# Financial technologies (FinTech) for mental health: The potential of
objective financial data to better understand the relationships between
financial behavior and mental health

Johnna Blair 1,∗, Jeff Brozena 1, Mark Matthews 2, Thomas Richardson 3 and
Saeed Abdullah 1

###### Abstract

Financial stability is a key challenge for individuals with mental illnesses.
Symptomatic periods often manifest in poor financial decision-making including
compulsive spending and risky behaviors. This article explores research
opportunities and challenges in developing financial technologies (FinTech) to
support individuals with mental health. Specifically, we focus on how
objective financial data might lead to novel mental health assessment and
intervention methods. We have used data from one individual with bipolar
disorder (i.e., an N=1 case study) to illustrate feasibility of collecting and
analyzing objective financial data alongside mental health factors. While we
have not found statistically significant trends nor our findings are
generalizable beyond this case, our approach provides an insight into the
potential of using objective financial data to identify early warning signs
and thereby, enable preemptive care for individuals with serious mental
illnesses. We have also identified challenges of accessing objective financial
data. The paper outlines what data is currently available, what can be done
with it, and what factors to consider when working with financial data. We
have also explored future directions for developing interventions to support
financial wellbeing and stability. Furthermore, we have described the
technical, ethical, and equity challenges for financial data-driven
assessments and intervention methods, as well as provided a broad research
agenda to address these challenges.

##  1 Keywords:

Mental Health, Financial technologies, FinTech, Open banking, Intervention,
Impulsive spending, Privacy preserving

##  2 Introduction

Mental illness is a serious public health crisis on a global scale. It affects
more than one billion individuals (1). This results in significant economic
consequences, with an estimated total annual cost of $2.5 trillion globally
(1). This growing issue impacts many facets of daily life, including a strong
association between mental health and financial instability. Individuals with
mental health issues are more likely to live in relative poverty (2, 3).
Symptoms of some illnesses can also manifest in poor financial decision
making. More specifically, bipolar disorder (BD) appears to be linked with
greater risk of impulsive financial behaviors—The diagnostic criteria for
hypomanic and manic episodes specifically lists impulsive spending as possible
symptoms (4). Those with BD are at greater risk of problem gambling (5), and
71% report impulsive spending whilst hypomanic (6). This impulsive spending
has also been shown to be linked to negative feelings and subsequent comfort
spending (7, 8). As this illustrates, there is often a cyclical and
bidirectional relationship between financial stability and mental
health—financial instability can worsen mental health, which in turn, can
cause further financial challenges, leading to a vicious cycle (9, 10, 7, 8).

The lack of access to objective financial data has been a key challenge in
understanding the nuanced relationship between mental health and financial
behaviors. Prior studies have mostly used surveys (2) and focus group
interviews (10) to assess financial behaviors associated with serious mental
illnesses. This means that much of what we know about this relationship is
mediated through subjective methods that may be prone to bias and
retrospective recall error. For example, Richardson et al. (8) measured self-
reported compulsive spending in Bipolar Disorder, rather than objective data
on spending patterns. While this provides a useful broad overview, there
remains a knowledge gap regarding how idiosyncratic, context-driven, and
illness-specific factors impact financial decision making and stability of an
individual living with mental illness. Furthermore, the lack of granular, in-
situ assessment methods is a key barrier against developing just-in-time,
adaptive, and personalized interventions focusing on financial stability for
this population. Given the importance of financial wellbeing for mental
health, this remains a serious knowledge gap with broad practical
implications.

In recent years, there has been considerable progress toward more open and
accessible financial data. Financial institutes are increasingly adopting open
banking application programming interface (API) mandating access to accounts,
payments, and transactions. There have also been third-party tools and
platforms (e.g., Plaid (11)) that enable access to financial data across a
broad range of institutes. We argue that the granular and (near) real-time
access to individual financial data can lead to a paradigm shift in the domain
of financial wellbeing and mental health. That is, it can provide a unique
opportunity to explore the nuanced relationship between money and mental
health, uncover new financial patterns indicative of early-warning signs, and
develop preemptive interventions to support healthier financial decisions.

However, there are considerable challenges before we can achieve this vision
of financial technologies (FinTech) to effectively support mental health.
Specifically, there is a need to identify potential features and analytical
methods that can leverage financial behavioral data to identify early-warning
signs and opportune moments for intervention delivery. It will also be
essential to design effective interventions that balance the need for personal
agency and long-term financial stability. Addressing privacy concerns will be
critical given the sensitive nature of the data — a recent survey of those
with bipolar disorder found that 97.5% used smartphone apps but concerns about
privacy were common (12). Individuals with mental illnesses might need support
from others (e.g., family members) to make sustainable financial decisions. As
such, the assessment and intervention methods should focus on shared,
collaborative financial decision making.

Toward this broad research vision, we make the following contributions for
future FinTech development:

  * •

Discuss the potential for technology to serve as a useful way to map the link
between finances and mental health and identify personalized warning signs
indicating mental health issues.

  * •

Use a N=1 case study with financial data from an individual with bipolar
disorder to illustrate the opportunities and technical challenges for
collecting and using objective banking data in mental health contexts, such as
data access and data quality.

  * •

Lay the landscape for future research on the potential intervention strategies
and ethical challenges for using objective data in the financial mental health
domain.

##  3 Related Work

###  3.1 Financial Behaviors Related to Mental Health

Mental health issues can lead to problematic financial behaviors. For
instance, those with bipolar disorder have reported often avoiding finances
and finding it hard to plan especially when depressed (7). Depression has also
been linked with cognitive impairments such as in attention, working memory
and problem solving(13), all of which have the potential to impact financial
capability. In bipolar disorder, problematic financial behaviors including
impulsive spending have been linked to impulsivity, low self-esteem, and
thoughts around achievement and dependency on others (14, 15).This combination
of purchasing behaviors related to manic and depressive mood episodes can have
a significant impact on long-term personal finances. In the following section,
we discuss prior work establishing problematic financial behaviors in relation
to mental health conditions like bipolar disorder. These spending behaviors
are often interrelated and can take on a cyclical pattern(15).

