The Relationship Between Subjective Cognitive Decline, Financial Interference, and Excess Spending in Older Adults with and Without Early Memory Loss

Abstract

Background/Objective: This study examined whether a brief measure combining subjective cognitive concerns and financial interference, termed Subjective Cognitive Decline-Financial (SCD-F), is associated with excess spending behavior in older adults. Methods: Community-dwelling older adults, some with early memory loss and some with no cognitive complaints (N = 150, M age = 72.6), provided 12 months of checking account statements and participated in interviews to clarify aspects of their personal financial behaviors. SCD-F was defined by asking if memory decline was interfering with financial decision-making or transactions. A 3-point SCD-F measure was created. Excess spending was determined by checking whether account expenditures exceeded all sources of income. Nonparametric tests (Kruskal–Wallis and Mann–Whitney U) and multiple regression models assessed group differences and predictors. Results: Group differences in excess spending were pronounced (H(2) = 15.75, p < 0.001). Those in the high SCD-F group had a significantly greater likelihood of excess spending (Z = −4.11; r = 0.43) and higher excess spending percentages (Z = −4.11; r = 0.43) compared to those with no memory loss. Regression analyses indicated that SCD-F was the strongest predictor of excessive spending (β = 0.40, t = 5.43, p < 0.001), even after controlling for age, gender, race, and education (R² = 0.235, F(5,144) = 8.86, p < 0.001). Conclusions: A brief self-report measure, SCD-F, effectively identifies older adults at risk of financial mismanagement, even absent formal cognitive impairment. Monitoring subjective cognitive concerns together with financial interference could enable early intervention. This brief measure may be useful in clinical settings as a screening tool, and in large national surveys.

1. Introduction

A decline in financial management skills and loss of wealth has recently been linked to time before symptoms of Alzheimer’s disease emerge [1]. Angrisani and Lee [2] found that the earliest signs of memory loss were related to an 18% decline in overall wealth across a 2-year period. The decline in the ability to manage finances is a risk factor for financial exploitation among older adults [3]. Importantly, overspending by older adults experiencing cognitive decline may also carry significant consequences for their families, who may later bear the financial responsibility for long-term medical care, assisted living, or memory care facilities. Hsu and Willis [4] found that a substantial number of older adults with cognitive decline remain the primary financial manager in their family. These studies focus on wealth overall and credit, but do not investigate the personal finances of individuals with cognitive decline.

In this study we examine personal finances in two ways (1) we examine 12 months of checking account statements for which the participant is the primary person responsible for the account, and (2) we interview the participants to assess all sources of regular (e.g., Social Security, Pension), retirement draw downs and investment income as well as review expenses for which there are questions. The participants for this study are made up of older adults with and without early memory loss. No participant met the criteria or had a diagnosis of dementia. Many reported early memory loss, even in the face of cognitive test results that showed the individual had no deficits. The main focus for the analysis was whether a new Subjective Cognitive Decline-Financial Interference (SCD-F) measure would be useful in predicting objective excess spending. It is thus imperative to create and test new tools for when cognitive decline interferes with financial management. Using the Center for Disease Control model for a brief assessment of Subjective Cognitive Decline (SCD), we added a question about cognitive decline interfering with financial decision-making and transactions using an item from a validated decision-making scale [5].

Originally developed to maximize autonomy for older persons with a neurocognitive impairment by becoming more attuned to the person’s wishes, values, and preferences, Mast [6] expanded this approach in the context of assessment approaches and tools [7]. Building on Mast’s work, Lichtenberg and colleagues [8] created a person-centered financial decision-making tool in which the older adult conveys both the actual decision and understanding and appreciation of the decision and the context within which their financial decision-making occurs. Person-centeredness conveys a deep respect for an older person’s autonomy and his or her beliefs. Regarding assessments, this means that an older adult’s decision and decision-making process are seen as valuable information, and the goal is always to enhance autonomy and to aid in compensatory strategies when needed.

