The collapse of SVB has highlighted the need for better risk assessment for mid-sized banks. While there are strong controls for larger banks, smaller institutions are not required to undergo the same assessments that ensure the health of financial markets. These recent bank failures require new FDIC actions and potential regulatory responses for mid-sized banks with less than $250 billion in assets. The regulations that will require changes include capital and liquidity, TLAC, CCAR, and increased stress testing thresholds since banks under $250 billion are currently not required to stress test their portfolios.
SVB’s collapse was different from other financial recessions because social media played a significant role in its rapid decline. The speed at which customers were able to move their money due to real-time access and social media activity made it difficult for the bank’s leadership to respond adequately. As a result, firms must also look at their risk velocity very closely to mitigate any risks stemming from social media.
While financial recessions are cyclical, financial products are now more complex than ever. Coupled with the impact of social media sentiments, it is vital for firms to revisit their risk management strategy with a forward-looking vision and have a good data strategy. A solid data strategy empowers banks to leverage data effectively, drive growth, and navigate the ever evolving, complex and reactive financial landscape.
Data Challenges in the Financial Industry
Most financial firms face several data challenges that can impact their operations and risk management abilities. Some of the challenges are as follows:
Siloed Architecture: Most banks have organically grown their technology platforms to match their growing business needs. This has resulted in siloed architecture, where data is often stored and managed in separate systems or divisions, leading to fragmented data sets. This siloed approach hampers data integration, making it difficult to gain a holistic view of the bank’s operations and effectively manage risk.
Outdated Data Models: Due to issues such as organizational complexity, budget and resource constraints, banks often struggle with legacy systems and data models that are not equipped to handle the evolving complexities of the financial industry. Outdated data models may lack flexibility, scalability, and the ability to capture and analyze new types of data, hindering accurate and comprehensive risk assessments.
Inconsistent Reporting: Different departments within a bank may use different reporting formats, metrics, and definitions, for the same KPI’s, making it challenging to compare and consolidate data across the organization. Inconsistent reporting can lead to confusion, errors, and difficulties in identifying and addressing various risk metrics for a bank. Consistent and accurate reporting enables better decision-making, regulatory compliance, operational efficiency, and stakeholder trust.
Data Quality: Data quality poses significant challenges for banks when assessing risk. Incomplete, inaccurate, and inconsistent data can lead to faulty risk evaluations and misinformed decisions and incorrect mitigation strategies. Improving data quality requires robust data governance, data validation processes, data cleansing techniques, and regular monitoring to ensure accurate and reliable data for effective banking operations.
Predictive Analytics: Market volatility makes it difficult to predict future trends accurately. The biggest challenge that banks have with establishing predictive market sensing capabilities is the lack of data availability, quality, and privacy in accessing and utilizing relevant data. Complex and outdated models may also lack interpretability.
Key Themes and Imperatives for Banks in the Aftermath of SVB Failure
Recent SVB failure highlights two key themes: the importance of predictive market sensing capabilities and the need to swiftly adapt to regulatory changes, such as stress testing and liquidity management. Bank failures have a cascading effect on the industry, leading to imminent regulatory changes, particularly for mid-sized banks. Anticipated changes include increased stress testing thresholds and revised liquidity coverage ratio requirements. Banks must prepare to comply with these changes to manage risks effectively.
Recent failures also necessitate improvements in regulatory oversight, risk management practices, corporate governance, capital adequacy, technology and cybersecurity, customer protection, and crisis management. A robust data strategy is crucial to fostering a safer, resilient, and transparent banking industry. However, addressing data challenges requires a comprehensive approach, including modernizing data architecture, implementing robust data governance frameworks, enhancing data quality controls, and ensuring data integrity throughout the organization. By doing so, banks can improve their ability to assess and manage liquidity risk accurately.
How Apexon Can Help
Apexon offers readily implementable solutions for market monitoring and risk management, supported by robust data capabilities and subject matter expertise. Our data and analytics practice creates systems to monitor a wide range of market signals using predefined KPIs and predictive analytics. Our subject matter experts collaborate with your firm to provide innovative analytics solutions, including proactive monitoring of social media hashtags. We also design daily stress testing frameworks and liquidity risk analysis using real-time data and predictive analytics. With a team of over 1000 data experts and architects, we offer accelerators in data management, including an enterprise data quality engine and micro-service-based data services for regulatory compliance.
Our comprehensive offerings encompass metadata models, data catalog, lineage, and data virtualization services. By leveraging our expertise, firms can proactively manage risk and meet regulatory requirements in real-time. Apexon closely monitors emerging trends and develops solutions with a strong data strategy at the forefront. Our data service & analytics supports organizations in achieving their data strategy objectives and driving success, with expertise in regulatory reporting, data governance, stress testing, and AI/ML. We provide custom frameworks driven by real-time data and predictive analytics, aligning with any organization’s functional environment. Apexon offers ready-to-use accelerators for data management and a proven framework for building a data strategy in under eight weeks.
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