####  3.1.1 Non-Purchasing Behaviors

Many mental health conditions come along with decision-making challenges,
memory difficulties, and avoidance behaviors that can have an impact on
finances (16, 13). Memory or attention challenges which are common with some
mental illnesses (e.g., depression, bipolar disorder) can make it difficult to
remember important deadlines or due dates (13). Without significant support or
reminder systems, individuals may miss these deadlines and face overdue bills
or late fees that negatively impact their finances. Different avoidance
behaviors can affect financial decisions as well. Some may find it difficult
to make a decision or feel anxious when presented with too many options (17),
so they may choose to put off making imperative financial decisions. Poor
perceived financial wellness can increase stress and anxiety about finances,
which can then lead to avoidance of financial matters and ultimately making
their financial situation worse (15). Some may avoid acting on their finances
entirely, like in cases of debt-related stress, which can also lead to overdue
bills. If this avoidance results in neglecting to review banking balances,
this can lead to negative outcomes, such as overdrawn accounts(18). All these
behaviors, though not focused directly on spending money, can make managing
finances and budgeting even more difficult.

####  3.1.2 Compulsive Spending

One of the leading issues in the context of mental health and money is
compulsive spending, which is commonly reported in bipolar disorder (10) This
can be a standalone condition or a symptom of other conditions like BD,
creating further challenges to mental wellbeing. Compulsive buying, which can
often present during manic mood episodes of BD, is an intense, irresistible
need to spend beyond what is necessary, and in many cases beyond one’s
financial means (19). This can occur despite the emotional distress it may
cause, limited funds, or a lack of actual need for the items purchased. Like
other addictive behaviors, the amount of spending or the size of purchases may
escalate over time as one needs more to feel the same level of outcome. For
many, it is the desired part of a dopamine loop and a distraction from
negative feelings (19). While these purchasing habits may have temporary and
fleeting positive outcomes for the individual, they can create increased
feelings of regret, guilt, and even suicidality in the long-term (7). Not only
can they affect personal finances, but can also become a stressor in
relationships and other parts of their life. This shame and disappointment
associated with spending may then be followed by additional spending (15).

####  3.1.3 Symptom-Specific Spending

Other BD symptoms can manifest in the types of purchases individuals choose to
make. These include impulsivity, risk-taking behavior, goal-directed activity,
and comfort spending. Whereas compulsive spending is a drive to spend money
for various reasons, _impulsive spending_ is a momentary decision to make a
purchase (20). Manic mood episodes in BD can result in high levels of
impulsivity, as well as difficulty delaying gratification (4, 20). This can
lead individuals to make quick decisions with money without considering the
consequences of those decisions (14). Increased _risk-taking behavior_ is
another common costly symptom that manifests in financial decisions (21, 6,
20). These financial decisions could be perceived as high-risk, high-reward
opportunities (e.g., investments or risky business ventures) that individuals
jump into quickly, which can negatively impact finances in the long term (21)
and increase regret and feeling of guilt (6). Some may also see an increase in
_goal-directed activities_ , where they are very focused on completing tasks
toward a specific project or multiple big ideas (4). Those with bipolar
disorder tend to have ambitious goals linked to manic symptoms (22), and
cognitions around achievement predict more spending (15). This can be
activities like starting a new business or learning a new hobby, which often
include many upfront costs to support.

On other occasions, depressed mood episodes may give way to _comfort spending_
(7). These purchases are made as an attempt to change one’s mood state or to
give themselves something to look forward to in the future (7). This may also
take the form of charity or gifts for others, rather than a purchase for
themselves. In this case, this can be seen as improving their mood or reducing
guilt about their purchases by being generous to others (15). Research with BD
found that symptoms of depression, stress, and anxiety increased impulsive
spending over time, linked with this theme of ‘comfort spending’ as an
outcome(7, 8). A model based on bipolar disorder by Richardson et al.(15)
suggests an interpersonal vicious cycle where regret and guilt from impulsive
or high-risk spending leads to worries about others and excessive generosity
as a way to cope with this, thus fueling more spending. This suggests that
these symptom-related behaviors can be connected. Additional research
involving real-time purchasing data could help uncover other relationships and
driving factors between different types of spending behaviors.

####  3.1.4 Patterns of Spending

Previous work has provided insight into the different goals and drivers behind
spending in the context of BD, less is known about the specific patterns that
may exist in these spending behaviors. For some, impulsive spending may result
in a single high-dollar purchase (6, 20). Others may experience spending
sprees or bursts of many items purchased within a narrow window of time, as is
common with bipolar disorder (4). While these temporal bursts of spending have
been explored in regards to individual psychological traits (e.g., neuroticism
(23)), however spending patterns in relation to symptoms of mental illness
have been under-explored. Our current understanding of how spending temporally
presents in relation to symptoms still lacks the depth needed to help predict
these behaviors and develop personalized interventions to provide preemptive
support.

While previous studies have resulted in valuable insights on how symptoms can
manifest in specific financial behaviors, they might not lead to the complete
picture given their reliance on self-reported perceptions of individual’s own
behaviors. In other words, self-reported data can suffer from incomplete
recall and other biases, particularly during symptomatic periods. Being able
to access objective personal financial data can help to address this issue.
That is, we can use granular and objective financial data spanning symptomatic
and non-symptomatic periods to identify idiosyncratic patterns indicative of
problematic financial behaviors. However, there are considerable challenges in
accessing objective financial data. In the following section, we discuss
strategies and highlight these challenges using a N=1 case study in accessing
financial data from an individual with bipolar disorder.

