“Identifying the volatility of compliance risks for the pension custodian banks”

The high probability of risk transfer from banks to their counterparties in the field of non-state pension provision (pension account owners, non-state pension funds, insurance companies, asset management companies, etc.) determines the relevance of this study. The paper aims to develop a toolkit for identifying the compliance risk volatility for pension custodian banks based on causal modeling. This toolkit contributes to: 1) tentative cognitive mapping of the causal relationship be- tween the compliance risks of pension custodian banks in the field of financial monitoring and financial and reputational risks to assess their acceptability by stakeholders in non-state pension programs, and 2) impulse modeling. The created toolkit is based on the performance data provided by Ukrainian banks, as well as on the reports of the National Bank of Ukraine. Apparently, an increase in penalty rates by 0.1% would reduce the compliance risks for banks by 0.03%, and the number of violations in financial monitoring (specifically the improper assessment/ reassessment of customer risks) by 0.01%. In turn, the compliance risk volatility inher- ent in custodian banks affects the variability of their reputational and financial risks. Thus, reducing the compliance risks by 0.1% would improve the reputation of banks and increase their regulatory capital by 0.01%. The study findings substantiate the use of the created toolkit to supplement the risk profile components for pension custodian banks, thereby demonstrating the potential volatility of their compliance risks and their consequences for banks and individual groups of their stakeholders. risk assessment, and management; bank management; monitoring;


INTRODUCTION
In addition to the key functions inherent in the specifics of banking business, integration into non-state pension programs increases the functional load on banks with respect to pension assets (directly in cooperation with the owners of pension accounts; through "chain transactions" with non-state pension funds, insurance companies, asset management companies), as well as the complex integration of the functions performed by the founder, asset administrator, and asset manager when creating corporate pension funds. Accordingly, when the role of banks increases, financial and social effects are synthesized, ultimately enhancing the importance of banks in ensuring the stability of the national financial system and implementing the strategically vital tasks to promote socio-economic development in a country.
The defined contribution pension system in Ukraine is still in its infancy. In 2018-2020, the total asset value of non-state pension funds grew by almost 32% (from UAH 2,651,000,000 to UAH 3,488,600,000), while the profit from the asset investment increased by 57% (from UAH 1,355,300,000 to UAH 2,119,700,000). Meanwhile, bank deposits and public securities as fixed income instruments (despite the existing restrictions on the assets investment by non-state pension funds in the amount of 50% for each specific investment area) account for more than 80% of invested pension assets.
Banks are naturally interested in raising additional funds, but other entities involved in pension asset depositing and safekeeping programs ("chain transactions") are exposed to certain threats, taking into account the sufficiently risky activities conducted by the banks. Therefore, to protect the property rights and lawful interests of the participants of non-state pension funds, it is necessary to (1) intensively supervise and monitor banks that are engaged in the pension assets safekeeping (the National Securities and Stock Market Commission licenses the Ukrainian banks to perform the depositary functions in accordance with its own procedures); and (2) form and implement the prudent approach to the policy relevant issues by the counterparties of pension custodians concerning the establishment and development of mutual relationship, i.e. the reasonable approach to the custodian bank selection, as well as its performance monitoring in order to secure them against any potential transference of the bank's financial risks. As it happens, pension assets kept in the Ukrainian banks are poorly secured so far. Once these banks are qualified as insolvent, the return of the pension assets is under certain threats. On the one hand, this is because non-state pension funds' assets (with the exception of deposits) are not included in the liquidation estate of such banks; on the other hand, this is caused by the return of deposits, for which the insolvent banks retain liabilities before the non-state pension funds at the cost of the liquidation estate sale only on a seventh-priority basis.
