INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 06,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 1446
INTERNATIONAL EXPERIMENTS IN OPERATIONAL RISK ASSESSMENT IN
INVESTMENT ACTIVITIES OF COMMERCIAL BANKS
G’ulomova Kamolaxon Anvarxon kizi
Teacher of the Department “Evaluation work and investments”
Abstract:
Operational risk remains a significant concern in the investment operations of
commercial banks globally. This paper explores international experiments and methodologies
in operational risk assessment related to investment activities. By analyzing case studies from
leading economies and financial institutions, we identify emerging best practices and challenges.
The findings highlight the growing importance of integrated frameworks that combine
regulatory compliance, data analytics, and ESG considerations in modern risk management
strategies.
Keywords:
operational risk, investment activities, commercial banks, Basel III, risk assessment
models, international banking, ESG risk, AI in banking.
INTRODUCTION
Operational risk in commercial banking, especially in investment activities, has become
increasingly complex due to globalization, technological advancements, and regulatory shifts.
Unlike credit or market risk, operational risk originates from internal processes, people, systems,
or external events. These risks can result in significant financial losses and reputational damage.
Internationally, banks and regulators have been experimenting with frameworks to assess and
manage operational risk, particularly in investment domains where risk exposure is multifaceted.
This study investigates the nature and outcomes of such experiments in various jurisdictions.
The goal is to understand how commercial banks worldwide are adapting their operational risk
assessment mechanisms in investment contexts, and what lessons can be drawn from
international experiences.
REVIEW OF LITERATURE
Operational risk has gained significant academic and regulatory attention since the early 2000s,
especially following high-profile banking failures attributed to internal process failures,
misconduct, and IT breakdowns. The evolution of operational risk management (ORM)
frameworks has been driven by regulatory reforms, particularly from the Basel Committee on
Banking Supervision, as well as technological advances in data analytics and financial
modeling.
According to Cruz (2002), operational risk differs from credit and market risk in its origin—it
stems from inadequate or failed internal processes, people, systems, or external events. The
Basel II and Basel III accords provided formal definitions and capital requirements for
operational risk, pushing banks to develop more robust assessment tools. Alexander (2003)
emphasized the need for qualitative as well as quantitative approaches to capture low-frequency,
high-impact risk events in investment operations.
Studies by Moosa (2007) and Jobst (2010) discuss the adoption of Advanced Measurement
Approaches (AMA), which allowed banks to build internal models based on loss event data,
scenario analysis, and business environment indicators. However, the complexity and lack of
comparability led to a shift toward the Standardized Measurement Approach (SMA) under
Basel III (BCBS, 2017), which sought to streamline operational risk capital requirements
globally.
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 06,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 1447
Recent literature (Doff, 2020; KPMG, 2022) explores how banks are integrating machine
learning and artificial intelligence to improve the predictive accuracy of operational risk models,
especially in high-volume investment transactions. These tools enable real-time monitoring of
system performance, trade anomalies, and regulatory compliance.
Comparative studies reveal distinct national approaches to operational risk management in
investment activities. For instance, Llewellyn (2018) compared the U.S. and European models,
noting that U.S. banks tend to rely more on quantitative modeling, whereas European banks
incorporate broader governance and ethical considerations. A study by Tanaka and Kim (2021)
examined East Asian banks, finding that operational risk assessment in investment activities
often emphasizes cultural compliance, regulatory adherence, and internal audit mechanisms.
Emerging literature connects ESG (Environmental, Social, Governance) risks to operational
risk categories, especially in investment banking. According to reports by the OECD (2021)
and McKinsey & Company (2023), poor ESG integration can lead to reputational damage, legal
challenges, and compliance breaches—each of which constitutes operational risk. Studies such
as those by Bianchini et al. (2022) propose embedding ESG metrics within operational risk
dashboards used by banks' investment divisions.