Many gerontologists prefer objective assessments or tools and are wary of self-reports by older adults because they may not be accurate [9]. Subjective Cognitive Decline, a person’s belief in their own worsening cognition, occurs in the context of having no abnormal cognitive test results [10]. Subjective Cognitive Decline is considered a risk factor for Alzheimer’s disease and is linked to reduced functioning across several domains well before any objective symptoms of cognitive change occur [10,11,12]. Self-Rated Health, a simple 5-point measure of how an individual views their health, has been shown to be a powerful predictor of mortality: those who rate themselves in poorer health survive the least length of time [13]. Many individuals with SCD report difficulty with instrumental activities of daily living (IADLs; [14,15]). These include managing medications, transportation, meal preparation, and notably, financial management [14,16]. Among IADLs, financial functioning is especially sensitive to early cognitive changes. A person-centered brief assessment for financial interference due to a decline in cognition is currently lacking. The present study aims to use a person-centered approach to develop a measure of financial interference that can be used as a screening tool or in large national surveys.

While SCD typically captures general concerns about memory or thinking, our study focused on a more functionally meaningful indicator: Subjective Cognitive Decline with Financial Interference (SCD-F). This construct reflects not just perceived cognitive difficulty, but a participant’s belief that these cognitive concerns are interfering specifically with their financial decision-making and transactions. This distinction provides a practical lens for identifying individuals who may be at increased financial risk even in the absence of formal cognitive impairment. Two important areas of measurement in Gerontology have drawn increased focus during the past decade: Subjective Cognitive Decline and financial capacity. This study provided a closer examination of measuring SCD and personal finances.

There is ample evidence that cognitive decline is linked to a decline in the ability to manage personal finances. Mazzonna and Peracchi [17] analyzed 9 waves of the Health and Retirement Survey (1998–2014) and reported that cognitive decline in older adults was linked to a greater likelihood of experiencing financial loss. These losses were primarily connected to investment-related decision-making and, to a lesser extent, reductions in savings, with the impact being most pronounced among those holding considerable assets. Additional analysis using the Health and Retirement Study (HRS) has further emphasized the risk of wealth loss during early cognitive impairment. For instance, Hsu and Willis [4] reported that a decline in financial management abilities, such as difficulty with bill paying, was strongly associated with an older person’s cognitive skills. Similarly, Angrisani and Lee [2] investigated the relationship between cognitive decline and private wealth loss using the HRS. They found that significant memory decline over a 4-year period was associated with an average wealth loss of more than $30,000 compared with those who were non-impaired. More recently, using Medicare claims data spanning a 19-year period, Nicholas and colleagues [1] revealed that Alzheimer’s disease diagnoses were followed by notable increases in subprime credit scores and missed bill payments. Collectively, these findings illustrate the association between significant memory loss and wealth loss, as well as increased risk for changes in subprime credit scores.

In 2007, the CDC launched the Healthy Brain Initiative and by 2009 had developed an optional module on subjective cognitive decline for each State’s Behavioral Risk Factor Surveillance Survey [18,19]. The SCD module asked one broad question about cognitive decline in the past year and one question about whether that decline had interfered with everyday activities. One study demonstrated that the association between SCD and various health outcomes was evident in a study of over 220,000 respondents [20]. Ficker and colleagues [21] found that while 30% of their sample responded yes to cognitive decline, only 7% further stated that the decline affected any of their daily activities. Respondents who affirmed SCD had significantly more chronic health conditions and a higher risk of developing dementia, yet less than half had discussed their cognitive concerns with a healthcare professional. The importance of further studying individuals with SCD is emphasized by recent neuroimaging studies [22,23]. One study found that there were no differences in neuropsychological test results between those with SCD and those without complaints, but more white matter hyperintensities were observed in the SCD group than in the no-complaints group [23]. Finally, Viviano and Damoiseaux [24] and Parfenov and colleagues [25] have provided a review of SCD and noted that it is a risk factor for developing Alzheimer’s disease.