In addition to financial risks that can directly affect the financial position of pension custodian banks and define their stability, the need to minimize banks' exposure to compliance risks is of paramount importance for other participants of non-state pension programs interacting with these banks. Otherwise, financial and reputational damage resulting from the compliance risk actualization will affect their financial standing, destabilize and pose a threat to pension assets. Therefore, given the importance of the issues pertaining to compliance control and compliance risk assessment, not only for pension custodian banks and supervisory agencies, but also for their counterparties in the field of non-state pension programs, it is necessary to develop a theoretical basis and tools to assess the potential increase and materialization of the compliance risks.

Banks' compliance with regulatory requirements
The regulations applicable to implementing the compliance functions and the key principles of the institutional compliance management in banks were developed by the Basel Committee on Banking Supervision and presented as consultation documents (BCBS, 2003(BCBS, -2005 According to analytical studies (Birindelli & Feretti, 2008) on the identification of compliance regularities and, specifically, the comparison of compliance systems used by Italian banks and structural subdivisions of foreign banking groups, the latter seem to pay more attention to compliance issues. Gabbi et al. (2011) also factored in the criterion of international activity while cross-checking various groups of financial institutions (banks, insurance and investment companies) and pointed out that financial go-betweens with foreign capital appeared to be more experienced in terms of their ability to assess compliance risks and recognize their danger. In addition, Gabbi  Considering the need to achieve the synergy between the objectives of compliance functions and risk management in banks (Fox, 1999), Edwards and Wolfe (2007) and Mikes (2008) studied certain aspects concerning the significance of compliance with regulatory requirements and the improvement of compliance functions and risk management not only for financial institutions, but also for their stakeholders. Edwards and Wolfe (2007) point out that an important aspect of day-to-day life of any corporate business, especially financial institutions of the United Kingdom, implies the ability to demonstrate the compliance with and competence in regulatory requirements to regulators and other third parties. Mikes (2008) also emphatically indicates that the interaction with a large number of stakeholders would require that risk managers form a clearly defined position regarding risk functions in the organizational management process and clarify the involved parties' expectations with respect to risk management.
As for the intersection of the banking sector and non-state pension provision, relevant issues appear to be more related to defining the specific features of the activities conducted by custodian banks and the problems inherent in the custodian business ( Kyalo (2014) addressed the problems faced by banks that provide custodian services in the context of pension assets. The author also found out that Kenyan banks engaged in the depositary services provision are most frequently exposed to the regulatory non-compliance, credit, strategic, and reputational risks. That is why custodian banks are also subject to high compliance costs (Coste et al., 2021) and get involved in the efforts to curtail money laundering (Chan et al., 2007).

Compliance risks of banks: assessment and management issues
The compliance risk is most frequently construed as the likelihood of legal sanctions, claims of supervisory authorities, considerable financial or reputational damage that an organization may face resulting from its failure to comply with the applicable laws, bylaws, industrial standards, best practices, and ethical standards. Given the substantive characteristics of the compliance risk, its conse-quences for banks can be disastrous. According to Safari et al. (2016), the institutions are exposed to the compliance risk due to their relationship with numerous interested parties. As it follows from to the paper (PricewaterhouseCoopers LLP, 2003), one of the reasons why financial institutions are unable to comply with the regulatory requirements is the gap between the historical approach to compliance and what is really needed for assessing and reducing the totality of the existing risks. Due to the negative perception by regulators, customers, and other stakeholders concerned, these organizations are threatened with losing their reputation. On the other hand, the actualization of compliance risks may affect stakeholders.
Compliance risk assessment is carried out in several ways: in terms of the probability of events occurring and their consequences, if they do occur (Ludwick, 2006;Birindelli & Feretti, 2008), taking into account the impact factors (Haynes, 2005) or the risk probability and rating of the risk factors (Losiewicz-Dniestrzanska, 2015). When reviewing the compliance risks identification and assessment for banks, Asenov (2015) suggests applying the following indicators: the number of customer complaints, the amount of cash payments, the number and frequency of the detected violations for the banks' failure to comply with the applicable laws, etc. In the context of compliance risk assessment, Nicolas and May (2017) deem it necessary to take into account the findings of the supervisory control, the data obtained from the internal compliance tests and the external audit results, external inspections and calculations, customer data, business data, etc.
