INTERNATIONAL MULTIDISCIPLINARY JOURNAL FOR
RESEARCH & DEVELOPMENT
SJIF 2019: 5.222 2020: 5.552 2021: 5.637 2022:5.479 2023:6.563 2024: 7,805
eISSN :2394-6334 https://www.ijmrd.in/index.php/imjrd Volume 12, issue 02 (2025)
83
REVOLUTIONIZING RISK MANAGEMENT: INNOVATIVE APPROACHES IN
COMMERCIAL BANKING
Yuldasheva Shahnoza Baxrom kizi
Master's student at the Higher School of Business and Entrepreneurship
Annotation:
This article explores innovative approaches to risk management in commercial
banks, focusing on the integration of advanced technologies, such as Artificial Intelligence (AI),
Blockchain, and Machine Learning (ML), as well as new methodologies like stress testing and
RegTech. It discusses the benefits of these innovations in enhancing risk prediction, compliance,
fraud detection, and overall financial security. Additionally, it highlights the importance of data-
driven decision.
Key Words:
risk management
,
artificial intelligence (AI)
,
machine learning (ML)
,
blockchain
,
stress testing
,
regulatory technology (regtech)
,
fraud detection
,
credit risk
,
data analytics
,
compliance.
Introduction.
Risk management has always been at the core of operations in commercial banks.
However, the evolving financial landscape—shaped by rapid technological advancements,
globalization, and regulatory changes—has prompted the need for innovative approaches. In
recent years, the banking sector has embraced new tools and strategies to mitigate risks more
effectively and efficiently.
As traditional risk management techniques (e.g., manual credit scoring, simple portfolio
diversification) evolve, modern technologies such as Artificial Intelligence (AI), Machine
Learning (ML), and Blockchain are revolutionizing how banks approach risk mitigation. By
leveraging these tools, financial institutions can detect fraud, assess credit risk more accurately,
conduct stress testing under a variety of scenarios, and streamline compliance processes. In this
article, we will examine these innovations in detail and assess their impact on commercial
banking.
1. Artificial Intelligence (AI) and Machine Learning (ML) in Risk Management.
AI and ML
have transformed traditional risk management models. Banks are increasingly relying on these
technologies to enhance their capabilities in fraud detection, credit scoring, and predictive
analytics.
Fraud Detection and Prevention: AI-powered systems analyze transaction patterns in real time,
identifying anomalies and preventing fraudulent activity. Machine learning algorithms improve
over time as they process more data, enhancing their ability to flag potential fraud.
Credit Risk Assessment: AI and ML enable more accurate credit scoring by evaluating a wider
range of data beyond traditional credit histories. This includes behavioral and transactional data,
allowing for more personalized and accurate risk assessments.
Statistical Data Example: A study by McKinsey & Company found that AI-based fraud detection
systems can reduce false positive rates by up to 50% compared to traditional rule-based systems.
2. Blockchain for Transparency and Security.
Blockchain technology, known for its secure and
decentralized nature, is another innovation transforming risk management in banking. By
providing transparent, immutable transaction records, it mitigates risks associated with fraud, data
breaches, and operational inefficiencies.
Enhanced Security: Blockchain provides a highly secure method of verifying transactions,
reducing the likelihood of fraud and counterparty risk.
INTERNATIONAL MULTIDISCIPLINARY JOURNAL FOR
RESEARCH & DEVELOPMENT
SJIF 2019: 5.222 2020: 5.552 2021: 5.637 2022:5.479 2023:6.563 2024: 7,805
eISSN :2394-6334 https://www.ijmrd.in/index.php/imjrd Volume 12, issue 02 (2025)
84
Smart Contracts: These self-executing contracts reduce operational risks by automating
transactions and ensuring that terms are met without human intervention. This also minimizes the
risk of errors and discrepancies in contract execution.
Statistical Data Example: According to PwC’s 2020 Global Blockchain Survey, 61% of financial
services organizations are already incorporating blockchain into their operations, with over 30%
using it for risk management purposes.