Financial vulnerability in older adulthood has been increasingly recognized as a complex, multidimensional construct influenced by both neuropsychological and behavioral economic factors. From a neuropsychological perspective, declines in executive functioning; particularly in planning, inhibitory control, working memory, and cognitive flexibility can impair one’s ability to budget, anticipate future consequences, and resist impulsive or emotionally driven financial decisions. Simultaneously, behavioral economics highlights how aging-related changes in decision-making processes, such as increased susceptibility to present bias, diminished risk sensitivity, and reliance on heuristics, may further compromise financial well-being. Together, these perspectives suggest that cognitive aging not only affects capacity but also alters the behavioral patterns through which individuals interact with financial systems. This integrated framework helps explain why some older adults, even in the absence of overt cognitive impairment, may be at greater risk for overspending or other financially vulnerable behaviors.

Lichtenberg and colleagues [8] reported a link between SCD and excess spending in a sample of 150 older adults who shared 12 months of their checking account statements with the authors and underwent a comprehensive interview about their accounts. Excess spending was predicted by a person-centered financial decision-making scale. Embedded in that scale was a single item about whether cognitive decline impacted their financial transactions and financial management. This study examined whether a grouping variable representing Subjective Cognitive Decline with a financial interference (SCD-F) was sensitive to differences in the management of personal finances and whether SCD-F was related to excess spending.

The following Hypotheses were made:

Hypothesis 1. 

Individuals in the highest SCD-F group (e.g., those with both subjective cognitive concerns and financial interference) will be significantly more likely to engage in excess spending compared to individuals in the other SCD-F groups (i.e., no memory impairment or memory concerns only).

Hypothesis 2. 

Individuals in the highest SCD-F group will demonstrate a significantly higher percentage of excess spending, calculated as the percentage by which spending exceeded income, compared to individuals in the other SCD-F groups.

Hypothesis 3. 

SCD-F will be a significant predictor of excess spending even after demographic measures are accounted for in a multivariate analysis.

2. Methods

Participants and Procedures

Data from the current study came from the Wealth Accumulation and Losses in Later life Early Cognitive Transitions (WALLET). The WALLET study recently introduced a novel approach to measuring spending using actual checking account statements of older adults [8]. By analyzing 12 months of participants’ checking account statements followed by in-depth interviews, the WALLET study provided a more nuanced understanding of the association between memory, financial decision-making, and financial behaviors, particularly in relation to excess spending. The WALLET study collected detailed financial information from participants’ primary checking account statements (n = 150). These were submitted either as hard copies by mail or in person, or electronically via email, and were de-identified and assigned a random ID number. The study participants were from the metro Detroit area (ages 59 to 96; 80% female). A total of 150 interviews were conducted; the interviews administered several standardized measures and collected detailed financial information from participants’ primary checking account statements. Participants in this study were categorized as normal cognition (n = 67), Perceived Cognitive Impairment (n = 63), and Mild Cognitive Impairment (n = 20). Information on participant sociodemographics, health, and disability status, memory functioning, financial decision-making, and financial literacy was also collected during interviews by administering several standardized measures. Compensation was provided for their time and any study-related banking or mailing costs. The Wayne State University Institutional Review Board approved the study protocol.

3. Measures

3.1. Subjective Cognitive Decline—Financial (SCD-F)

Subjective Cognitive Decline is a derived categorical grouping variable was created to capture subjective cognitive decline with financial relevance, based on two items from the study dataset. First, we combined participants with MCI or PCI into one group and compared them to a no memory loss group. Participants with MCI were obtained through the Michigan Alzheimer’s Disease Research Center, which uses a consensus diagnosis conference process and nationally agreed on procedures and definitions for diagnosing MCI. A PCI measure was established by the CDC to investigate population-based issues and coordinate with each state’s Behavioral Risk Factor Surveillance Survey [20,26]. Participants were asked, “Are your memory, thinking skills, or ability to reason worse than a year ago”? If the answer was yes, but there was no cognitive work-up or no positive findings on a cognitive work-up, then the participant was classified as having PCI. The no memory loss group included those who denied any problems with memory and had no neurocognitive diagnoses. Notably, participants with no memory loss had distinctively higher RAVLT scores compared to the PCI (−7.7) and MCI (−9.3) groups. The average scores for the latter two groups were not statistically distinct (p = 0.530). Therefore, we created a dichotomous cognitive status (0 = No cognitive decline, 1 = Yes, cognitive decline).

