Impact of Geographic Location on Risks of Fintech as a Representative of Financial Institutions

Abstract

The activities of contemporary financial institutions require significant geographic expansion. Even the increased level of industry digitalisation does not minimise the importance of the physical assets of financial institutions. The environmental factors specific to each geographic region can significantly influence the efficiency of operations of financial institutions. The goal of the article is to determine the impact of the geographic location of physical assets via environmental risks affecting the other risks of fintech as a representative of financial institutions. The impact is determined by the employment of the PLS-SEM model implemented in SmartPLS 4.0 software. The model determines the impact of environmental risks on governance risks, operational risks, human resources and safety risks, ICT risks, compliance risks, and strategic risks. These groups of risks form the latent variables, which comprise the experts’ estimation of threats and vulnerabilities impacts and their likelihoods. After testing five hypotheses, two of them were supported—environmental risks impact human resources safety risks and operational risks.

1. Introduction

For a long time, financial institutions have been in a constant race for geographic expansion: opening new branches and affiliates and merging with other economic entities [1]. Despite the development of digitalisation, the industry is not trying to eliminate physical property. On the contrary, new services require infrastructure development: cooperation with international IT companies, building server rooms and data centres, and opening local subsidiaries dealing with legal and security issues [2]. Such an expansion most often takes into account the problems of anthropogeography only.

First of all, it comes down to such legal issues as local regulation of financial institutions, which can either promote or restrict fintech innovation via the existence or absence of friendly regulations, providing regulatory sandboxes that allow start-ups to test new services in a controlled environment. Another factor is the ease of obtaining financial licenses. In regions with favourable licensing frameworks, fintech companies can operate more easily and expand faster [3,4,5]. In the second place, the stability of the economy and local currency is necessarily considered. Usually, such locations also have access to a capable workforce with a high level of expertise, which allows fintech to grow and function well [6,7]. The third factor is the assessment of customer demands. Local traditions and customer behaviour influence how financial services are used [8,9]. The fourth factor is proximity to strategic financial hubs. Geographic location in a financial hub influences the reputation of the company and allows attracting investors; these areas traditionally have a high concentration of investors looking to support fintech start-ups [10,11,12,13]. The last but an important aspect is the additional opportunities provided by the region. This can be substantial support from the government in the form of grants, various incentives, tax breaks, etc. [14,15,16,17]. It can also be a strong technological sector, reliable internet connection, cheap cloud services or simply access to a major market [18,19,20].

This approach to assessing the geography of expansion is logical and aimed at maximising the fintech opportunities, but it has one disadvantage in the form of a lack of assessment of the environmental impact on the material base. The physical assets of fintech companies are subjected to the effects of air and water pollution since air and water are used for the operation of fintech. Various environmental incidents, for example, earthquakes or floods, can have a drastic ruining influence on equipment employed by fintech. Sufficient water supply is essential for operating the physical assets. Ignoring geographic risks can cause significant financial losses and, in some cases, significantly set back a company’s development [21,22,23,24]. However, there is a lack of studies devoted exactly to the determination of the impact of environmental risks on other risks of fintech.

The aim of the study is to determine the impact of the geographic location of physical assets via environmental risks affecting the other risks of fintech. We hypothesised that geographic location has a direct impact on the fintech risks. To test this hypothesis, we manifested location through environmental risks, as this is a meaningful way to represent geographic diversity in verifiable numerical values. The European region was chosen for this study, although for practical use of the hypotheses tested in the study, regions should be considered in a much closer approximation, as geographical location features may differ for even very close settlements.

2. Materials and Methods

The methodology for estimating the impact of environmental factors on other groups of risks comprises several steps: determination of criteria for risk estimation, choice of expert board, and application of PLS-SEM to the obtained results of experts’ risk assessment.

