Transforming securities finance through technology

Key takeaways

• Modern tech improves decision-making, efficiency, risk management
• Firms can leverage synergies as technologies develop
• Full adoption requires overcoming regulatory challenges

The first intraday repo transaction on a distributed ledger technology (DLT) platform last year marked a major step in transforming this market.

It demonstrates the potential for such technology to lower risks and costs associated with repo activities, and, by accelerating settlement and collateral mobility, it makes possible the idea of shortening the minimum duration of a trade to hours or even minutes.

Whether used for intraday repo financing transactions, in securities lending to help reduce settlement lag in collateral swaps, or to give regulators an overview of collateral chains in the global securities finance and repo markets, DLT platforms are picking up the pace.

So are other technologies, which are changing how data—the lifeblood of financial markets—is exchanged and managed. The emergence and intersection of DLT, machine learning (ML), artificial intelligence (AI), cloud, and big data are transformational. Each technology progresses at a different speed and brings new opportunities as well as challenges.

Generative AI and ML on the rise

AI, particularly multimodal and generative AI, is rapidly transforming the landscape of Capital Markets. With an anticipated global expenditure exceeding USD 100 billion in 2024, AI’s integration into financial sectors is not just a trend but a significant investment. The automation capabilities of AI extend to various domains, including trading decision making, fraud and market abuse detection, risk assessment, and the acceleration of trend analysis and capital risk reporting.

A notable shift is observed in the securities and investment firms, with a Risk.net webinar poll indicating that 69% of them are actively exploring AI options, including machine learning (ML) and generative AI. This interest is driven by AI’s ability to enhance risk management through multimodal systems that process both textual and visual data, offering comprehensive insights into market trends and investment strategies.

This year is poised to witness generative AI becoming an essential component of enterprise technology stacks, according to a report by Snowflake. In Securities Finance, AI models are particularly beneficial for analyzing market data, simulating scenarios, and creating custom financial models and also support decision-making processes by generating synthetic data for back-testing and stress-testing purposes.

Moreover, ML is at the forefront of implementing ethical AI frameworks within the Securities Finance industry. Investment platforms leverage ML for predictive analysis, price prediction, portfolio optimization, and future trend forecasting.

Ethical considerations are paramount, and ML plays a pivotal role in ensuring privacy, fairness, transparency, risk assessment, security, and governance. By adhering to ethical principles, ML algorithms can scrutinize transaction patterns, detect anomalies, and prevent fraudulent activities, thereby maintaining trust in the financial system. The convergence of ML and ethical AI frameworks signifies a commitment to responsible innovation in finance.

The cloud as a platform for synergies

With the finance and banking sectors ranked among the most proactive global cloud services users, the cloud computing market is expected to surpass USD 1 trillion by 2028, according to findings by Precedence Research.

To optimize operations, financial institutions will likely continue this year to adopt more cloud-native solutions. This requires rationalizing applications to choose those truly suitable for the cloud. Adopting cloud infrastructure enhances efficiency through cost optimization and the allocation of resources and enables automation.

As APIs are increasingly connected to the cloud, cloud-native applications can seamlessly integrate with external APIs, enabling collaboration with other financial institutions. Cloud also supports blockchain-based solutions and DLT for securities settlement, improving transparency and reducing settlement times.

Financial institutions are moving to the cloud at such a pace that by 2024 two thirds were expected to have applications that are either fully cloud-native or to have fully adopted cloud, the DTCC said in January last year.

Big data transforms Securities Finance

Whether it’s real-time insights, enhanced decision-making, fraud detection, risk management or cost-savings, big data plays a crucial role in Securities Finance, often in tandem with AI, DLT, or via cloud architecture.

ML allows banks to use data to train algorithms more accurately and to do this more efficiently than humans. Companies now use AI to incorporate big data sources with deep learning and predictive analysis applications.

According to a Statista report, the value of global big data analytics market – technologies, services, and solutions related to analyzing large and complex datasets – will exceed USD 650bn by 2029.

Addressing the challenges

Tackling the challenges and risks related to data, regulatory compliance, and integration with legacy systems remains crucial for responsible adoption.

Financial institutions must comply with regulations that vary across jurisdictions, such as those governing the use of AI/ML. The decision-making of these models can also carry risks if they are fed biased, inaccurate, or incomplete data. In addition, given that finance is a key target for cyber-attacks, working with big data or migrating to cloud systems raises privacy and data security concerns.

These issues outlined will continue to challenge financial institutions and must be addressed, but the benefits of harnessing new and transformational technologies are substantial.