Second, participants were asked whether these cognitive difficulties interfered with their financial decision-making or transactions (1 = yes, 0 = no). Using these two items, a four-level ordinal variable was constructed (0 = no memory impairment and no financial interference, 1 = memory impairment only, 2 = memory impairment with interference in financial decision-making, 3 = No memory impairment but financial interference). Of note, no participants who denied cognitive difficulties (i.e., the No Memory Loss group) endorsed financial interference. Therefore, the combination of “no cognitive impairment + financial interference” (SCD-F Group 3) did not appear in the dataset and was not included in the analysis. This SCD-F variable was used as a grouping factor to examine differences in excess spending behavior across levels of cognitive and financial vulnerability.

3.2. Excess Spending (Binary and %Variables)

Excess spending was a primary outcome in the present study and was operationalized in two ways: (1) Excess Spending (%)—a continuous variable representing the percentage of spending beyond annual income; and (2) Excess Spending (Binary) a dichotomous variable (0 = no excess spending, 1 = excess spending). Excess spending was defined as annual expenditures exceeding reported income. Income sources included Social Security, pensions, tax refunds, employment earnings, and any planned distributions from retirement or investment accounts. These income estimates were confirmed via participant interviews to reflect the amount participants intended or expected to spend over the 12-month period. Although some participants may have had additional assets or savings, this approach focused on whether their actual spending exceeded their expected income, as defined and confirmed by the participants themselves.

To calculate excess spending, the total annual expenditures (drawn from checking account records) were subtracted from the total annual income. Negative values, indicating spending beyond income, were flagged as cases of excess spending (Excess Spending (Binary)). For participants meeting this criterion, we derived the percentage of excess spending by dividing the overspent amount by the total income (Excess Spending (%)). For example, spending $10,000 beyond an income of $100,000 would result in a 10% excess spending rate.

Given the non-normal and bounded nature of the excess spending (%) variable, we used nonparametric tests (Kruskal–Wallis and Mann–Whitney U) to examine group differences (Hypotheses 1 and 2). These analyses are robust to violations of normality and include effect size estimates (r) to facilitate interpretation. For Hypothesis 3, we retained a multiple linear regression model to evaluate the unique predictive contribution of SCD-F after adjusting for demographics and income. Although OLS regression assumes normally distributed residuals, we selected this approach due to its interpretability, widespread use in applied settings, and alignment with prior financial vulnerability research. This dual analytic strategy allowed us to balance statistical rigor with translational relevance.

3.3. Rey Auditory Verbal Learning Test (RAVLT)

The Rey Auditory Verbal Learning Test [27] is a widely used measure of episodic verbal learning and memory. During the task, an examiner reads a list of 15 unrelated nouns aloud over five consecutive learning trials, with the participant asked to recall as many words as possible after each trial. Several learning and memory indices can be derived from the RAVLT. For the purposes of this study, we utilized the total number of words recalled across Trials 1 through 5 as an index of verbal learning capacity. The RAVLT has demonstrated strong psychometric properties, with extensive evidence supporting its reliability and validity [28].

3.4. Demographic Characteristics

Demographic factors are age, based on the birthdate provided by the participant, self-reported gender, race (e.g., White, Black, Mixed Race, etc.), and education, based on the highest level of education completed.

4. Results

Descriptive statistics for all study variables by SCD-F group are presented in

Table 1. Descriptive Statistic by SCD-F Groups.