According to Ernst & Young (E&Y, 2021), the main areas covered by the banks' compliance function are as follows: compliance with the requirements of the applicable laws, financial crimes enforcement, data confidentiality, cybersecurity, operational activities, and interaction with the third parties (counterparties). Some areas are characterized by the related risks, one of which is the compliance risk associated with the prevention of money laundering, terrorist financing and proliferation of weapons of mass destruction.
The AML (Anti-Money Laundering) compliance and risk management in this field was addressed by Singh (2003), Apreda (2006) Singh (2003) and Apreda (2006), Lagzdins and Sloka (2012) emphatically point out that the AML compliance represents a powerful tool for the improvement of corporate governance in the banks. According to Singh (2003), the enhanced interest to promotion of the efficient corporate governance is further boosted by the concerns of the international financial community about the use of the banking system as a channel for criminal money laundering and terrorism financing. However, as it follows from Yeoh (2019), the efficient compliance with regulatory requirements by banks for the purpose of money laundering/terrorism financing (ML/TF) prevention is still hindered by the competitive pressure, shareholder income imperatives, incentives for the aggressive profitability growth and the growing prices on the shares.
To identify the bottlenecks in the compliance function implementation for the purpose of supporting the anti-money laundering program, Viritha et al. (2015) interviewed bank employees in India. The questions offered to the respondents covered the following areas: the employee's and the bank's profiles; countermeasures (anti-money laundering policy; customer identification procedures, risk assessment, and management; bank transfer management; transaction monitoring; reporting; updating of the KYC mechanisms); problems related to the anti-money laundering policy implementation (deficient resources, lack of customer support, and other restrictions to promote the anti-money laundering policy). Following the study, the authors rated the adaptability of the ML/ TF policies established by Indian banks as unsatisfactory. Arasa and Ottichilo (2015) studied factors influencing the compliance with the KYC requirements by Kenyan banks. Out of the factors with the statistical importance confirmed by the existing regressive model, the authors singled out: cus-tomer properties, bank size, staff competence, and the infrastructure of data communication technologies. The authors emphasized that the first two factors had the biggest impact on the compliance with the KYC requirements. Root (2019) emphasizes the necessity to identify root causes of the compliance violations taking into account the provisions of cognitive psychology, behavioral economics, and behavioral ethics. The author also refers to the multi-faceted nature of the cause of such discrepancies, such as the flawed implementation of the compliance function, difficulties associated with supervising the compliance programs, and the lack of the integrated compliance culture throughout the corporate structure.
According to the Guidelines to Sound Management of Risks Related to Money Laundering and Financing of Terrorism (BCBS, 2014), inadequate or improper ML/TF risk management (due to insufficiently operated risk management and control policies and procedures) provokes grave consequences for the banks. These consequences include, among others, the enforcement actions taken by regulators and damages incurred directly or indirectly by banks. All this requires a risk-oriented approach that serves to promote the development of the prevention and mitigation measures that are proportionate to the identified ML/TF risks, and the accessibility of all pertinent information about banks and compliance risks (FATF, 2014).
To achieve the effective compliance risk management of banks in the field of financial monitoring, researchers pointed out the need to promote the consistency between the compliance with anti-money laundering requirements, general customer relations, and the structure of internal control over business areas (Raghavan, 2006); integrate the process of compliance with anti-money laundering rules into the corporate social responsibility ; introduce the uncertainty-based approach as an alternative to the risk-based approach (Bello & Harvey, 2017); integrate the internal control systems, risk management, personnel management, and computerization of systems and processes related to compliance (Haddad, 2016); develop a compliance risk assessment program while considering potential financial losses, regulatory impact and negative impact on reputation (Nicolas & May, 2017); au-tomate the collation of data for generation of compliance risks reporting (Ludwick, 2006); and combine the automated machine learning and big data analysis algorithms to detect fraudulent schemes (Kolodiziev et al., 2020).