##  4 Financial Behaviors and Bipolar Disorder: N=1 Case Study

As previous research illustrates, the relationship between mental health and
money is highly complex and nuanced. In this paper, we focus on bipolar
disorder and spending behaviors as an example case to investigate this
further. Symptoms related to BD impact several facets of daily life, including
personal finances. BD is typically characterized by cycling between the highs
and lows of manic and depressive mood episodes. A depressive mood episode
often includes severe feelings of sadness, low-energy, indecisiveness, and
poor concentration (4). Conversely, manic mood episodes often feature feelings
of euphoria, increased energy, high activity, risk-taking behaviors,
impulsivity, and a decreased need for sleep (4).

These different mood episodes provide a framework to better understand the
relationship between mood and money and the spending patterns that present
during different mental health contexts more broadly. This can also inform the
ideal points for intervention to help support healthy financial choices.
However, there are considerable challenges in accessing objective financial
data. In the following section, we discuss strategies and highlight these
challenges using a N=1 case study in accessing and processing financial data
from an individual with bipolar disorder.

###  4.1 Methods

We conducted a case study involving a single individual, also a coauthor, who
experienced a relapse into a hypomanic state lasting between 2017 and 2018.
This episode was the individual’s first substantial symptomatic period since
their 2008 diagnosis of bipolar disorder type II. In the following section, we
discuss the process of collecting, pre-processing, and analyzing granular
financial data across symptomatic and non-symptomatic periods.

####  4.1.1 Label Creation

To determine symptomatic periods during the study duration, we used the
National Institute of Mental Health’s Life-Chart Method (NIMH-LCM) (24). Prior
work has validated the use of NIMH-LCM for longitudinal assessment of bipolar
disorder (25). Our coauthor collaborated with their family members to complete
a Retrospective NIMH-LCM Self-Rating form for a period spanning 2017 to 2018.
Periods of hypomania were logged as mild, moderate, or severe following the
form’s criteria. Depressive symptoms were also measured on a similar scale.

During the study period, the co-author and their family members recalled that
the hypomanic symptoms had escalated slowly with no sudden variations in mood
over time. They chose to represent these slower variations in mood by marking
their responses to the Retrospective NIMH-LCM on a monthly basis. They began
by accounting for a small number of landmark events serving as anchor points
in relation to any additional events they could recall. They supplemented
their dialogue with emails, photographs, and SMS logs to produce additional
date ranges where symptoms were present. These steps resulted in 2 periods of
mild mania lasting 5 months in total, and 5 periods of moderate mania lasting
9 months in total. They did not identify any period with depressive symptoms
during the study duration.

####  4.1.2 Extracting Data from Financial Statements

The banking institution associated with this case study does not provide a
mechanism for access to structured financial data. As such, we had to manually
recreate a structured financial dataset by programmatically extracting tabular
text content of digitized financial statements (i.e., PDF files). We collected
a total of 24 monthly statements as multipage PDF files spanning the study
duration. Each PDF file contained transaction-level detail for all open
accounts.

We first extracted the tabular text content of each statement into comma-
separated text files using Camelot (26), an open-source Python package for PDF
text extraction. We manually inspected output from Camelot to ensure accuracy.
Specifically, we compared a series of 25 transaction dates, descriptions, and
amounts against the original PDF statement, all of which were extracted
correctly by Camelot. The formatting of the original PDF files was read with a
similarly high degree of accuracy. While the text output was technically
accurate, formatting issues prevented its immediate use. Camelot preserved all
pagination and line breaks of the original statement, resulting in cases where
a single transaction could span multiple rows, a single account could span
multiple files, and multiple accounts could be contained within a single file.

Accounting for the variety of these exceptions required a non-trivial amount
of manual preprocessing in order to produce a dataset where each row contained
a single, complete transaction. We used Microsoft Excel to visually inspect
and clean all Camelot output. We then manually merged transactions spanning
multiple rows into a single row. During the data processing, we retained
account labels (i.e., checking and credit). We used these labels to separate
transactions in a single file per account per month. We then joined these
files in sequence to produce a single file per account containing transaction
dates, descriptions, and transaction amounts. We also sort the transactions by
date. Figure 1 summarizes the pre-processing steps.

Figure 1: Workflow to create structured financial data from PDF statements

We then used pandas (27) — an open source Python package — to transform
information in comma-separated files into a structured dataset. To preserve
privacy, we removed all text-based transaction descriptions. Additionally, we
normalized the transaction values between 0 and 1 per account. We hypothesized
that problematic financial behaviors will manifest in expenditure
transactions. As such, we focused on expenditure and omitted income-based
transactions from our analysis. This resulted in creating a single _privacy-
preserving_ dataset containing normalized expenditure transactions with labels
indicating originating account but containing no personally identifiable
information nor actual amount.

We performed our analysis based on this combined set using two strategies. The
first strategy focused on analyzing details on transaction volume for all
accounts, labeled by account and grouped by date. The second strategy used
detail on transaction counts to analyze by frequency of expenditure, again
labeled by account and grouped by date.

####  4.1.3 Analysis

We conducted an exploratory visual analysis of these two datasets to identify
trends in frequency and amount of expenditure. Transactions were resampled on
a daily, weekly, and monthly basis. For each of these resampled periods, we
visualized transaction frequency and volume, visually shading each figure to
indicate symptomatic severity following the NIMH-LCM labels generated earlier.
We define transaction frequency as the number of transactions occurring in a
given time period, and transaction volume as the normalized, aggregated
dollar-value of purchases made during a time period.

To account for unbalanced group sizes and the possibility of unequal
variances, we used a one-way Welch analysis of variance to determine how
expenditure varied between symptomatic phases. Each transaction was marked as
“mild”, “moderate”, or “none” to account for symptom severity using the NIMH-
LCM labels. We also performed a one-way Welch ANOVA after merging “mild” and
“moderate” periods together. For each one-way Welch ANOVA test, a Games-Howell
post-hoc test was performed to produce confidence intervals for the
differences between group means and test for statistical significance.