Variable	No Impairment (n = 67)	Memory Only (n = 58)	Memory + Financial (n = 25)	F(2, 147)	χ2(2)	p
Excess Spending (Binary)	41.8%	51.7%	88.0%
Excess Spending (%)	10.76 (23.92)	16.89 (23.95)	25.12 (23.89)	3.43		0.035 *
Age	72.04 (7.53)	73.98 (8.55)	72.52 (6.79)	0.98		0.377
Gender (% Female)	80.6%	77.6%	88.0%		1.22	0.545
Race (% African American)	68.7%	65.5%	52.0%		2.24	0.326
Education	15.70 (2.22)	15.57 (2.41)	15.40 (2.35)	0.16		0.850
RAVLT Total	49.44 (9.58)	41.56 (8.57)	45.05 (10.19)	13.46		0.001 **

p < 0.01 **, p < 0.05 *.

. Excess spending rates varied substantially across groups, with the highest percentage occurring in the group reporting both cognitive concerns and financial interference (88.0%), compared to 51.7% in the memory-only group and 41.8% in the no-impairment group (see Table 1). In addition to group differences in excess spending, objective memory performance on the RAVLT also varied significantly across SCD-F groups, F(2, 147) = 13.46, p = 0.001. Participants in the no-impairment group recalled the most words (M = 49.44), followed by group 2 (M = 45.05), and the memory-only group (M = 41.56). This pattern suggests that even subjective cognitive complaints in the absence of reported financial interference may be associated with early objective memory decline (see Table 1). It is important to note that RAVLT scores were not used to classify SCD-F groups. Grouping was based solely on clinical diagnosis (MCI), self-reported cognitive concerns (PCI), and reported financial interference. The inclusion of RAVLT performance was intended to provide construct validation, that is, to examine whether self-reported concerns corresponded with differences in objective memory performance.

Bivariate correlations among key study variables are presented in

Table 2. Correlations between demographics, Excess Spending (%), and SCD-F.

	Age	Gender	Education	Race	Excess Spending (%)	Income	SCD-F	RAVLT Total
Age
Gender	0.01
Education	0.02	0.09
Race	−0.04	−0.34 **	−0.19 *
Excess Spending (%)	−0.08	−0.08	0.13	−0.05
Income	0.09	0.26 **	0.49 **	−0.30 **	−0.02
SCD-F	0.06	−0.04	−0.05	−0.11	0.21 **	−0.00
RAVLT Total	−0.10	−0.29 **	0.12	0.05	0.01	−0.02	−0.37 **

p < 0.01 **, p < 0.05 *.

. The Subjective Cognitive Decline–Financial Interference (SCD–F) index was significantly positively correlated with excess spending (%) (r = 0.210, p = 0.010), and negatively correlated with RAVLT total recall (r = −0.370, p < 0.001), providing initial support for its construct validity. Education was also positively associated with excess spending (%) (r = 0.131, p = 0.109), although this did not reach statistical significance. Income was significantly associated with education (r = 0.488, p < 0.001) and gender (r = 0.263, p = 0.001), but was not significantly correlated with SCD–F (r = −0.003, p = 0.967) or excess spending (r = −0.021, p = 0.796). These results indicate that SCD–F is moderately associated with both self-reported cognitive concerns and objective memory performance, and is uniquely related to excess spending behavior.

To test Hypothesis 1, which predicted that individuals with both subjective cognitive concerns and financial interference (SCD-F Group 2) would experience greater excess spending than individuals with no cognitive concerns (SCD-F Groups 0 and 1), a Kruskal–Wallis H test was conducted (see

Table 3. Nonparametric Tests of Excess Spending (Binary) by SCD-F Group.

Comparison	Z/H	p	r
Overall group difference	15.75	<0.001	--
No Impairment vs. Memory + Financial	−4.11	<0.001	0.43
No Impairment vs. Memory	−1.61	0.11	0.14
Memory Only vs. Memory + Financial	−2.37	0.018	0.26

Note. R values were calculated using the formula r = Z/√N. Effect sizes are interpreted as small (≈0.10), medium (≈0.30), and large (≥0.50).