2.1. Determination of Financial and Environmental Risks

Risk is associated with the uncertainty surrounding any deviation from projected outcomes [25]. There are differences in the types, sources, applications, and effects of risks. For instance, among the major risk factors that small and medium-sized businesses must contend with in their operations are financial [22], bankruptcy [23], and export risks [24]. However, vulnerabilities and threats are two risk components which should be regarded as elements required for risk assessment [25,26,27]. Vulnerabilities are weaknesses that could be used to carry out certain actions that could put the business in danger [28,29,30,31,32,33,34,35]. When vulnerabilities and threats combine, the business is put in danger. The likelihood of threats and vulnerabilities being implemented is another crucial component. This likelihood is based on factual information about how frequently threats and vulnerabilities are implemented in real-world settings during reporting periods. In other words, risk estimation tracks how frequently a specific threat or vulnerability manifests in real-world situations [36,37].

This study pays special attention to environmental risks and their impact on the material base of fintech.

The list of the main environmental issues affecting the operations of fintech and included in ICT risks comprises such factors as ecosystem pollution (air pollution [37,38], environmental incidents [39,40], water scarcity, or lack or insufficient supply of water [41], and extreme weather events—earthquakes [41,42,43], flooding [44,45,46,47,48,49], hurricanes [50,51,52], lightning, heatwaves [53,54], and fire).

Other groups of risks which are estimated in the study are as follows:

• Governance risks refer to the possibility that the organisation’s policies, procedures, and other systems, which are crucial for supervision and decision-making, will not work as intended. This risk is associated with the directors’ decisions made about the composition, leadership, and organisation of the board, as well as the decisions related to applicable legal framework [3].
• Operational risks refer to the possibility that the business will suffer a loss as a result of insufficient or malfunctioning internal systems, personnel, processes, or outside events [33,51].
• Human resources and safety risks relate to the threats to the business’s operations that human resources present and to the possibility that an employee in a particular workplace could suffer harm, an injury, pass away, or become ill due to a health and safety hazard [52,53,55,56].
• ICT risks are associated with malicious attacks, spam, viruses, hardware and software malfunctions, and other ICT issues [32].
• Environmental/external risks are risks resulting from external economic events that the corporate structure cannot control [54,57].
• Compliance risk: the possibility that the company will lose money, damage its reputation, or face legal action as a result of its inability to follow, manage, solve or significantly lower regulatory issues [5,58].
• Strategic risks are associated with the potential for loss resulting from poorly aligned business decisions with strategic goals, the inability to respond to industry and macroeconomic dynamics, and the unsuccessful implementation of policies and processes intended to achieve those goals [59].

2.2. Risk Estimation

The research considers the following groups of risks: environmental risks, human safety risks, ICT risks, operational risks, strategic risks, and governance risks. These risk groups include the whole set of risks determined for the fintech by scientific and legal sources.

For each group of risks, the scores of estimations of threats and vulnerabilities impact from very low (1) to very high (5) were determined.

The numeric values from 1 (very low) to 5 (very high) were assigned to the impacts. The likelihoods of occurrence of events corresponding to the risks were also estimated by the experts from very low (1) to very high (5) [29].

The following formulae were applied to risk calculation [30]:

$$\mathrm{I}\mathrm{m}={\displaystyle \frac{\sum _{\mathrm{i}=1}^{\mathrm{n}}({\mathrm{T}\mathrm{i}}_{\mathrm{i}}+\frac{{(\mathrm{V}\mathrm{i}}_1+{\mathrm{V}\mathrm{i}}_2+\cdots +{\mathrm{V}\mathrm{i}}_{\mathrm{m}})}{\mathrm{m}})}{\mathrm{n}\times 2}}$$

(1)

where Im is the consequence of the risk (risk’s impact), Ti is the impact of each threat within the risk category, Vi is impact from vulnerabilities, m is total vulnerabilities within a specific risk category, n is count of threats within a particular risk group.

$$\mathrm{L}={\displaystyle \frac{\sum _{\mathrm{i}=1}^{\mathrm{n}}({\mathrm{T}\mathrm{l}}_{\mathrm{i}}+\frac{({\mathrm{V}\mathrm{l}}_1+{\mathrm{V}\mathrm{l}}_2+\cdots +{\mathrm{V}\mathrm{l}}_{\mathrm{m}})}{\mathrm{m}})}{\mathrm{n}\times 2}}$$

(2)

where L is the probability of the risk (risk’s likelihood), Tl is the likelihood of each threat in the risk set, Vl is likelihood attributed to vulnerabilities, m is count of vulnerabilities in a given risk set, n is total threats in the specified risk group.