Therefore, based on the analysis of the academic literature, it can be concluded that modern researchers mostly focus on general recommendations regarding the implementation of the efficient compliance functions by bank, particularly, the AML compliance. In addition, considering the regulatory compliance and improvement of risk management in the field of pension programs is well-timed and important ( It is important to understand that AML threats identification, the objective assessment thereof, and forecasting of potential consequences are fundamental for the development of the operational algorithm applicable to: taking measures to promote the prevention and to counter further violations of regulatory requirements in financial monitoring; the elimination of these consequences and support of enhanced and efficient functioning of the risk management system in custodian banks; and preventing the involvement of pension assets in criminal money laundering schemes. This sets the focus on the search for tools in the field of identification, measurement, and modeling of banks' compliance risks in financial monitoring, as well as represents an extremely important aspect for banks (pension custodians), their regulators, and other interested parties to the nonstate pension system where it applies to establishing the feasibility vector while identifying the relations and threats to their development.
This aim of this paper is to form a toolkit for identifying volatility of compliance risks faced by banks (pension custodians) in the field of financial monitoring, based on causal modeling (cognitive map).

RESULTS
This study considers three key criteria defining the acceptance of pension custodian banks (meaning their reliability as counterparties), namely, compliance risks ( To assess the compliance and variability of the acceptance criteria applicable to pension custodian banks affected by violations in financial monitoring and the enforcement measures, the supervision of the bank's activities, and the likelihood of the network schemes by customers, the causal approach and cognitive modeling method were applied. The choice of the cognitive modeling method was dictated by the need to: • formalize and define the structural interaction of banking compliance criteria, actions taken by a regulator, likelihood of network schemes by market participants, and the quality of the intrabank compliance policy, as presented by the cognitive map concepts; and • generate the potential scenarios for the modification of key compliance criteria used by pension custodian banks due to changes in the perspective involvement of banking institutions in the network schemes, the need to enhance the intrabank compliance, changes in the Central Banks' policies and the banking institutions' performance. The cognitive modeling method is based on the construction of a fuzzy cognitive map. It is suggested to use the following fuzzy cognitive maps presented in Silov (1995) in order to build a cognitive map of the acceptance criterion variability applied to pension custodian banks affected by violations and measures taken in financial monitoring, their performance supervision and the likelihood of network schemes by customers: , , , FCM C F W = 〈〉 (1) where С = {C i } -multitude (of concepts); F = {F i }multitude of links between the concepts; W = {W ij } -multitude of arc weights (links).
To achieve the objectives, a set of 32 indicators was generated to characterize the banking institutions' compliance criteria, their variability, and the resulting volatility, which would serve as an analytical basis for the conduct of cognitive modeling and, consequently, development of the toolkit (Table 1).
To determine the compliance criteria variability for pension custodians, taking into account the impact of violations and influence in financial monitoring, and to promote the supervision of banks' performance through the enforcement of mandatory prudential ratios, and the likelihood of network schemes by customers, it is required to calculate key system indicators of a cognitive map, namely, consonance (Outdegree), dissonance (Indegree), and the impact of concepts on the system (Centrality). These indicators are determined when comparing the contours on the concept maps in accordance with the compliance criterion, balance, and the extent of impact (Silov, 1995).
A consonance is the function that takes into account positive and negative impact and links between all research process components. The high-er the value, the higher the validity of the impact indicator. A dissonance reflects the extent of distrust in the impact results.
In turn, the impact on the system exerted by the concepts reflects the maximum positive or ne-  Note: Violations committed by banks in financial monitoring that proved to be of little importance in the course of the study include failure to apply the proper procedure of data submission to the competent agency (C5); failure to apply financial transaction suspension procedures (C7); improperly performed obligation to develop, enforce, and update the bank's bylaws pertaining to financial monitoring on a regular basis in accordance with the applicable laws (C8); operating software that does not automatically detect and suspend financial transactions before it has been completed in cases envisaged by the applicable laws (C10); failure to apply the proper procedure to record financial transactions (C11).
gative impact of violations and the appropriate measures in the field of financial monitoring, the Central Bank's regulations, the level of the internal compliance established by a banking institution and the likelihood of network schemes on the criteria of banks' conformity to the interested parties' expectations in the context of implementing the non-state pension system.