We expected to see occasional increases in transaction frequency,
hypothesizing that these would correspond to periods of hypomania and mania.
Following methods outlined in Tovanich et al. (23), we explored potentially
impulsive, short-term increases to spending frequency by calculating a
burstiness parameter.

$B=\frac{r-1}{r+1},$

where $r=\sigma/\tau$. Here $\tau$ is the average and $\sigma$ is the standard
deviation of interevent timing for all transactions. Here, a burstiness
parameter of 1 indicates spikes, 0 indicates randomness, and -1 indicates
stability. We calculated burstiness for the entire dataset. We also calculated
burstiness for each symptomatic phase after grouping transactions by
symptomatic intensity. We tested for significant differences in burstiness
between phases using a one-way Welch ANOVA test with a Games-Howell post-hoc
test.

###  4.2 Findings

####  4.2.1 Exploratory Visual Analysis

Our visual analysis of transaction volume and frequency during approximated
periods of manic symptoms yielded several points of interest. In Figure 2,
several noticeable spikes in expenditure from the primary transaction account
are visible during periods of moderate manic symptoms, shaded in yellow. As
Figure 3 shows, two periods of mild manic symptoms, shaded in green, are
marked by an increase to credit expenditure. A mixture of risky spending
dynamics occurs during late 2018 where a marked decrease in cash-based spend
coincides with an increase of credit spend in Figure 3).

Figure 2: Weekly expenditure from transaction account. Figure 3: Weekly
expenditure from credit account.

This case study intentionally excluded income-related transactions, assuming
that many individuals have more control over expenditure than income. Although
income-related data could provide interesting features (i.e., irregular income
as a sign of partial employment or the stressors of gig work), we chose to
focus our case study on the relationship between mood episodes and
expenditure, including the use of credit. The complicated relationship between
debt and bipolar disorder is illustrated in Figure 4, where the percentage of
credit-based transactions is shown to increase as this symptomatic episode
concludes.

Figure 4: Monthly Percentage of Purchases Made on Credit

####  4.2.2 Quantitative Analysis

There were a total of 3,373 transactions in our analysis. We grouped daily
transaction data (frequency and volume) by intensity of symptoms (“mild”,
“moderate”, “none”) and calculated the mean for each group. Our Welch ANOVA
test yielded no statistically significant differences in transaction frequency
($F_{1,362}=0.31,p=.734$) or volume ($F_{2,362}=1.93,p=.147$). With
transactions grouped by the presence or absence of any symptoms, we calculated
the mean for each group. No statistically significant differences were noted
in transaction frequency ($F_{1,558}=0.35,p=.556$) or volume
($F_{1,431}=0.19,p=.665$) between groups.

| None | Mild | Moderate
---|---|---|---
Frequency | 5.23 | 5.49 | 5.34
Volume | 0.05 | 0.05 | 0.04
Table 1: Normalized mean daily amounts for transaction frequency and volume by symptom intensity. | None | Sympomatic
---|---|---
Frequency | 5.23 | 5.39
Volume | 0.05 | 0.04
Table 2: Normalized mean daily amounts for transaction frequency and volume by
presence or absence of symptoms.

Following Tovanich et al. (23), we calculated two different burstiness
parameters: $B_{D}$ with the difference in days between each transaction as
the interval, and $B_{C}$ with the number of consecutive days without any
expenditure as the interval. In our dataset, we found $B_{D}$ to be 0.402 and
$B_{C}$ to be -0.148. Note that a burstiness parameter of 1 indicates spikes,
0 indicates randomness, and -1 indicates stability. No statistically
significant differences between symptomatic periods and non-symptomatic
periods were found using either interval calculation.

These non-significant results may be due in part to the small sample size of
this case study or to the sparse labels available to our dataset. This
analysis could be improved with additional longitudinal financial data for
comparison. If mood logs or specific health records were available,
symptomatic periods could be labeled with greater accuracy. The presence of
more detailed transaction data and metadata (i.e., timestamps, purchase
locations) could significantly improve future analyses.

| None | Symptomatic
---|---|---
Mean $B_{D}$ | 0.40 | 0.38
Mean $B_{C}$ | -0.43 | -0.53
Table 3: Mean burstiness parameters for periods with and without the presence
of symptoms.

####  4.2.3 Unsupervised Anomaly Detection

In addition to the above statistical testing, we implemented an unsupervised
isolation forest algorithm (28) using scikit-learn (29) to detect potential
anomalies in weekly spending frequency throughout the analysis period without
relying on data labels. This isolation forest algorithm requires a user-
defined “contamination parameter” — the estimated percentage of anomalous data
points in a dataset. We chose a contamination parameter of $0.05$ following a
visual inspection of values ranging from $0.01$ to $0.1$. Setting this
parameter value below $0.05$ resulted in only the most extreme fluctuations in
spending frequency being identified. Larger values resulted in the detection
of an increasing number of false positives. Taking this approach to parameter
selection was possible with existing background knowledge of the case study.

Figure 5 displays outlier periods of spending frequency detected by the
isolation forest algorithm. In this case, both high and low frequency periods
were identified by the algorithm. The majority of the higher frequency
spending identified by the algorithm occurred during self-reported periods of
moderate mania. Additionally, an anomalous decline in spending frequency was
identified following the conclusion of this episode. Our approach shows the
potential to identify relationships between financial behaviors and mental
health states. A potential early warning system could integrate these results
with other predictive approaches to form a more complete representation of
risky financial behavior.

Figure 5: Outlier periods of spending frequency as identified by an
unsupervised isolation forest algorithm.