And the risk is finally calculated as follows [60]:

$$\mathrm{IR}=\mathrm{Im}\times \mathrm{L}$$

(3)

where IR is inherent risk, Im is the risk’s impact, and L is the risk’s likelihood.

2.3. Fintech as a Representative of Financial Institutions

Fintech is now a full-fledged representative of financial institutions. In the article [61] four different types of fintech are discussed, they are as follows:

• Financing;
• Payments;
• Asset management;
• Insurance.

Insurance is a very specific type of fintech, which does not provide payment operations, has very specific risks and requires specific actions. These risks are beyond the framework of this research, and they can be considered in further studies.

All other types of fintech are connected with payment operations, they work directly with business entities and natural persons and present the same groups of risks. The risk groups of these entities’ representatives are analysed in this article.

According to the vision of the authors, the fintech companies, the risks of which serve as the basis of this study, must represent one of the types of fintech with payment operations, work in several countries, and have risk indicators similar to other companies of this range. Since the risks are estimated on the basis of threats and vulnerabilities, each fintech will be represented by thousands of risk values, and these risk values will be the same for the same type of fintech operating in the same region. Therefore, each company should represent the whole population of the same type of fintech. If considering more companies, the values of risk estimations will be repeated. Therefore, it was decided to analyse the data of five companies, fully representing the types of fintech population in the EU region.

2.4. Experts’ Board

The risk officers from 5 fintech were chosen as experts for risk estimations.

The list of criteria for choice of the organisations working in the financial market is as follows:

• The company should be registered in the EU;
• The company should be subjected to the regulation of legal supervisor;
• The company is involved in payment operations;
• The company should have an official responsible risk officer/risk specialist.

As a result, 5 financial institutions were offered to participate in the experts’ board. These institutions are fintech companies; they are registered in the EU; moreover, two of them are passported for operations in all EU members. All the selected institutions are supervised by the European regulator. All these institutions are fintech companies providing the payment operations. Moreover, they represent three of four main business models of fintech (the fourth model does not work with payment operations, and, correspondently, it is beyond the frameworks of this study). The selected institutions have responsible risk officers who represent these institutions in this research.

Therefore, we assume that the experts’ body is representative of the whole population of fintech in the EU. In total, there were considered 217 threats and 78 vulnerabilities represented by 2950 indicators.

The experts estimate the impact of risks based on their experience according to scale from 1 to 5, and likelihoods were estimated on the basis of existing statistics.

2.5. PLS-SEM Model

Using the numeric data obtained from the risk estimations by experts, the PLS-SEM model in SmartPLS 4.0 software was constructed.

The choice of PLS-SEM (partial least squares structural equation modelling) is based on the fact that it allows determining the relationships among several latent variables, it is used in cases of confirmatory or exploratory studies, and, moreover, it does not put forward the requirements towards data sample size or normal distribution of data. Therefore, it can be successfully applied to this study to obtain the expected results.

The reference criteria for estimation of outer model, inner model and entire model are presented in

Table 1. The reference criteria for estimating outer model (Source: based on [29,30,62]).