3. Stress Testing and Scenario Analysis.
Stress testing is essential for evaluating the resilience
of a bank’s portfolio under adverse conditions. Recent advancements have made stress testing
more dynamic, enabling more comprehensive risk management strategies.
Dynamic Stress Testing: With powerful data processing tools, banks can simulate various
economic and geopolitical scenarios to assess how their portfolios would perform under stress.
This helps identify vulnerabilities and prepare for worst-case situations.
Scenario Analysis with Big Data: By utilizing big data sources, banks can forecast potential risks
more accurately. For example, analyzing real-time market data, geopolitical developments, and
even social media sentiment can inform risk management decisions.
Statistical Data Example: In 2023, the European Central Bank (ECB) reported that 80% of EU
banks used dynamic stress testing models that integrate macroeconomic data, financial market
indicators, and historical crisis data for more comprehensive risk assessments.
4. RegTech for Enhanced Compliance.
Regulatory technology (RegTech) is another area where
innovation has streamlined risk management. With financial regulations becoming increasingly
complex, RegTech tools help banks automate compliance processes and mitigate legal and
reputational risks.
Automated Compliance Monitoring: RegTech platforms provide real-time monitoring for
compliance with local and international regulations, reducing the risk of penalties and operational
disruptions.
Anti-Money Laundering (AML) and KYC: RegTech tools are used to automate the process of
Know Your Customer (KYC) checks and Anti-Money Laundering (AML) monitoring, improving
accuracy and reducing manual errors.
Statistical Data Example: Deloitte reports that the use of RegTech tools can reduce compliance
costs by up to 30%, while also decreasing the time spent on regulatory reporting by as much as
50%.
5. Data-Driven Decision-Making.
The vast amounts of data generated by financial transactions
and market activity provide banks with valuable insights into potential risks. Banks are
increasingly adopting data-driven decision-making to improve the accuracy and timeliness of their
risk assessments.
Predictive Analytics: By using big data and machine learning models, banks can predict emerging
risks, such as loan defaults, market downturns, or liquidity shortages, and take proactive measures
to mitigate these risks.
Real-Time Dashboards: Data visualization tools enable banks to monitor their risk exposure in
real-time, ensuring they can quickly respond to shifts in market conditions or internal operations.
Statistical Data Example: A Gartner report suggests that 62% of banks that adopted data analytics
for risk management reported an improvement in their decision-making capabilities and risk
mitigation strategies.
Conclusion.
Innovative risk management approaches are fundamentally reshaping the way
commercial banks assess, manage, and mitigate risks. Technologies such as AI, ML, Blockchain,
and RegTech are providing banks with more accurate, real-time insights into their risk exposure,
enabling them to respond proactively to emerging threats.
INTERNATIONAL MULTIDISCIPLINARY JOURNAL FOR
RESEARCH & DEVELOPMENT
SJIF 2019: 5.222 2020: 5.552 2021: 5.637 2022:5.479 2023:6.563 2024: 7,805
eISSN :2394-6334 https://www.ijmrd.in/index.php/imjrd Volume 12, issue 02 (2025)
85
As the financial industry continues to evolve, the integration of these technologies will play a
pivotal role in enhancing the resilience and security of financial institutions. However, the
adoption of these innovations requires continuous investment in both technology and human
capital to ensure that banks are prepared for an increasingly complex risk environment.
The future of risk management lies in leveraging these innovations to not only safeguard against
risks but also drive growth and competitive advantage in an ever-changing global economy.
References:
1.
McKinsey & Company (2020): AI in fraud detection and its impact on reducing false
positives.
2.
PwC’s 2020 Global Blockchain Survey: The adoption of blockchain in financial services.
3.
European Central Bank (2023): Stress testing models in European banks.
4.
Deloitte (2021): The impact of RegTech on reducing compliance costs.
5.
Gartner (2022): The role of data analytics in risk management decision-making.