##  5 Discussion

In this paper, our goal is to explore financial “biomarkers” indicative of
mental health issues as well as identifying opportunities and challenges in
developing financial data-driven interventions. Toward this goal, we conducted
a N=1 case study to collect, process, and analyze financial transactions from
an individual with bipolar disorder to illustrate the potential, pragmatic
considerations and factors to consider when working with objective financial
data. From this dataset, we have identified potentially risky financial
behavioral dynamics across symptomatic phases (e.g., ratio of increased credit
spending). However, we have not found any statistically significant trends,
which might be due to the small sample size (i.e., only 3,373 transactions
spanning two instances of mild mania and five instances of moderate mania in
total). Furthermore, our findings and analyses are limited to this N=1 case
study, which might not generalize to individuals with bipolar disorder or
other mental illnesses.

However, our approach illustrates the potential benefits and challenges of
accessing, working with, and collecting ground truth associated with objective
financial data. In the following sections, we will first discuss potentials
for using objective financial data to detect early warning signs and
interventions focusing on financial stability for individuals with mental
illnesses. Specifically, there are open research opportunities to develop
technological interventions that leverage real-time, objective data to reduce
risky financial decision-making for both individuals and institutions. We then
describe existing technical, legal, and ethical challenges as well as
providing a broad research agenda to address these challenges. Lastly, we will
discuss how to promote equity and prevent discrimination for FinTech focusing
on mental health support.

###  5.1 Early-Warning Prediction

Considering the complex relationship between mental health and finances,
FinTech systems can help users learn about both their financial behaviors and
their mental health status throughout time. Over the course of use, such
systems could help predict when different mood states are about to happen and
serve as an early warning for upcoming spending sprees. Conversely, the
financial behaviors detected by a FinTech system could be used to predict the
onset of future mood episodes. This would not only help users manage their
mental health and financial decisions, but also explain the connections
between the two (30).

It is reasonable to believe that a system capable of early detection of
symptomatic behaviors could have provided actionable feedback to our coauthor
at the time of their episode, potentially affording improved self-awareness of
risky behaviors and of illness states. This is especially salient considering
the length of this episode and subtlety of its symptoms. Although the harms of
this episode may be clear in hindsight, they remained opaque to the individual
and several mental health professionals at the time. Objective financial data
may have provided a strong signal of relapse.

However, we faced a number of challenges to present this case study in its
present form. We believe many of these challenges represent requirements to
operationalize objective financial data analysis in research settings. Central
to these challenges is the theme of timely access to objective, structured
financial data in machine-readable and human-readable formats. Considering our
retrospective retrieval, our ability to obtain financial statements as PDF
depended on the technical capacities and policies of the banking institution.
If a longer period of time had elapsed beyond this symptomatic episode, the
data retrieval steps might require processing paper-based records (i.e.,
digital data retrieval might be available only for a certain time period in
some financial institutes). This would seriously hinder data collection at
scale in retrospective studies.

Financial data collection methods will vary depending on the data source. At
the scale of an N=1 case study, we could afford to manually process PDF
statements. Larger studies may require utilizing more scalable data sources
such as structured, delimited text or API-based access. Even at our scale, the
use of PDF statements constrained our analysis steps. Many printed or
digitized financial statements only list transactions by date. A finer-grained
temporal analysis could yield valuable insights into behavioral changes during
symptomatic states (e.g., how spending patterns change during disruptions to
sleep). Additionally, our calculation of burstiness parameters was limited to
a daily time-grain. More specific timestamps could yield useful, actionable
insights into more specific patterns of spiky, rapid spending during
symptomatic periods. Mood monitoring apps have also been found to relate to
more formal measures of mood in bipolar disorder, suggesting that they might
have utility in measuring mood (31). Linking such mobile based measures with
live real-time measures of finances could therefore prove useful in the
future.

Although reliance on retrospective patient self-report remains common in the
field of psychiatry, our analysis was limited by a lack of specific labeling.
Access to health records, granular self-reported mood logs, or sleep data
would each individually improve our capacity to assess these financial data in
more specific terms. In the following section, we will describe future
research directions to identify early-warning signs by leveraging real-time,
objective financial data.

####  5.1.1 Research Directions

Research in this domain remains limited by the scarcity of structured
financial data to individuals and especially to trusted third-parties. The
possibility of performing this research at scale depends on a standardized,
permissioned technical infrastructure for electronic access by individuals and
authorized third-parties. Although the technical infrastructure currently
exists to support these aims, regulations are under development which may
clarify the role of authorized third-party access to consumer financial data.

##### Better access to financial data

As evidenced in our case study, objective financial data varies widely in
quality and availability based on the data storage and access management
practices of the individual’s banking institution. These variations pose a
number of technical challenges, especially when considering how best to
operationalize and scale data collection. Financial data can be made available
in different formats including as paper or PDF documents, as structured text
files, or using software-based approaches known as application programming
interfaces, or APIs. We assess each of these types of source material by the
quality and ease of access they each afford as shown in Table 4.

Document-based source material (printed or PDF financial statements) imply
substantial preprocessing steps to render printed transaction-level detail as
structured, digitized data. Transactions are likely logged on a daily basis.
Metadata such as location or transaction category will likely be absent.
Conditional inclusion or exclusion of transactions will likely only be
possible after completion of preprocessing steps. Retrieval will likely
involve manual processes and make real-time analysis impossible.

Files exported as delimited plain-text (e.g., from a web-based banking portal)
are structured, but may require preprocessing steps to account for variations
in institutional output. These may include more granular transaction-level
data which is simpler to retrieve on a recurring basis. It may be possible to
conditionally include or exclude specific transactions without revealing them
to the researcher. Real-time analysis may be possible, but likely depends
heavily on access protocols of the institution. Additional third-party
metadata may be merged with these types of files.

Overall, API access (e.g., Plaid (11)) supported by financial institutes
provides real-time transaction-level financial data and allow for simple,
permissioned, and revocable third-party access.