Indicator	Criterion	Value	Source
Number of iterations	The sum of the changes in the outer weights between two iterations	5–10	[62,63]
		Max 300
Indicators loadings	The degree to which the indicator represents the latent variable; the links between the indicator and the latent variable	>0.70 (highly satisfactory)	[64,65,66,67]
		>0.50 <0.70 (acceptable)
		>0.40 <0.50 (week)
Convergent validity (the research variables accurately capture the intended latent constructs, showcasing their validity in convergence)	The degree to which one test is associated with other tests that assess the same or comparable constructs is known as convergent validity [62]	>0.80 (satisfactory)	[68]
		>0.70 <0.80 (acceptable)
		0.60 <0.70 (Exploratory study acceptable range is 0.60 to 0.70)	[68]
	The average variance extracted (AVE)	>0.5	[69]
		AVE >0.5 and CR <0.6	[70]
Discriminant validity: Fornell and Larcker (F&L) Cross-loading method, HTMT	Shows whether seemingly unrelated concepts or measurements are in fact unrelated	HTMT: <0.85 for theoretically distinct constructs	[71,72]
		<0.90 for analogous constructs

,

Table 2. The reference criteria for estimating inner model. (Source: based on [29,30,62]).

Indicator	Criterion	Value	Source
Coefficient of determination	Evaluation of the strength of the linear relationship between two variables (higher value is preferred)	0.67 (substantial)	[73,74]
		0.33 (average)
		0.19 (weak)
Standardised path coefficients	Demonstrates the relative magnitude of the effects of various explanatory variables; estimation of the importance and the confidence intervals	From −1 to +1.
Effect size (f2)	Evaluates the degree to which two variables in a population are associated	0.35 (strong effects)	[73,75]
		0.15 (moderate)
		0.02 (weak)
Variance inflation factor	Estimated the degree of multicollinearity of the data (VIF)	<3.3	[76,77,78]
p-value	The probability of the statistical test’s null hypothesis is true	Based on the degrees of freedom p < 0.05	[64]
Predictive relevance Q2	Demonstrates how well the model predicts the future results	>0.5	[74]

and

Table 3. The reference criteria for estimating the entire model (Source: based on [29,30,62]).

Indicator	Criterion	Value	Source
Standardised root mean square residual (SRMR)	Shows the discrepancy between the correlation matrix’s actual and model-predicted correlations.	<0.08	[72]
Bentler and Bonett Index: normed fit index (NFI)	It compares the Chi2 value of the proposed model against a meaningful benchmark	>0.09, the closer NFI to 1, the better the match	[79,80]

respectively.

The model is based on 7 latent variables:

• Governance risks;
• Operational risks;
• Human resources and safety risks;
• ICT risks;
• Environmental/external risks;
• Compliance risks;
• Strategic risks.

Each latent variable includes the experts’ estimation of threats and vulnerabilities impacts and their likelihoods.

The following hypotheses were set:

H1. 

Environmental risks have a direct significant impact on governance risks;

H2. 

Environmental risks have a direct significant impact on human and safety risks;

H3. 

Environmental risks have a direct significant impact on strategic risks;

H4. 

Environmental risks have a direct significant impact on compliance risks;

H5. 

Environmental risks have a direct significant impact on operational risks;

H6. 

Environmental risks have a direct significant impact on ICT risks.

3. Results

Based on the set hypotheses and experts’ estimations, the PLS-SEM model was constructed (see Figure 1 and

Table 4. Results summary for reflective measurement models.

Latent Variable		Convergent Validity			Internal Consistency Reliability		Discriminant Validity
		Loadings	Indicator Reliability	AVE	Cronbach’s Alpha	Composite Reliability (ρc)	HTMT
		>0.7	>0.5	>0.5	>0.7	>0.6	Significantly Lower Than 0.85
Environmental Risk	Compliance (1)	0.910	0.857	0.764	0.964	0.941	0.097
	Compliance (2)	0.922	0.864
	Compliance (3)	0.753	0.863
	Compliance (4)	0.797	0.856
	Compliance (5)	0.970	0.853
	Human Safety (1)	0.978	0.957	0.968	0.992	0.993	0.607
	Human Safety (2)	0.944	0.983
	Human Safety (3)	0.992	n/a
	Human Safety (4)	0.967	n/a
	Human Safety (5)	0.989	n/a
	ICT (1)	0.990	0.980	0.981	0.995	0.996	0.348
	ICT (2)	0.993	0.982
	ICT (3)	0.991	0.980
	ICT (4)	0.988	0.976
	ICT (5)	0.991	0.981
	Operational (1)	0.951	0.944	0.913	0.976	0.981	0.445
	Operational (2)	0.955	0.950
	Operational (3)	0.945	0.933
	Operational (4)	0.962	0.949
	Operational (5)	0.964	0.955
	Strategy (1)	0.959	0.950	0.956	0.989	0.991	0.310
	Strategy (2)	0.977	0.967
	Strategy (3)	0.986	0.975
	Strategy (4)	0.988	0.977
	Strategy (5)	0.984	0.971
	Governance (1)	0.959	0.946	0.956	0.983	0.991	0.269
	Governance (2)	0.976	0.961
	Governance (3)	0.956	0.942
	Governance (4)	0.974	0.956
	Governance (5)	0.971	0.958