Based on the calculated values of the relevant system indicators and the matrix of mutual influence between the concepts of a non-linear cognitive map, one would proceed with impulse modeling (using the Mental Modeler software) and implement variability scenarios for the compliance criteria applicable to banks that act as pension custodians, factoring in the interested parties' expectations that result from the changes in the measures of financial monitoring, compliance, banking regulation, and supervision.
The cognitive modeling was based on the statistical reporting of the National Bank of Ukraine (NBU) and performance indicators of ten systemically important banks of Ukraine, to which influence measures were applied for identified violations in the financial monitoring sphere during 2018-2020 (NBU, 2018-2020).
The resulting fuzzy cognitive map (see Appendix A) represents a mutual relationship between the variability of compliance criteria applicable to banks acting as pension custodians (levels of compliance risks in the financial monitoring sphere, financial and reputational risks) impacted by financial monitoring, supervised by the National Bank of Ukraine and facing the likelihood of the network schemes by bank customers.
The parameters that are construed to be the key concept of the cognitive model were calculated as follows: 1) the compliance risk assessment is based on applying the methodology to build an integrated indicator presented in Chmutova et al. (2020); 2) the reputational risk assessment relies on the average values of three ratings (mentioned below) that form the reputation of banking institutions. Based on the criteria of publicity, ac-cessibility, awareness, objectivity, and locality, the following factors have been chosen to assess the banks' reliability: • the bank soundness rating of the Ministry of Finance of Ukraine (where the key components include stress resistance, loyalty of depositors, expert opinion of the analysts, ranking by retail deposits); • the financial health rating developed and introduced by the International Center for Policy Studies supported by the Independent Association of the Banks of Ukraine (based on the reliability sub-index, containing the criteria that characterize the degree of the bank's compliance with the key prudential ratios; sub-index of efficiency, namely, the criteria reflecting the bank's ability to generate profit and the efficient use of its assets); • the bank viability rating by the business portal of Ukraine (based on the values of instant liquidity, size of net assets, and the amount of impaired loans); 3) the assessment of the financial risk by building an integrated indicator containing the values for capital adequacy, overall liquidity, and liquidity coverage for all currencies, as well as credit risk ratios, investment ratios, and the net foreign exchange position limit.
According to Appendix A, the fuzzy cognitive map of the acceptance criterion variability applied to the banks acting as pension custodians, taking into account the impact factors, combines 32 components, including: 10 driver concepts characterizing the impact on other concepts but giving no evidence of being affected by any of these concepts; 22 ordinary concepts that are regular concepts influencing and being influenced by the system components. To formalize the dependence of the acceptance criteria applied to the banks acting as pension custodians, as well as violations and influence measures in financial monitoring, the probable existence of the network schemes and prudential ratios implemented by the National Bank of Ukraine, the influence strength is determined taking into account the correlation analysis (Appendix B).
The linguistic-numerical scale (Saliieva & Yaremchuk, 2019) helps identify the relationship between the key impact measures in financial monitoring and key criteria of banking institutions ranging from medium (0.35 -0.6] to strong (0.6 -0.85]. Based on the correlation analysis (see Appendix B) and the linguistic construction, the key relationships were formalized and identified (weak, moderate, and strong).
The following analyses were conducted using the matrix of concepts that comprises a non-linear cognitive map of the transitively looped cognitive matrix. These include: 1) the structural analysis of the cognitive map showing the acceptance criterion variability applicable to pension custodians, taking into account the impact exerted by financial monitoring measures and regulations of the National Bank of Ukraine in non-state pension provision and the likelihood of network scheming by the bank's customers; 2) the analysis of the structural importance of the cognitive map elements. The analysis yielded functional indices (Table 2), such as consonance (Outdegree), dissonance (Indegree), and impact of one factor on another (Centrality).
