Technology
Risk Management and Digital Disruption in the Modern Financial Market
Global Banking & Finance Review®
Company
By Nuruddin Sheikh
Risk management remains an essential part of open-market trading. It involves a robust system of checks and balances that upholds market integrity and protects the global economy. Today’s market participants demand new instruments and derivatives for trading—all at a low cost with efficient liquidity utilization. These nuances will introduce new dimensions and complexities to provide risk-management solutions. As the next wave of digital disruptions impacts technological advancements related to the trading of financial assets, buyers and sellers will face new challenges. As a result, it is critical for next-generation risk management solutions to incorporate market innovations to increase investor confidence in capital liquidity while promoting financial stability with portfolio-based risk management.
Advancement of financial technologies
First, the advent of algorithmic trading via artificial intelligence (AI) and machine learning (ML) has all but removed human involvement in the completion of trades. Professionals, of course, have a role in creating and fine-tuning algorithms, but once correctly equipped, the programs can maximize profits with efficiency and frequency unattainable to humans. For example, algorithm creation allows a program to purchase several hundred shares of a stock based on a moving average or other calculated features. The program monitors prices and executes the desired transaction when the price meets the determined conditions.
Second, while blockchain technology is seeing increased association with the cryptocurrency markets, decentralized finance (DeFi) also ensures its enduring place in the traditional markets. Under blockchain, transactions are instantly recorded and viewable to all interested parties, eliminating the need for independent monitoring and guaranteeing collective control. Despite the increasing variety of potential uses of blockchain, its original intent to track, maintain, and display transactions continues to be the primary factor in the technology’s popularity and relevance in today’s markets.
Lastly, quantum computing reshapes risk mitigation for data-driven models within financial powerhouses like JPMorgan Chase. Researchers examined the ability of quantum computers to enhance deep hedging as a risk management strategy. They found the machines enabled frameworks to add to their existing skill sets by increasing the programs’ learning efficiency in predicting expected returns. In layman’s terms, as computing technology becomes more sophisticated and powerful, so will the ability of algorithms to deliver increased returns.
Impacts of emerging advancements in risk management and fraud detection
Buyers and sellers rely on each other for contractual commitments, but in a fast-paced environment, central counterparty clearing houses (CCP) function as both to mitigate the risk. The clearinghouse mandates collateral for each participant (buyer and seller), which is calculated dynamically based on the risk profile and market conditions. Such calculations need to be adjusted in real time, incorporating evolving market parameters to maintain integrity and control risk while providing efficient liquidity demand. DeFi and blockchain systems can enable CCP to maintain transparency, reduce audit time, and discover transaction anomalies by adopting smart contracts to store and execute financial assets.
Enhanced efficiency requires new and sophisticated means of fraud prevention and mitigation. Late in 2024, NASDAQ announced the development of an advanced ML-based approach to risk determination and predictive analytics with the ability to process complex products up to 100 times faster with no loss of accuracy, significantly reducing infrastructure required to run those calculations. From a more general perspective, a recent report found that AI had the following statistical impacts on trading and finance:
Improved fraud detection. AI-powered fraud detection systems are 95 percent accurate, compared to just 70 percent for traditional methods.
Specific impacts in major industries.Financial services organizations reported a 32 percent drop in credit card fraud after adopting AI solutions, while insurance companies reported the ability to flag 50 percent more fraudulent claims utilizing the tools.
Increased adoption.Most banks (91 percent) say they will be utilizing AI-driven fraud protection by the end of this year, and the overall market for AI in fraud protection is projected to nearly triple by 2030.
Portfolio-based risk management
The word ‘disruption’ may carry negative connotations, but digital disruption has made meaningful, positive contributions to finance and trading. Portfolio-based approaches to risk management are one prominent example. By moving beyond a traditional, singular asset-based assessment and determining a risk profile by viewing the entirety of a portfolio, investors and managers can properly diversify investments while monitoring new, emerging risks in real time.
A traditional value at risk (VaR) approach looks at the possibility of financial losses within a firm, corporation, or individual’s portfolio over a specified time frame. One advantage of this strategy is ease of understanding.VaR is expressed as a single number or percentage, allowing for straightforward interpretation by consumers and professionals alike. Another benefit is widespread popularity.Due to its ubiquity, VaR is often calculated automatically through Bloomberg terminals and other financial software.