). The above-described latent variables were used: environmental risk as an influencing variable and governance, human safety, strategic, compliance, ICT and operational risks as dependent variables. The model corresponds to the set hypotheses.

The latent variables are shown by blue circles, while indicators are shown by yellow rectangles. R² are in the centres of latent variables. The path coefficients are on the arrows from environmental risk construct to dependent variables. Each latent variable includes the indicators (yellow rectangles), and the arrows from construct to indicator show the loading.

All the measurements indicating the quality of the model are presented below.

The quality of the model is supported by the presented measurements. Further, the estimation of obtained results is demonstrated in Section 3.1.

3.1. Result Assessment

To assess the reliability of the model and explain the results for the standardised root mean square residual (SRMR) and d_ULS (unweighted least squares discrepancy), we will examine key indicators of model fit and the robustness of the construct measures.

3.1.1. Model Reliability

Reliability in a PLS-SEM model refers to the consistency of the constructs in representing the underlying latent variables. Several metrics are used to assess reliability, including Cronbach’s alpha, composite reliability (ρa and ρc), and average variance extracted (AVE).

a. Cronbach’s Alpha:

Cronbach’s alpha measures internal consistency reliability, with values between 0.60 and 0.90 considered acceptable. All constructs have very high Cronbach’s alpha values above 0.9, indicating excellent internal consistency across all items representing each latent variable.

b. Composite Reliability (ρa and ρc):

Composite reliability evaluates the shared variance among the observed indicators. The threshold is also between 0.60 and 0.90. The obtained values are above 0.9, which suggests that the constructs are highly reliable, as they significantly exceed the minimum required thresholds.

c. Average Variance Extracted (AVE):

The AVE measures the level of variance captured by the latent variable versus the variance due to measurement error. A value above 0.50 indicates good convergent validity. The high AVE values further confirm that the constructs are valid and reliable, capturing significant variance from their respective indicators. In this model, one variable has an AVE above 0.7, while others have an AVE above 0.9.

3.1.2. Discriminant Validity

All three measures—the Fornell–Larcker (F&L) criterion, the cross-loading method, and HTMT—demonstrate high discriminant validity. The variables are unrelated and consist of different indicators.

3.1.3. Model Fit: SRMR and d_ULS

a. Standardised Root Mean Square Residual (SRMR)

The SRMR is a model fit index, which measures the difference between the observed correlation matrix and the model’s predicted correlation matrix. An SRMR value below 0.08 indicates a good fit between the model and the data.

-

In this model, the SRMR value is 0.08, exactly at the threshold for acceptable model fit. This indicates that the difference between the predicted and observed correlations is small, and the model fits the data well, though it is close to the upper limit of acceptability.

b. d_ULS (Unweighted Least Squares Discrepancy)

The d_ULS is another goodness-of-fit indicator used explicitly in PLS-SEM for assessing the discrepancy between the empirical and predicted covariance matrices. A lower value indicates a better model fit. However, unlike SRMR, there is no strict threshold for an acceptable d_ULS value.