). This nonparametric test was used due to the skewed and bounded nature of the excess spending variable (range: 0–130%) and the categorical structure of the SCD-F variable. The test revealed a significant overall group difference in excess spending (Binary), H(2) = 15.75, p < 0.001. While post hoc comparisons were limited and guided by a priori hypotheses, formal corrections for multiple comparisons (e.g., Bonferroni or Holm) were not applied. These decisions are acknowledged in the Discussion. Post hoc pairwise comparisons using Mann–Whitney U tests were conducted to examine specific group differences (see Table 3). Individuals in Group 2 spent significantly more than those in Group 0 (Z = −4.11, p < 0.001) and significantly more than those in Group 1 (Z = −2.37, p = 0.018). The difference between Group 0 and Group 1 was not statistically significant (Z = −1.61, p = 0.11). Effect sizes were calculated using the formula r = Z/√N, following common conventions for nonparametric tests [29]. These analyses showed a large effect for Group 2 vs. Group 0 (r = 0.43), a medium effect for Group 2 vs. Group 1 (r = 0.26), and a small, non-significant effect for Group 0 vs. Group 1 (r = 0.14). These findings support Hypothesis 1, indicating that excess spending is significantly greater among individuals with both cognitive and financial concerns. Although the Memory + Financial Interference group included 25 participants, this represents approximately 30% of the total sample with subjective cognitive concerns (PCI or MCI). Despite the modest subgroup size, this group demonstrated the highest rates and magnitude of excess spending, consistent with the study’s hypotheses.

Hypothesis 2 predicted that individuals in Group 2 would exhibit significantly greater excess spending than those in Group 0. This hypothesis was supported by the Mann–Whitney comparison between Group 2 and Group 0, which showed a statistically significant difference with a large effect size (Z = −4.11, p < 0.001, r = 0.43).

To test Hypothesis 3, a multiple linear regression analysis was conducted to examine whether SCD-F predicted excess spending (%) after accounting for demographic and financial variables. Predictors included age, gender, race, education, total annual income, and SCD-F status. The overall model was statistically significant, F(6, 143) = 2.29, p = 0.039, accounting for 8.8% of the variance in excess spending (%) (R² = 0.088; see

Table 4. Regression predicting Excess Spending (%).

						Model
	β	SE	t	B	95% CI	R2	F	p
Model						0.09	2.29	0.04
Constant	-	23.58	0.23	5.40	[−41.21, 52.01]			0.82
Age	−0.09	0.25	−1.12*	−0.28	[−0.77, 0.21]			0.27
Gender	−0.08	5.34	−0.86	−4.58	[−15.13, 5.98]			0.39
Race	−0.04	4.48	−0.49	−2.21	[−11.08, 6.65]			0.62
Education	0.19	0.97	2.07	2.01	[0.09, 3.93]			0.04 *
Income	−1.0	0.00	−1.03	−8.35 × 10−5	[0.00, 0.00]			0.31
SCD-F	0.22	2.68	2.66	7.15	[1.84, 12.45]			0.009 **

Note. Dependent variable = Excess Spending (%). p < 0.01 **, p < 0.05 *.

). SCD-F emerged as a significant predictor (β = 0.22, t = 2.66, p < 0.001) with a 95% confidence interval for B [1.840, 12.449] even after adjusting for income and other covariates. Income itself was not a significant predictor (p = 0.305), suggesting that excess spending in this context is not merely a reflection of low financial resources. Education was significant and positively associated with excess spending (β = 0.19, t = 2.07, p < 0.05), indicating that individuals with higher educational attainment may be at increased risk for overspending, independent of income. These findings support Hypothesis 3, demonstrating that SCD-F uniquely predicts excess financial behavior beyond demographic risk factors. A generalized linear model using a Gamma distribution and log link was conducted as a robustness check. This model excluded 74 participants with zero or negative excess spending values. Within the reduced sample (n = 76), years of education was the only significant predictor of annual excess spending (p < 0.001), while other predictors, including the SCD-F grouping variable, were not statistically significant but remained directionally consistent with the linear regression results. These results suggest the primary findings are not solely an artifact of the analytic approach.