According to Table 2, the compliance risks in financial monitoring and the impact factors such as penalties charged resulting from the NBU's enforcement measures and warning letters represent a key compliance indicator of the bank. The components used to assess banks' financial risks that simultaneously belong to the compliance criteria applicable to banking institutions (mandatory prudential ratios) comprise a separate group of important impact factors, such as: the size, adequacy of regulatory and fixed capital, and exposure of banks to considerable credit risks. Improper assessment/revaluation of customer-related risks appear to be the most significant violation to the model.
Having considered the importance of the integrated system indicators (Table 2), the analysis of the variability of compliance indicators (compliance risks in financial monitoring, reputational and financial risks of banks) proceeded with the impulse approach to three scenarios (see Figures 1, 2  and 3).
Scenario 1 implies the increase of penalties by 0.1%; Scenario 2 -the reduction of compliance risk by 0.1%; Scenario 3 -the increase in the capitalization of a banking institution (i.e. the increasing regulatory capital) by 0.1%.
The study mathematically proves that (1) the bank's compliance risk in terms of financial monitoring is a key acceptability criterion applicable to pension custodians; and (2) changes in this indicator determine the variability of the bank's reputational risk and its financial parameters.

DISCUSSION
The study findings lead to the following conclusion. According to Scenario 1 of impulse modeling (Figure 1), an increase in the size of penalties would bring down the compliance risk by 0.03% and reduce the reputational risk, which, in turn, would reduce the number of violations in financial monitoring, specifically reducing the likelihood of improper assessment/revaluation of customer-related risks (C4) by 0.01%. Given that the amount of penalties is the resulting factor of the influence, there will be a simultaneous growth in the number of penalties (Y3) for the following violations of the applicable laws by banks acting as pension custodians in financial monitoring: violations related to PEPs (C1); failure to detect financial transactions that are subject to mandatory financial monitoring (C2); failure to comply with the requirements regarding the identification, verification, and study of the bank's customers (C3); conduct of risky activities in financial monitoring (C6); delayed provision of information requested by the NBU and failure to provide complete and accurate informa- tion in the relevant reports to the NBU (C9); and the bank's failure to discontinue customer service in cases envisaged by the applicable laws regulating the financial monitoring matters (С12).
According to Scenario 2 (see Figure 2), the reduction in the compliance risk indicators for banks acting as pension custodians in the context of financial monitoring will promote the improvement of their reputation in the non-state pension system and the need to increase the bank's regulatory capital by 0.01%, as the number of violations related to servicing PEPs (C1) will grow by 0.03%; failure to detect financial transactions that are subject to mandatory financial monitoring (C2)by 0.01%; and risky activities in financial monitoring (C6) -by 0.02%. Increases in the authorized capital to comply with the Central Bank's regulatory requirements often acquire a manner other than cash investments made by stakeholders. The increase in capital occurs due to the debt conversion to the parent structure, which may catalyze violations in the field of financial monitoring.
An increase in the authorized capital by 0.1% (Scenario 3, Figure 3) will boost the reputation of pension custodians by 0.01% and reduce their compliance risk by 0.03%. Changes in the field of potential violations follow the same tendencies as variation in the amount of penalties.
This study offers a completed cognitive model, and the results of impulse modeling should be integrated in the risk profile of banks acting as subjects of primary financial monitoring, which would promote further informing of their counterparties on the matters of non-state pension provision in the context of the probable involvement of pension assets in criminal schemes and potential threats to further interaction with custodian banks.