-

The d_ULS value in this model is 4.025. This value can be interpreted by comparing it to other models or assessing it in context with other fit measures, such as the SRMR. Given the acceptable SRMR value of 0.08, we can infer that while d_ULS might seem relatively high, the overall model fit remains within an acceptable range.

The model exhibits strong internal consistency reliability and convergent validity, as evidenced by high values of Cronbach’s alpha, composite reliability, and AVE. The SRMR value is at the threshold of acceptability, suggesting that the model fits the data reasonably well, though future improvements might slightly reduce the residual differences between the predicted and observed correlation matrices. The d_ULS value, while not benchmarked, appears reasonable when considered alongside the SRMR, supporting the conclusion that the model’s overall fit is acceptable.

3.1.4. Testing Results of the Total Effects

The total effects are measured via f² effect size and significance (see Figure 2). These measurements allow for determining whether the hypotheses are confirmed or rejected.

As we see (

Table 5. Significance testing results of the total effects.

	Total Effect	t Values	p Values	95% Confidence Intervals	Total Effect
Environment risk -> compliance risk	−0.168	0.509	0.611	[−0.589; 0.489]	No
Environment risk -> human safety risk	0.605	2.220	0.026	[−0.070; 0.948]	Yes
Environment risk -> ICT risk	0.365	1.943	0.052	[−0.258; 0.661]	No
Environment risk -> operational risk	−0.445	1.971	0.049	[−0.725; 0.298]	Yes
Environment risk -> strategy risk	−0.313	1.558	0.119	[−0.592; 0.269]	No
Environment risk -> governance risk	−0.270	1.609	0.108	[−0.516; 0.228]	No

), three variables—human safety risks, ICT risks and operational risks—show moderate or strong effects, they are highly associated with environmental risks. However, other risks have weak effects.

The analysis of the model demonstrates how environmental risk, a key factor in the sustainability of the environment, impacts various organisational risks: compliance, human safety, ICT, operational, strategy, and governance. The results from the PLS-SEM analysis show varying degrees of influence, some of which are statistically significant (

Table 6. Results of testing the hypotheses.

No.	Hypotheses	Results
H1	Environmental risks have a direct significant impact on governance risks;	Rejected
H2	Environmental risks have a direct significant impact on human and safety risks;	Confirmed
H3	Environmental risks have a direct significant impact on strategic risks;	Rejected
H4	Environmental risks have a direct significant impact on compliance risks;	Rejected
H5	Environmental risks have a direct significant impact on operational risks;	Confirmed
H6	Environmental risks have a direct significant impact on ICT risks.	Rejected

).

Environmental Risk and Compliance Risk

Environmental risk has a negative but statistically insignificant effect on compliance risk, with a path coefficient of −0.168 (t-statistic = 0.509, p = 0.611). This suggests that changes in environmental risk do not directly impact compliance risk within the given model. The confidence interval supports this finding, ranging from −0.589 to 0.489, further confirming the lack of significance.

Environmental Risk and Human Safety Risk

In contrast, environmental risk significantly influences human safety risk, with a positive path coefficient of 0.605 (t-statistic = 2.220, p = 0.026). This finding implies that an increase in environmental risk is associated with an increase in human safety risks. The bias-corrected confidence interval (−0.070; 0.948;) validates the significance of this relationship, indicating that environmental concerns, such as sustainability issues, pose direct risks to human safety.

Environmental Risk and ICT Risk

The relationship between environmental risk and ICT risk is borderline significant, with a path coefficient of 0.365 (t-statistic = 1.943, p = 0.052). While this result suggests a potential positive effect of environmental risk on ICT risk, it falls slightly above the traditional threshold for significance. The confidence interval (−0.258; 0.661) indicates that this effect requires further investigation but points to a possible emerging risk in ICT systems influenced by environmental factors.

Environmental Risk and Operational Risk

Environmental risk has a significant negative impact on operational risk, with a path coefficient of −0.445 (t-statistic = 1.971, p = 0.049). This suggests that increased environmental risk leads to a reduction in operational risk, possibly indicating adaptive operational strategies that mitigate the effects of environmental challenges. The confidence interval (−0.725; 0.298) further supports the statistical significance of this finding.