5. Discussion

This study examined whether a derived grouping variable capturing subjective cognitive concerns and self-reported financial interference (SCD-F) could detect differences in excess spending among older adults. Results provided consistent support for all three hypotheses.

As predicted in Hypothesis 1, individuals in the highest SCD-F group, those endorsing both cognitive concerns and interference in financial decision-making, exhibited significantly greater excess spending than those with no impairment or with cognitive concerns alone. Mann–Whitney U tests confirmed that this group had significantly higher spending rates than both comparison groups, with effect sizes in the medium-to-large range. These findings suggest that the co-occurrence of cognitive concerns and functional financial difficulties may signal elevated financial vulnerability. Notably, even individuals who reported memory concerns without financial interference had significantly lower RAVLT scores than those with no concerns, suggesting that subjective memory complaints may reflect early cognitive decline even before functional consequences emerge. Although the Memory + Financial Interference group had slightly higher RAVLT scores than the Memory Only group, this difference was not statistically significant and likely reflects sample variability. More importantly, SCD-F classifications were not based on RAVLT scores; rather, RAVLT was included to examine whether subjective reports of cognitive and financial difficulty aligned with differences in objective memory performance. The observed pattern supports the construct validity of SCD-F as a meaningful indicator of early vulnerability.

Hypothesis 2 was also supported; excess spending was significantly greater among participants with both cognitive and financial concerns compared to those with no subjective cognitive complaints. This group showed an 88% rate of excess spending, substantially higher than the 41.8% rate in the no-impairment group. The large effect size further underscores the importance of functional interference in financial domains as a meaningful differentiator.

Supporting Hypothesis 3, SCD-F remained a significant predictor of excess spending in a multivariate regression model that included demographic and financial variables. This finding demonstrates that SCD-F captures unique variance in financial vulnerability beyond age, gender, race, education, and income. Notably, income was not a significant predictor, reinforcing that excess spending in this context is not merely a function of financial means. Education was also a significant predictor, with higher educational attainment associated with increased overspending. The overall model accounted for approximately 9% of the variance in excess spending (R² = 0.088), highlighting the incremental utility of the SCD-F construct in identifying financial risk.

The behavioral patterns contributing to excess spending in this sample were examined in a companion publication, which used the same WALLET dataset [30]. That analysis identified four key behaviors linked to financial vulnerability and overspending: (1) bank fees for insufficient funds, (2) overspending in categories such as online shopping or utilities, (3) regularly providing financial help to others, and (4) financial exploitation. These findings provide insight into potential sources of overspending observed in the current study and highlight the real-world financial consequences of emerging cognitive and financial vulnerabilities.

Taken together, these results suggest that a brief, dichotomous assessment of subjective cognitive concerns and financial interference can meaningfully identify older adults at greater risk for excess spending behavior. The findings align with prior work linking cognitive impairment to increased financial risk but extend this research by emphasizing the importance of early subjective and functional indicators, even in the absence of formal diagnoses.

The SCD-F grouping variable may offer clinicians and financial advisors a practical tool to detect early signs of financial vulnerability. Because SCD-F relies on self-report, it may be especially useful in community or non-clinical settings where neuropsychological testing is not readily available. Importantly, the results also reinforce the clinical value of asking about financial functioning in tandem with cognitive concerns, rather than in isolation. These findings have two important practical implications. First, the SCD-F construct may serve as a brief and scalable screening tool in clinical settings to identify individuals at heightened risk for financial vulnerability, particularly those not yet diagnosed with cognitive impairment. Second, the core components of SCD-F could be incorporated into national surveys to explore the intersection of financial interference, cognitive health, and wealth-related outcomes at a population level. Broader implementation of these measures could inform early intervention strategies and public health policy.