Source |  | Data
---
granularity
(e.g.,
transaction
level,
day level)
| Processing
---
difficulty
(e.g., manual
to automatic)
| Metadata
---
available?
Data structure |  | Possibility
---
of real-time
access?
| Retrieval
---
Difficulty
| PDF
---
Statement
| Least
---
granular,
daily
time-grain
| Extremely
---
difficult to
scale
| No. Would
---
need
to manually
incorporate
third-party
data after
preprocessing
| Unstructured,
---
requires
preprocessing
| Non-realtime.
---
Impossible
to obtain
in realtime
| Manual
---
process,
difficult.
| Retrospective
---
structured
data
| Depends
---
on data
source
| Possible to
---
scale,
especially
retrospective
studies
| Possible,
---
but likely
requires
third-party
data
| Possible,
---
but likely
requires
preprocessing
steps
| Likely not
---
realtime,
but may be
possible
| Depends on
---
source &
analysis
needs
| API
---
integration
| Per
---
transaction
| Immediately
---
scalable
| Possible,
---
given
institutional
capacity
| Structured,
---
machine-
and
human-
readable
data
| Possible,
---
given
institutional
support
Simple
Table 4: Factors to consider when collecting objective financial data.

##### Integrating behavioral and contextual datastreams with financial data

Future research should also explore how to integrate multimodal behavioral and
contextual datastreams with financial data. This can lead to granular,
context-specific understanding of how mental health symptoms can manifest in
idiosyncratic financial behaviors. For example, mood data collected using
ecological momentary assessments (EMA) can be combined with financial
transaction data. This can help to establish the relationship between
emotional states and financial decision-making.

Financial data can also be combined with passively sensed data. Prior work has
used behavioral and contextual data including location information to assess
stability in bipolar disorder (32, 33). Combining such data streams with
financial transaction information can provide valuable insights to
researchers, clinicians, and individuals seeking tools for self-management and
reflection. Furthermore, the availability of wearable devices and their
ability to collect granular health data can complement financial data to
predict early warning signs. For example, by leveraging data from wearable
sleep-trackers, we can identify how circadian and sleep related factors might
impact financial decision making and risk taking.

###  5.2 Opportunities for Financial Data-driven Interventions

This use of objective financial data opens up new opportunities for financial
intervention systems. In this section, we suggest future directions on
different ways to intervene on point-of-purchase decisions and incorporate
cognitive financial behavioral therapy activities to help improve overall
financial wellbeing.

####  5.2.1 Point of Purchase Interventions

Online shopping has become streamlined and mostly frictionless. With auto-
saved credit card information or one-step payment functions, as well as
contactless purchasing, consumers spend less time on their purchasing
decisions. As a result, steps in the purchasing process that would otherwise
be an opportunity for the buyer to reconsider their decisions are often
removed(34). While this lack of friction broadly impacts all users, this is
particularly problematic for those prone to impulsive spending. Adding
friction in online spending process can lead to harm reduction—both at an
individual level and for banking institutions themselves. For instance, some
UK banks have already added “positive friction” to help customers prevent
gambling (35). Future interventions could help address impulsive spending and
promote more mindful purchasing by adding increasing levels of friction with
flexible settings. In other words, we can introduce friction into the process
of purchasing based on the user’s changing situational needs or depending on
their current mood state.

To start, friction can be reintroduced into online purchasing, such as forcing
the reentry of payment cards or adding two-factor authentication. While this
ultimately does not prohibit the user from purchasing, it adds additional time
to the activity which may slow down their decision process, make the user
reconsider, and potentially choose not to follow through. In-situ reevaluation
prompts could be provided to the user to help with informed decision making.
Systems could ask the user questions such as “are you sure you want to buy X
for $Y?”to provide an opportunity to consider the potential impact of making
this purchase. Constraints could also be added that items must sit in a
virtual shopping cart for a certain amount of time (e.g., 24 or 48 hours)
before the user can initiate payment—especially for very large purchases.
Lastly, users could proactively set the system to restrict certain purchases
when a mood episode is detected to prevent spending sprees. This could be
tailored to the user by restricting purchases containing chosen keywords, by a
specified cost threshold, or by time of day (e.g., limiting late-night impulse
purchases). These restrictions could then be lifted under specific and
predetermined conditions, such as detecting a mood episode has passed or
having the user go through an attention or cognition-based assessment.

####  5.2.2 Psychological Financial Therapies

In collaboration with clinicians, we can also explore how to leverage existing
psychological therapies such as Cognitive Behavioral Therapy (CBT) and
Cognitive-Behavioral Financial Therapy (CBFT)(36) to target impulsive spending
behaviors and deliver them digitally. CBT has been delivered online and has
been shown to be effective for depression and anxiety (37, 38). CBT often
involves individuals identifying and challenging unhelpful thinking patterns,
as well as facing fears they are avoiding. With CBFT, this could be linked to
financial difficulties, such as understanding the thinking patterns leading to
impulse spending and trying to face finance-related fears. When incorporated
with FinTech systems, users may be able to reflect on their behavior over
time, recognize important patterns, and develop coping skills to manage their
spending. Users could be prompted to evaluate and reflect on their behaviors
during a set window and consider the context in which they made those
financial decisions. Mindfulness has also been shown to predict later
compulsive spending in bipolar disorder (15), so incorporating this and
helping individuals be more mindfully aware of the thoughts and feelings which
might come before impulsive spending could be useful.

Future research should focus on developing just-in-time interventions to
support in-situ and real-time decision making. For instance, if the system
senses an increase in spending, the user could be pushed a suggestion to move
funds to a savings account or make them temporarily inaccessible to them to
preemptively reduce potential harm. Users could also be provided alternative
activities, such as calling a friend or going for a walk, to prevent
problematic behaviors in situations where spending is motivated by boredom or
a want to elevate their current mood. In this case, these alternative
activities could provide an outlet for their current needs and motivations
behind spending that does not involve spending money. In a recent study,
Richardson et al. (39) developed ‘Space from Money Worries’ — an online CBT-
based intervention designed to tackle the link between financial difficulties
and mental health problems, including impulsive spending. Their data showed
reduced symptoms of depression and anxiety, as well as lowered financial
distress (39). Linking such interventions with objective financial data could
help determine whether they have a real-world impact on spending patterns and
other financial behaviors.

