findings. We also find that Living with Partner couples have lower trust
levels (compared with married couples and those who are single), and people in the Midwest
have higher trust levels than those from other regions.

In summary, these results show that checking a financial adviser’s background helps
develop trust in that financial adviser. However, simply considering more than one adviser
does not necessarily develop higher trust levels in the financial adviser that is eventually
chosen. Overall, our results indicate that having a reliable and well-known background check
system in place that allows financial consumers to conduct background checks of financial
advisers in an easy and efficient manner may help improve trust in financial advisers.

Notes

1 http://www.sec.gov/investor/brokers.htm.
2 http://assets.aarp.org/www.aarp.org_/articles/bulletin/money/financialquestionnaire.

pdf.
3 http://www.oag.state.md.us/Forms/checklist.pdf.
4 http://www.letsmakeaplan.org/cfp-pros-their-expertise/cfp-experts-corner/article/lets-

make-a-plan-blogs/things-to-think-about-when-choosing-a-financial-adviser; http://
www.letsmakeaplan.org/working-with-a-financial-planner/what-to-ask.

5 We do not use the 2012 Financial Capability Survey data because this survey does not
contain any questions on whether the respondents verified the credentials or how much
they trust their financial advisers or their advice.

6 http://online.wsj.com/news/articles/SB10001424052702304679404579459831342132
534.

7 http://www.sec.gov/spotlight/investor-advisory-committee-2012/fiduciary-duty-
recommendation.pdf.

8 Bonding costs are incurred by the agent (financial adviser) to signal to the principal of
his fiduciary responsibilities through certifications and other binding constraints.
Residual loss will include losses suffered by the principal (consumer) because of
agent’s (financial adviser’s) action that may not maximize the consumer’s wealth.

323B. Balasubramnian et al. / Financial Services Review 23 (2014) 305–324



9 Appendix A provides details about various certification and maintenance requirements
for various professional designations.

10 http://www.finra.org/investors/toolscalculators/brokercheck/.
11 http://www.sec.gov/rules/final/ia-2256.htm.
12 http://www.adviserinfo.sec.gov/IAPD/Content/Search/iapd_Search.aspx.
13 http://www.finra.org/Investors/ProtectYourself/BeforeYouInvest/p120759.
14 http://www.sec.gov/foia/docs/invafoia.htm.
15 http://www.nasaa.org/2709/how-to-check-out-your-broker-or-investment-adviser/.
16 Please see the following link for the complete questionnaire: http://www.usfinancial

capability.org/downloads/NFCS_2009_Natl_Qre_Eng.pdf.

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