Designing data architectures that adapt to changing conditions

The global economy is at the mercy of evolving technologies (did anyone think ‘AI!’?), as we live in an increasingly data driven world. Robust data architecture design is crucial for ensuring efficient data management, scalability, adaptability, and the support of business intelligence. Creating a sustainable data ecosystem is important for a viable economic future for the enterprise, allowing for more efficient data flows, storage, and retrieval.

A clear and well-designed data architecture helps businesses grow, ensuring infrastructure can scale smoothly when accommodating larger loads without producing negative effects on reliability or performance. Effectively structured data architecture enables organisations to adhere to regulatory compliance requirements for data security and governance, thus reducing possible risks linked to data misuse and system leaks. 

The role of AI in market shifts and informing risk models

Allocating around $35 billion towards AI projects, the financial sector is leading the way when adopting AI. It is estimated that AI in the finance market will reach $190.33 billion in value by 2030, a CAGR of 30.6% from 2024 to 2030. 

AI-powered solutions help predict market shifts and produce financial modelling through improved data processing, and automated responses.  

Some areas in which AI can be leveraged in risk intelligence and the financial sector include:

• Credit risk assessment
• Fraud detection
• Personal finance assistant
• Portfolio management 
• Stock market prediction
• Algorithmic trading

Organisations such as Siemens have integrated AI dashboards to enhance financial reporting, achieving a 10% increase in accuracy. Continuous learning models and the integration of digital twins requires scalable data infrastructure, as advanced AI and digital simulations cannot run effectively unless platforms are built to store, process, and move different data types, efficiently and at scale.  

AI is playing a key role in portfolio optimisation, evaluating risk-return trade-offs, market conditions, and asset correlations. Moreover, AI stress testing models are implemented to evaluate portfolio performance, particularly during market downturns or periods of economic uncertainty. 

By 2025, it is forecasted that 85% of financial institutions will have adopted AI into their operations, a rise of 40% from 2022. In the last four years, we have witnessed a 150% increase in cloud-based financial modelling platforms being deployed, with the demand for skilled experts in financial modelling rising by 60% compared to 2020. 

According to an NVIDIA financial services survey, 86% of financial institutions reported increased revenue streams from AI-based projects, while 82% experienced a reduction in expenditure. The report also discovered 97% of companies plan to increase AI investments, underlying the true impact AI already has on global markets. 

ML and DL (deep learning) algorithms are important in helping organisations learn from sourced data, in structured and unstructured forms, to predict future outcomes. Alternative data, such as news feeds and social media – so-called third party data – are also being used to gain new insights into market shifts.

When it comes to fraud detection, AI is taking a key role, able to spot anomalies in transactional data, and help flag potential human errors and risks.

Multi-cloud strategies for compliance and performance

It goes without saying but managing multiple cloud platforms heightens operational complexity with each provider having its own set of tools, billing structures, and interfaces; a situation often leading to integration and management challenges. To overcome such hurdles, it is recommended to implement unified tools, automation, and governance frameworks that work irrespective of platform. 

Compliance here is another area of concern, with different providers offering different security features and compliance certifications. Therefore, having a clear understanding of all relevant policies, regulations, and tools is table stakes to ensure adherence across all cloud services. 

Multi-cloud strategies can lead to unexpected substantial expenses, especially when there is a lack of sufficient management of day-to-day spend on cloud resources. To combat this, a comprehensive cost monitoring strategy is needed. This may include the use of unified management tools, automated governance, investment in training, and certification for upskilling teams. 

How financial services are responding to geopolitical and macroeconomic events 

According to its Financial Stability Report in May 2024, The European Central Bank (ECB) spoke of geopolitical instability, emphasising a need for banks to take a “proactive approach[es].” To manage risk, the paper suggested diversity in risk management and diversification technologies, such as enhanced, multi-cloud risk monitoring systems and collated real-time data analytics. 

Global institutions have responded in a range of ways to their architectural challenges, including the adoption of strategic risk diversification. With real-time data insights, portfolio rebalancing to hedge market volatility and inflation, and the ability to adjust operations, highly regulated financial institutions can get closer to constant compliance.

(Image source: “architecture” by barnyz is licensed under CC BY-NC-ND 2.0.)

See also: Amazon invests $10B in North Carolina AI data centre

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