The role of fintech, blockchain, and regulatory technology (RegTech) is increasingly prominent
in recent literature. Research by Arner et al. (2017) highlights the potential of blockchain to
reduce operational risk in investment transactions by increasing transparency, auditability, and
efficiency. Meanwhile, PwC (2022) underscores the use of cloud-based risk management
platforms in integrating data across investment units.
While substantial literature exists on operational risk management, fewer studies focus
specifically on its application within investment activities of commercial banks. Moreover,
there is a need for cross-country empirical comparisons and a deeper understanding of how
emerging technologies and ESG criteria are reshaping operational risk frameworks globally.
RESEARCH METHODOLOGY
This research is based on a qualitative comparative analysis of international case studies and
regulatory reports from 2015 to 2024. Data sources include:
reports from the basel committee on banking supervision (bcbs);
case studies from commercial banks in the eu, united states, and east asia;
peer-reviewed journal articles on operational risk in banking;
internal risk assessment methodologies disclosed in financial statements.
Each case was analyzed based on three dimensions:
the operational risk assessment model used (e.g., ama, standardized approach);
integration of investment-specific risks (e.g., trading desk errors, compliance failures);
role of regulatory frameworks and technological tools (ai, blockchain, etc.).
ANALYSIS AND RESULTS
Europe: Scenario Analysis and Internal Models.
European banks, under the Capital
Requirements Directive (CRD IV), have implemented advanced measurement approaches
(AMA) that incorporate scenario analysis and loss event data. Banks like Deutsche Bank and
BNP Paribas have piloted integrated platforms to simulate investment risks related to
algorithmic trading and cross-border asset flows. In the U.S., operational risk assessment in
investment activities is heavily data-oriented, encouraged by both regulatory guidance and
industry innovation.
JPMorgan Chase uses real-time surveillance tools to monitor investment activities,
focusing on algorithmic trading errors and unauthorized transactions.
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 06,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 1448
Citibank integrates AI and machine learning into its risk analytics platforms to
predict and prevent operational disruptions in portfolio management and trade execution.
The Federal Reserve supports innovation under regulatory sandboxes, allowing
controlled experimentation with risk modeling.
Strong use of AI to preempt operational errors in investment banking, supported by robust
internal audit systems.
United States: Emphasis on Quantitative Tools.
U.S. commercial banks, such as JPMorgan
Chase and Citibank, focus on quantifying operational risk through large-scale data analytics.
The Federal Reserve encourages the use of machine learning tools to detect anomalies in
investment operations, such as unauthorized trades or mispricing of complex derivatives.
In the United States, operational risk assessment within investment activities of commercial
banks is heavily characterized by the use of advanced quantitative techniques and real-time data
analysis. Regulatory authorities, particularly the Federal Reserve and the Office of the
Comptroller of the Currency (OCC), promote the integration of data-driven approaches into risk
management frameworks.
Leading financial institutions, including JPMorgan Chase, Citibank, and Bank of America, have
developed proprietary systems that combine big data analytics, predictive modeling, and
artificial intelligence (AI) to measure and mitigate operational risks in investment processes.
Key components of these systems include:
Anomaly detection algorithms used to identify irregularities in high-frequency trading
platforms, such as unauthorized trades or abnormal market behavior.
Model-based risk scoring of operational failures in derivative pricing and settlement
processes.
Automated compliance engines that flag potential violations of investment-related
regulations (e.g., Volcker Rule compliance in proprietary trading).
The Federal Reserve has encouraged the responsible use of machine learning models,
particularly in monitoring complex products like collateralized debt obligations (CDOs) and
exotic derivatives. Additionally, regulatory sandboxes allow banks to pilot these tools under
supervised conditions.
A strong reliance on real-time data and AI-based quantitative tools to proactively detect and
manage operational risks inherent in investment banking, supported by both institutional
innovation and federal regulatory alignment.
East Asia: Regulatory and Cultural Adaptation.