Environmental Risk and Strategy Risk

Although environmental risk has a negative effect on strategy risk (−0.313–0.313−0.313), this relationship is not statistically significant (t-statistic = 1.558, p = 0.119). The confidence interval (−0.592; 0.269) suggests that the model does not conclusively support environmental risk as a predictor of strategy risk.

Environmental Risk and Governance Risk

Lastly, environmental risk has a negative but insignificant effect on governance risk, with a path coefficient of −0.270 (t-statistic = 1.609, p = 0.108). The confidence interval (−0.516; 0.228) shows that the data does not support this relationship, indicating that governance structures may not be directly influenced by environmental risk in this context.

Therefore, we conclude that the environmental risks affect human safety and operational risks of fintech. Since environmental risks significantly depend on geographic location, the importance of geographic location for risk management of fintech is evident.

4. Discussion

Environmental risk is inherently tied to geography because the manifestation and impact of such risks are geographically dependent. For example, natural disasters like floods, droughts, or storms are influenced by a region’s topography, climate patterns, and human activity [43]. Geographic variability thus shapes how regions experience and mitigate environmental risks, with some areas being more vulnerable than others due to factors like proximity to coasts, fault lines, or environmental degradation.

However, the article does not deal with the risks directly but uses environmental risks as a factor of the impact of geographic location on the physical assets of fintech. We demonstrate that the statistical model can support this approach. Other articles, including the ones cited in this study, discuss risks directly. Thus, it is difficult to compare the obtained results with the results of other scholars.

In the process of research, two of the set hypotheses were confirmed; the obtained results of the study demonstrate that the environmental risks have a significant effect on human safety and operational risks of fintech. Therefore, considering the interconnection of environmental risks and geographic region, it can be concluded that the geographic location should be taken into consideration in the risk management of fintech, and the mitigating measures for the risks, especially human safety and operational risks, should be taken with great attention to the geographic component.

Sustainability and Regional Context

Sustainability efforts are deeply tied to the geographical context. Homer-Dixon [81] has explored how environmental stress, influenced by factors like geopolitical events, can exacerbate these risks. His research highlights that geopolitical instability in specific regions can increase environmental stress by disrupting resource management and sustainable practices. For instance, areas experiencing political conflict often face challenges in implementing effective environmental policies, which may contribute to greater environmental degradation and heightened risk exposure.

What works in one region may not be suitable for another due to differing environmental conditions, resource availability, and socio-economic factors. Adger’s [82] research emphasises the need for a tailored, region-specific approach to environmental risk management that incorporates local knowledge and conditions, highlighting the intersection of geography and sustainability efforts.

The management of environmental risks is profoundly shaped by geography, as regional factors such as climate, physical landscapes, and socio-political conditions heavily influence how these risks manifest and can be mitigated. W. Neil Adger [82] emphasises the importance of incorporating these geographical variables into sustainability and risk management frameworks. His work suggests that understanding regional differences is crucial to developing strategies tailored to specific locations, making risk management more effective.

The presented manuscript is a logical continuation of the previous studies of the authors devoted to the anthropocentric evolution of regions by the development of smart city ecology [83,84], smart economy [85,86], smart people [87], smart mobility [88], human capital development [84,89], etc.

Thus, another very important component of contemporary risk management with consideration of environmental and geographic issues is the smart city concept, which has become very important due to the great urbanisation processes. Smart cities contribute significantly to the changing environmental situation in the region. They participate not only in meeting the new environmental challenges but also shapes the regions actively. Smart cities act via a smart and sharing economy [85,90], organisation of urban space [8,86], state-of-the-art IT infrastructure [13,20,87,88], and so on. Therefore, these issues can also be analysed and estimated in the risk management of financial institutions.