Therefore, in contrast to the existing recommendations to banks regarding their risk profiles (National Bank of Ukraine, 2019), i.e. in addition to factoring in the data on their business risks, structural risks, organization, and efficiency of the intrabank system, other business parameters that can serve as markers of potential threats to the interested parties, specifically on the matters of non-state pension provision, should also be integrated into the risk profile. This will help formulate recommendations regarding the procedure of risk profile building for the bank (pension custodians), taking into account the need to expand its components, which is implemented through: • analyzing and assessing the business risk pertinent to a bank on an overall basis. This block covers the parameters of the bank's business model: the nature of the bank's products and services, sales channels, customer portfolio, availability of PEPs, geographical location and the "geography" of transactions; • analyzing and assessing the structural risk pertinent to the bank on an overall basis. This block covers the aspects of the ownership/capital structure, asset size, and corporate governance mechanism; • analyzing and assessing the organization and efficiency of the intrabank control system on an overall basis. This block covers the aspects of the quality risk management and functioning of the internal control system; • analyzing the probability of changes (volatility) in the compliance risk in financial monitoring and consequences of its actualization. This block covers the aspects of implementing the financial monitoring, supervision of the bank's performance and the likelihood of network schemes by customers; it further defines the ability to forecast not only compliance risks but also financial and reputational risks, which are the main criteria for the acceptability of banking institutions by their counterparties in the field of non-state pension, subject to the influence of the analyzed factors.

CONCLUSION
This study outlines a toolkit to help identify the volatility of compliance risks faced by banks acting as pension custodians through causal modeling.
Within the framework of the developed toolkit, the compliance risks pertinent to banks (pension custodians) in the field of financial monitoring, along with reputational and financial risks, were considered as the criteria for the conformity of banks to the counterparties' expectations in the context of non-state pension provision. The method of cognitive modeling (that implies structuring of the cause-and-effect relationship) allowed building a fuzzy cognitive model of the relationship between the relevant impact factors and implement three predictive scenarios.
According to Scenario 1 test results (increasing the amount of penalties against the banks for the violations of applicable laws in the field of financial monitoring by 0.1%), the increased amount of penalties will: reduce the compliance and reputational risks of pension custodians by 0.03%; reduce the number of their violations in financial monitoring while specifically reducing the likelihood of improper assessment/revaluation of the customer risks by 0.01%.
According to Scenario 2, compliance risks of banks acting as pension custodians reduced by 0.1% will minimize reputational risks and increase the banks' regulatory capital by 0.01%, along with the increase in the number of violations (by 0.03%, 0.01% and 0.02% respectively) in connection with: 1) servicing of PEPs; 2) failure to identify financial transactions that are subject to mandatory financial monitoring; and 3) risky activities carried out in the financial monitoring sphere.
According to Scenario 3 (increased capitalization of a banking institution through the increase in regulatory capital by 0.1%), banks acting as pension custodians will strengthen their reputation by 0.01% and reduce their compliance risks by 0.03%. The trends in the field of potential violations are similar to those in the first scenario tested.
Therefore, according to the results of applying the toolkit developed to identify the volatility of compliance risks pertinent to banks acting as pension custodians, (1) compliance risks in financial monitoring are indeed the key criterion for determining the acceptability of banks in the non-state pension sphere, and the volatility of compliance risks causes variability in reputational risks pertinent to banking institutions and their financial parameters. To this effect, reduced compliance risks of custodian banks will contribute to strengthening their reputation and boost their regulatory capital, while simultaneously increasing the likelihood of individual violations in the field of financial monitoring. And, secondly, compared to the specific features inherent in the supervision of banks acting as pension custodians, the likelihood of network schemes by their customers, increased enforcement measures in financial monitoring would further increase the volatility of compliance risks for the banks.
The results of this study, obtained using the developed tools, are proposed for integration into the risk profiles of banks acting as pension custodians. In general, they provide an important basis for further empirical research in the understudied context of anti-money laundering compliance risk management, expanding and improving the design of banks' compliance systems using the obtained cognitive model (forecasting the probability of a risk event and reputational and financial damage).