###  5.3 Challenges for FinTech to Support Mental Health

While objective financial data opens up these opportunities to support
financial wellbeing and intervene on problematic behaviors, there are a number
of challenges to address through future research, including user agency,
privacy, and equity concerns.

####  5.3.1 Personal Autonomy and User Agency

The use of financial data for mood state prediction and intervention presents
several open research questions for personal autonomy and user agency. Given
that sensitive data can be used to limit or control users’ behavior, it is
important to ask who has access to and ownership of the data, as well as the
power to make decisions for the user—whether that is the system itself or
their care partners. These systems can also empower users to take back control
of their financial wellbeing in the long run. However, given the nature of
impulsive spending and different mood states, there are times when user agency
has to be limited or handed-off to other to support their overall goals of
harm-reduction.

Simply put, some users might not want to step back from the ledge on their own
when faced with impulsive spending opportunities, especially when in specific
mood states. Some users may have an awareness of such behavioral patterns
about themselves, but others may not. As previous work has shown, those who
are long aware of this, often take it upon themselves to proactively curb high
volume spending (15). For these users, maintaining control of their own
financial management systems may be sufficient, as they can set up appropriate
spending limitation settings on their own when they anticipate needing them.
Other high-risk users may be less aware of their mood states and their
relationship to spending sprees. These users may benefit from giving up some
of their agency in return for more support. This could take the form of the
system acting on their behalf in certain circumstances, like if it senses mood
episode onset, or transferring some of the decision-making power to other
people.

##### Research directions

Moving forward, we should work to understand personal risk thresholds to
better determine what type of intervention is most suitable for individual
users. This personal risk threshold could be used to determine whether other
people should be involved in users’ financial decisions, and what level of
friction is the best fit to slow down their impulsive spending habits. An
ideal system should strive for a balance between autonomy, constraint and
individual risk profiles. To work out this ideal balance, future work should
be oriented around the following questions: What level of friction is
appropriate and effective? For whom? And who decides this? What level of
autonomy is needed and when? Our overall goal should be to establish how we
can help promote financial stability for vulnerable individuals while
preserving their personal autonomy as much as possible.

##### Supporting Shared and Collaborative Decision Making

Previous work has leveraged open banking data to notify pre-selected care
partners of different financial events (e.g., low balance or high-dollar
alerts) (40) to improve financial wellbeing. For those with higher risk
thresholds, one could argue that some users might benefit from more active
third-party involvement, including the delegation of power to transact and the
disclosure of financial information. In this case, care partners could play a
more active role in decision making and co-managing patient finances. For
instance, a payment system could be set to require care partner approval
before significant purchases are authorized.

A collaborative approach can help address some of overarching agency concerns.
By using a more collaborative system, control can be distributed across a team
of individuals when user agency needs to be limited. This would involve
working within their larger social support network, rather than transferring
financial decision-making power to one individual. In this case, primary users
would work with their support networks—clinician, family, or care partners—to
make a preemptive plan of action for specific situations. This could leverage
existing clinical practices (e.g., _advanced directives_) (41) to define the
conditions in which the users agency in financial decisions are limited.
Ideally, mental capacity would be assessed prior to big purchases or taking
out loans, as when individuals are in a manic mood episodes they may legally
lack the mental capacity to consent to those decisions. However, this approach
could dictate what decisions can be made on their behalf, by whom, for how
long, and under what circumstances this plan goes into effect. While this type
of agreement is traditionally a legal document drafted between patients and
clinicians, it could then be carried out and reinforced through FinTech system
architecture.

####  5.3.2 Privacy

For both institutional and individual level interventions, it is crucial to
consider privacy risks and concerns with using objective banking data for
financial intervention and ensure the systems we develop remain in the users’
best interest (12). Privacy also needs to be considered in how the outputs
from this system are presented, used, and potentially shared with others. The
main goal here should be to find the right balance between providing the most
useful, accurate information to benefit the user while also maintaining as
much of their privacy as possible. In some circumstances, users may want to
share their data with other members of their social support networks. For
this, it could be beneficial to grant information access to others at
different levels of granularity, based on their roles in the larger system.
For example, a therapist could have access to long-term behavioral data for
clinical purposes. Whereas, a friend or family member may only be provided
“need to know ” information or a task by task basis, should the user need
assistance with different financial decisions.

Given the amount of social stigma that surrounds both mental health conditions
and poor finances, the data used to support these systems is highly sensitive
and personal to users. If users are uncomfortable with the amount of data
used, how their privacy is protected, and the amount of trust given to the
system, they are unlikely to use the system long enough to see any personal
benefits. To support user adoption and long term use, it is highly important
that we ensure privacy to maintain user trust in the intervention system.
Future work is needed to understand user acceptance and the privacy trade-offs
involved with using personal data within this context.

##### Establish privacy and acceptance concerns

Through qualitative work we can gather a more in-depth understanding of the
range of privacy concerns different users may have with using objective, real-
time data to monitor and intervene on their spending behaviors. This work
could be carried out through interviews with individuals with mental health
issues, their family members and care partners, and their clinical support. By
collecting data from multiple stakeholders, we can gain in depth insights into
concerns and expectations when it comes to developing FinTech to support
mental health issues. Furthermore, providing a prototype for users to explore
during these sessions can also prompt a more thorough discussion on privacy,
trust, and shared decision making. This could give users a more concrete idea
of the kind of data involved, but also what users stand to get out of the
system.

##### Establish data sharing policies

Additional work could look at the wider context in which these systems might
be used, for the purpose of establishing ideal data sharing policies. Focus
groups with primary users and members of their social support networks could
be conducted to gather a better understanding of these concerns and
expectations of all parties involved. Similarly, scenario-based data
collection could gather insight on the specific situations where users would
be comfortable sharing their data with others and at what granularity. These
next steps would provide a better sense of privacy tradeoffs that users may
work through and help design future FinTech systems in alignment with their
specific privacy needs.