Sustainable transport is also a component of sustainable ecosystems. The organisation of transportation in the region can decrease [91,92], or vice versa [93,94], increase the threat to the environment and correspondently affect the risks of pollution. Transport infrastructure includes vehicle transportation [95,96], micro-mobility transport [62,97], marine transportation [98,99], train and aviation transport [100,101], and the level of sustainability of transportation will affect the environment and risks of financial institutions.

This geographic consideration aligns with the PLS-SEM analysis results in several ways. For example, the analysis demonstrates that environmental risk significantly affects human safety risk and operational risk (positive and negative correlations, respectively). These outcomes could vary depending on geographic context, as different regions might experience varying levels of exposure to environmental hazards and have differing capacities for operational resilience. The negative relationship between environmental risk and operational risk may indicate that in regions where sustainability practices are already integrated, environmental risk can prompt more robust operational strategies. Conversely, in areas where geographic and environmental vulnerabilities are higher, human safety risks tend to increase, as shown by the significant positive impact in the analysis.

Thus, geography not only shapes the risks themselves but also influences how organisations respond to them, highlighting the need for location-specific strategies, as suggested by [78]. These strategies must account for local environmental conditions and socio-political dynamics to effectively reduce risks and promote sustainability.

Research Limitations

The study is limited to the listed risks. Other risk groups can also be considered and provide interesting results.

Another limitation is the risk assessment methodology. Application of other methods can result in other estimations of threats and vulnerabilities, impacts and likelihoods.

The authors used the PLS-SEM model, which they supposed to be the most suitable for this type of research. However, a variety of modern statistical tools can be employed for this analysis.

The research is based on fintech as a representative of financial institutions. Three of four types of fintech are represented, the fourth type does not provide payment operations. Therefore, other types of financial institutions or the fourth type of fintech can present other relationships between environmental risk and other risks of financial institutions.

Further Research

The authors plan to continue the analysis of the dependence of risks of financial institutions on environmental risks. Other groups of risks will be tested.

The authors plan to apply the Bayesian network approach to risk assessment for the financial institutions in the EU countries.

5. Conclusions

The study considers the impact of environmental risks on other risks of financial institutions.

The geographic expansion of fintech results in the growth of their physical property. New solutions put forward the requirements for infrastructure development, which can include cooperation with international IT companies, building server rooms and data centres, and opening local subsidiaries dealing with legal and security issues. As a result, among many risks interconnected with financial activities, it is necessary to consider the environmental risks as well. These risks are closely connected with the geographic location of the physical components of fintech. The main environmental problems influencing the functioning of the fintech comprise air pollution, environmental incidents, water scarcity, lack or insufficient supply of water, earthquakes, flooding, hurricanes, lightning, heatwaves, and fire.

Other risks considered in this study are governance risks, operational risks, human resources and safety risks, ICT risks, compliance risks, and strategic risks. All these groups of risks form the latent variable for the PLS-SEM model. Each latent variable includes the experts’ estimation of threats and vulnerabilities impacts and their likelihoods.

The model was used for testing six hypotheses on the impact of environmental risks on other groups of risks. As a result, two hypotheses were confirmed—environmental risks have an impact on human resources, safety risks and operational risks.

Negative environmental events can create a certain danger to employees’ health, threaten their efficient performance, and affect the entire activities of fintech. These negative events can interrupt or affect the functioning of the financial institution by impacting the physical infrastructure.

Operational risks from environmental events can lead to financial losses for the institution. For example, natural disasters may disrupt staff, prevent them from getting to work, or interfere with other internal operations.

Mitigating these risks can improve the functioning of fintech and create possibilities for its development. The mitigating measures greatly depend on the geographic location of the physical assets of the financial institution. Therefore, risk officers in fintech should consider the geographic locations of both staff and company assets when assessing risks and choosing mitigation strategies to reduce them.

The theoretical value of the article is proving the existence of significant statistical relationships between environmental risk and human resources and safety risks and operational risks. The practical implication is in the demonstration that environmental risk, dangerous for the company, affects other risks, and mitigation of it can improve the situation with human resources, safety risks and operational risks; therefore, it should be addressed by risk officers with special attention.