The most significant disadvantage of the VaR approach is the inability to develop a means of evaluating widespread risk over an entire asset class or portfolio. Using risk-based statistics that only account for typical distributions, probabilities, or periods of low volatility may underestimate or ignore the potential for tail-risk or black swan events. The most prominent example is the financial crisis of 2008 when VaR models failed to account for the extreme market disruption and specifically could not capture the systemic risk presented by subprime mortgages. The severity of this crisis set the stage for the current model, where VaR is just one factor in a set of risk measurements rather than the primary or sole determining method.
Meanwhile, portfolio risk models incorporate stress testing and tail-risk hedging by auto-balancing portfolios based on changing risk profiles. When credit default swaps for mortgages show signs of distress, portfolio-based models switch to higher allocations of government bonds or similar stable asset classes.
That’s not to say VaR lost its relevance in the financial markets risk management practices. The Archegos Capital collapse was a prime example of what can happen when high exposure to risky derivative trades doesn’t properly account for liquidity considerations—an ability that traditional VaR lacks. As a result, portfolio-based approaches began incorporating massive market depth analysis and liquidity-adjusted VaR (LVaR) strategies that work by adjusting traditional VaR by adding the costs of unwinding or liquifying positions within the market.
The benefits of portfolio-based risk management are apparent. Goldman Sachs successfully limited the loss with a sophisticated portfolio-based risk management strategy, while competitors heavily relying only on VaR lost over $100 billion USD with the massive subprime mortgage meltdown in 2007. Today, Goldman Sachs continues to lead the fast-paced risk management solutions with quantum computing research. One example with impressive results is completing shallow Monte Carlo risk simulations in mere minutes instead of hours, which can unlock near-real-time risk calculation for high-frequency trading portfolios. Meanwhile, London Clearing House (LCH), a provider of robust portfolio-based risk management system, efficiently managed portfolio risk, including a Lehman Brothers’ default of a $9 trillion USD portfolio using Lehman Brothers’ initial and variation margins (IM and VM) across all assets held at LCH.
Controversy and ethical concerns within the new financial system
In addition to the myriad benefits of these evolving approaches, there are risks and controversies. Experts attribute most of them to the relative newness of these entities. AI bias is always a concern, with discriminatory lending practices or trading decisions the most concerning outcome. DeFi platforms lack the regulatory oversight enjoyed by traditional models, leading to vulnerabilities to market manipulation and arguments from some financial institutions that such platforms are increasing risk. WhileFTX crypto exchangeexploited the decentralized blockchain technology to bypass the risk alarm, automated portfolio-based risk management by an independent CCP could have limited the damage.
Quantum computing breaks current encryption standards, leading to cybersecurity threats for unprepared institutions. At the same time, real-time tracking of risk raises privacy concerns for institutional and individual investors and ethical concerns in customer profiling and credit scoring. Most factors are symptomatic of technology moving and advancing at a rate that makes it challenging for regulatory bodies to adapt. Still, the success of these measures in providing an optimal environment for real-time financial trading makes adaptation the highest priority.
The continuing evolution of financial markets necessitates leading organizations to keep pace with emerging technology, evolving threats, and regulatory compliance. Organizations can stay ahead in an ever-changing, increasingly digital landscape by replacing static, historical data management methods with self-learning ML models that can anticipate and predict trends, concerns, and market direction with increasing accuracy.
About the Author:
Nuruddin Sheikh is a software performance architect with over 20 years of experience leading cloud and big data transformations in ML-driven content recommendation, virtual collaboration, software-defined infrastructure, and enterprise security. He has spearheaded strategic initiatives at Fortune 500 companies, driving innovations in search, e-commerce, digital conferencing, cybersecurity, and fintech. His expertise spans performance engineering, cloud computing, machine learning, and security, with a focus on designing large-scale, low-latency, and secure enterprise architectures. Nuruddin holds a master’s degree in software systems. Connect with him on LinkedIn.
Image : Nuruddin Sheikh
Risk management involves identifying, assessing, and prioritizing risks followed by coordinated efforts to minimize, monitor, and control the probability or impact of unfortunate events.
Algorithmic trading uses computer algorithms to automate trading decisions based on predefined criteria, allowing for faster and more efficient trading in financial markets.
Portfolio management is the process of managing a collection of investments, balancing risk and return according to an investor's goals and risk tolerance.
Fraud detection refers to the process of identifying and preventing fraudulent activities, often using advanced technologies like machine learning and data analytics.
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