##### Privacy preserving analysis

Recent technical developments may allow for financial mental health research
to be conducted while preserving the privacy of participants. Federated
learning, for example, creates the possibility of “training statistical models
over remote devices or siloed data centers, such as mobile phones or
hospitals, while keeping data localized” (42). In this case, a user’s
financial data could be securely stored and modeled on their own smartphone
without ever being exposed to third-parties. Further, users who wish to grant
third parties access to their financial data using API-based methods have a
high level of control over what is shared. Third party access could be
restricted to certain accounts or types of transactions, to transactions
within certain date ranges, or based on certain financial conditions being
met. An individual’s trusted care partner could have a different level of
access than their accountant, for example. Perhaps most importantly, an
individual sharing their financial data using API-based methods also has the
option to revoke this access.

####  5.3.3 Equity and Preventing Discrimination

FinTech systems can operative on either an institutional or individual level,
each providing users differential access to resources. Therefore, equity and
discrimination should also be considered when developing interventions. We
should prioritize making these systems more accessible to primary users—those
who need them for help with their own finances, by promoting digital access
and increased financial citizenship and limiting possible problematic use.

##### Institutional vs. Individual approaches

With an institutional approach, financial health interventions can intervene
at the point of individual transactions and control overall money outflow from
the user’s bank account. These efforts could reduce harm for the user, such as
decreasing their likelihood of defaulting on loans or going into bankruptcy.
This could also prevent banks from granting loans to those who are not in a
state, financially and psychologically, to take on these high-risk financial
decisions. This helps the individual avoid negative financial consequences of
defaulting on these loans in the future, which is also a significant incentive
for the bank itself (43), who would otherwise not see repayment.

Conversely, individual-level FinTech systems could provide assistance outside
the confines of existing banking institutions and potentially help a wider
range of users. Rather than relying on financial transaction data from banking
institutions, an individual-level system could instead use online purchasing
data or other alternative sources. An individual-level system could then
provide financial cognitive behavioral therapies (36) reinforced through
system architecture, involve existing social support networks to help the user
with financial decisions and meet behavior change goals—outside the confines
of banking institutions.

##### Equity and Discrimination

Previous work in this space has largely focused on institutional approaches.
The lack of individual-level options limits who can benefit from them. While
institutional level interventions involving banks can help promote good money
habits, this is only accessible to those with existing financial citizenship,
who already have the financial means to maintain a traditional bank account.
Focusing solely on institutional-level interventions helps those with
privilege of existing banking access avoid future bankruptcies, foreclosures,
defaulting on loans, or other dire financial situations—potentially leaving
out those in the most need (44).

Individual-level interventions can also present similar concerns when data or
control is shared or handed off to others. Choosing these third-party allies
may be a difficult decision for users to make. Some may feel obligated to
choose those who are closest to them, like a parent or a partner, who may
expect to be given this role, even if the user does not feel comfortable with
it. Others may lack significant relationships or experience strained
relationships, leaving them with no or few options for trusted support. These
scenarios may lead users to hand over control to those who may not use it in
their best interest. Additional work is needed to uncover the range of
challenges that may present in this context, as well as how to better support
users in the process of choosing and maintaining their financial allies.

Future work is needed to address institutional and individual equity concerns.
At an institutional level, it is important to consider the bank’s role in the
overall system. This could mean limiting how much of this personal data is
accessible to banks to only the minimum amount necessary for the intervention
to function as designed. Shifting too much control to banks could give them
the power to assume all Fintech users lack the ability to make their own
financial decisions, regardless of individual circumstances or current mood
state, and deny opportunities unjustly. More research is needed to understand
the ideal role for banking institutions within the wider system and how to
balance the needs of both the institution and individual users. Future work
should also expand on individual-level options to provide more equitable
systems that operate independently from banking institutions. Improving access
to financial data for individuals would take us one step closer to this, as
open financial data is currently limited, especially in the United States.

##  6 Conclusion

Mental illness and financial instability are often linked with each other.
Financial woes can be detrimental to mental health and problematic decision
making during symptomatic periods can lead to long-term financial ruin. Recent
studies have explored the bidirectional relationship between mental illnesses
and financial instability. However, they have been mostly limited to self-
reported data, which might not provide a complete picture due to recall bias
and coarse sampling methods. This lack of granular understanding is also a
serious barrier toward developing effective, just-in-time interventions to
support financial stability for individuals with mental illnesses. In recent
years, there has been an increasing focus on open and accessible financial
data (e.g., Open Banking API). Several third-party tools and platforms now
provide access to transaction and payment data across different financial
institutes. The resultant granular and (near) real-time access to financial
data can help to identify nuanced relationships between financial
(in)stability and mental health issues as well as developing personalized,
just-in-time interventions. However, there are considerable challenges before
we can use objective financial data for mental health support.

In this paper, we aim to explore opportunities in identifying financial
“biomarkers” and intervention strategies for individuals with mental illnesses
and document their associated challenges for future research. We have
identified potentially risky financial behavioral dynamics across symptomatic
phases (e.g., ratio of increased credit spending). While we have not found
statistically significant trends nor our findings are generalizable beyond
this case, our approach provides an insight into the challenges of accessing
objective financial data. Based on our findings, we have also explored
potential intervention strategies. Furthermore, we have described technical,
ethical, and equity challenges in developing financial data-driven assessment
and intervention methods, as well as providing a broad research agenda to
address these challenges. We believe that being able to leverage objective,
personalized financial data in a privacy-preserving and ethical manner will
lead to a paradigm shift in mental health care.

## Conflict of Interest Statement

T. R. has been paid as a consultant and receives royalties from a software
company for developing an intervention around financial difficulties and
mental health. TR also acts as an advisor to a financial technology company
for which he receives shares.

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