Nonetheless, several limitations warrant consideration. While we controlled for demographic characteristics and income, other known contributors to financial behavior, such as financial literacy, mental health symptoms (e.g., depression or anxiety), social support, and accumulated wealth, were not included in the analysis. Variables such as financial literacy, wealth, social support, and mental health status (e.g., depression, anxiety) are known to influence financial behaviors in aging and may confound or mediate the observed relationships. For example, depressive symptoms may exacerbate financial disengagement or impulsive spending, and individuals with low financial literacy may experience excess spending unrelated to cognitive changes. Additionally, access to wealth and informal support networks could buffer financial interference even in the presence of subjective cognitive decline. Future research should incorporate these covariates to further validate and refine SCD-F. In addition, while the excess spending variable used in this study was calculated using objective checking account data, it may not reflect the total financial picture for some individuals. For example, participants may have accessed funds from savings accounts, credit cards, or other financial resources not reflected in the observed account. Although annual income was self-verified and included all expected sources, some degree of misclassification may remain. Future research should incorporate data from multiple financial sources and examine the temporal pattern of overspending (e.g., brief overdrafts vs. sustained deficits) to improve accuracy and real-world applicability.

Future research should examine the longitudinal trajectory of individuals with high SCD-F scores to determine whether they are at increased risk for financial exploitation, cognitive decline, or both. While this study’s cross-sectional design limits our ability to determine causality, the significant associations between SCD-F and excess spending support its value as a functionally meaningful construct. It remains unclear whether perceived cognitive decline contributes to excess spending or whether financial strain exacerbates perceived cognitive difficulties. Nevertheless, the link between self-reported interference and objective financial outcomes highlights the utility of SCD-F in identifying risk. Future longitudinal studies are needed to better understand the directionality of these relationships.

Additional studies should also validate the SCD-F classification in more diverse populations and assess whether targeted interventions can mitigate excess spending in at-risk groups. Our sample was predominantly female (80%), which aligns with trends in participant recruitment for aging-related studies. This may reflect gender differences in willingness to engage in research involving health and financial disclosure. Additionally, participants were primarily recruited from two community registries, the Healthier Black Elders Center and the Michigan Alzheimer’s Disease Research Center, both of which include more female than male volunteers. This gender imbalance may limit generalizability and should be addressed in future studies.

The excess spending variable was positively skewed and bounded, which presents limitations for OLS regression. We retained this model for Hypothesis 3 to enhance interpretability; however, we conducted a supplemental generalized linear model using a Gamma distribution and log link to account for the outcome variable’s non-normal distribution. Despite a substantially reduced sample, the results from this model generally aligned with the primary regression findings, supporting the robustness of our conclusions. Nonetheless, future studies with larger samples and more complete financial data may benefit from using alternative analytic strategies (e.g., quantile regression, two-part models) to confirm and extend these findings.

Additionally, while our post hoc group comparisons were hypothesis-driven and limited in number, they were not adjusted for multiple comparisons. We encourage cautious interpretation of these exploratory contrasts and recommend that future studies consider correction procedures when conducting broader post hoc analyses.

Despite these limitations, this study contributes to a growing literature on subjective cognitive decline and financial vulnerability by introducing a novel index (SCD–F) that combines cognitive complaints and perceived financial interference. Unlike traditional financial capacity assessments, which are time-intensive and typically administered in clinical settings, the SCD–F is brief, scalable, and easily deployable in survey or primary care contexts. This makes it a promising tool for early identification of older adults at risk for financial mismanagement related to cognitive changes.

6. Conclusions

This study provides evidence that a brief, person-centered measure of Subjective Cognitive Decline with Financial Interference (SCD-F) can meaningfully identify older adults at increased risk for excess spending, even in the absence of formal cognitive impairment. By combining self-reported cognitive concerns with perceived financial interference, SCD-F captured unique variance in financial vulnerability beyond demographic and income factors. These findings highlight the importance of monitoring both cognitive and financial functioning in tandem, particularly for early detection of risk. The brevity and scalability of SCD-F make it a promising tool for use in clinical settings and large-scale surveys, offering opportunities for timely interventions that may help preserve autonomy and financial well-being in aging populations.