In Japan and South Korea, operational risk
models have been adapted to align with local regulatory expectations. Banks focus on internal
controls, often emphasizing staff training and culture-based risk awareness. Investment risk is
monitored closely via strict post-trade reconciliation processes and digital audit trails.
Japanese commercial banks take a conservative and culturally embedded approach to
operational risk in investment activities.
Mitsubishi UFJ Financial Group emphasizes internal compliance, detailed
documentation, and a low-risk investment culture.
Post-trade verification processes and manual reconciliation remain strong, especially
in foreign investment portfolios and syndicated loans.
High emphasis on internal compliance and personnel reliability in risk management.
South Korea's financial institutions are advancing rapidly in operational risk assessment, aided
by strong digital infrastructure.
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 06,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 1449
KB Financial Group and Shinhan Bank have implemented real-time monitoring
systems to detect anomalies in investment processes, particularly in cross-border digital
transactions.
The Financial Services Commission (FSC) mandates detailed reporting of operational
failures, especially in fintech-based investment products.
Digital-first monitoring combined with proactive regulatory enforcement.
The reviewed experiments reveal several key trends:
Shift toward Standardized Measurement Approaches (SMA): Following Basel III
reforms, many banks are moving away from AMA to SMA, which standardizes operational risk
capital requirements. While this simplifies compliance, it may underrepresent risks specific to
investment operations.
Role of Technology: Artificial intelligence and machine learning are transforming
operational risk detection. Technologies such as blockchain enhance transparency in investment
transactions, reducing fraud and error rates.
Integration of ESG and Cyber Risk: New international models increasingly integrate
ESG-related operational risks and cyber threats. For example, investment in high-emission
industries may pose reputational risks, which are now being quantified as part of operational
risk frameworks.
Regulatory Coordination: International convergence in operational risk assessment is
increasing but remains uneven. Harmonization efforts, particularly under the Basel framework,
are essential to ensuring consistency across borders.
CONCLUSION
International experiments in operational risk assessment have significantly contributed to
strengthening the resilience and adaptability of commercial banks’ investment activities. Across
jurisdictions, banks have implemented diverse strategies—from advanced analytics and AI-
driven monitoring in the United States to scenario-based frameworks in Europe and culturally
rooted risk controls in East Asia. These experiments have not only improved the detection and
mitigation of operational risks but have also supported banks in navigating increasingly
complex financial instruments and regulatory landscapes.
Despite these advancements, several persistent challenges remain. One of the most pressing is
the difficulty of adapting global risk models to national and institutional contexts, where
differences in regulatory expectations, market structures, and risk cultures can hinder the
implementation of standardized frameworks. Furthermore, the dynamic nature of investment-
related operational risk—driven by innovations in financial technology, growing exposure to
cyber threats, and rising stakeholder expectations around ESG performance—demands more
agile and integrated risk management systems.
Additionally, while the Basel III reforms have brought greater consistency to operational risk
capital requirements, many banks still struggle to balance regulatory compliance with
operational efficiency and innovation. Overly rigid frameworks may inhibit banks from
experimenting with new investment models or technologies, particularly in markets with fast-
paced development.
Looking forward, the future of operational risk assessment in investment banking lies in the
development of dynamic, real-time, and data-rich models. These must be capable of identifying
and responding to both traditional and emerging risk drivers—including those related to
environmental sustainability, social accountability, and governance ethics. Enhanced
collaboration between regulators, financial institutions, and technology providers will be
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 06,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 1450
essential to designing systems that are not only compliant but also anticipatory, adaptive, and
transparent.
In conclusion, international experimentation in operational risk assessment represents a critical
step toward a more secure and responsible investment banking environment. However, the
evolution of these frameworks must remain ongoing—continuously learning from cross-border
experiences, embracing technological advancement, and aligning with broader societal goals to
manage operational risk in an increasingly interconnected global economy.
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