The growth rate of the Global Artificial Intelligence in the fintech market is predicted to be 23.4% for the 2022 to 2027 forecast period.

This is worth an estimated $42 billion value by 2027. Key companies operating in this market included Active.Ai, Alphabet Inc., Intel Corp and Microsoft.

Leading artificial intelligence (AI) adoption are the big banks, according to research from Evident which ranked the 23 largest banks in North America and Europe by their ability to develop and deploy AI-powered solutions.

Big AI spender JP Morgan topped the list, followed by the Royal Bank of Canada, Citi, UBS, and Wells Fargo. Goldman Sachs and Morgan Stanley occupied the middle spot of the league table, which measured the financial institution’s AI talent, innovation, strategy, and responsible AI practices.

Yet for the technology to realise its potential, particularly in financial markets, it needs secure cloud environments to support data curation and movement, data analysis, connectivity and resilience for a dynamic ecosystem.

That is because AI and machine learning (ML) require real-time data to be processed with fast, reliable network connectivity. Large data must be stored and run on the cloud for scalability; examples of cloud-native AI and ML resources now available include Google TensorFlow, AWS Machine Learning and Microsoft Azure AI.

Furthermore, security is a priority for firms looking to utilise cloud-based applications within financial markets, firms need to ensure that their financial data is being transmitted and stored securely, with advanced Identity access and confidentiality techniques, coupled with encryption to guarantee that the risks of unauthorised access, data breaches are prevented.

Cloud and network-based functions can provide solutions that help achieve the security outcomes required. BT Radianz enables its clients to accelerate their AI and ML journey by providing access, over a resilient and diverse connections, to more than 400 third-party technology providers and has become a key component in the growth of AI and ML usage in financial markets. BT Radianz helps banks, brokers, trading and investment firms, exchanges, trading venues, and clearing houses leverage AI/ML from pre-trade to post-trade activities including execution, risk, and regulatory compliance. High-frequency trading firms, systematic traders, and quantitative hedge funds can now use ML to analyse market data to identify trading and investment opportunities.

AI and the future

In post-trade operations, AI adoption is still in the early stages, as most central securities depositories and central counterparties rely on legacy technology infrastructures. These institutions are now investigating how supervised and unsupervised ML techniques can help with trade settlement, clearing, and reporting.

Some data reporting service providers and trade repositories are now developing AI solutions such as anomaly detection and automated data extraction from unstructured documents, with the aim of improving the efficiency and accuracy of post-trade processes.

Stock exchanges and other trading venues are also applying AI to detect irregular and potentially malicious trading activity. Nasdaq, for example, now leverages specific machine learning capabilities for market surveillance.

Yet the progression of AI in financial services depends on the technology’s role in decision making. AI can currently improve trade execution performance; optimising hedging and quoting decisions; and automating brokers’ responses to client requests. Meanwhile, investment banks and brokers can also use AI-driven execution models to reduce transaction costs.

However, AI’s capacity for higher-level strategic decisions has been challenged. A 2020 study by Amit Joshi and Michael Wade of IMD Business School in Switzerland finds that “AI is mainly being used for tactical rather than strategic purposes — in fact, finding a cohesive long-term AI strategic vision is rare.”

Likewise a 2018 KPMG survey of 2,190 executives from nine countries representing financial, insurance, government and other sectors found that 67% of CEOs often prefer to make decisions based on their intuition and experience rather than insights from data.

And in a 2022 Deloitte survey, 67% of executives said they weren’t comfortable using data from advanced analytic systems, and that in 2021 42% of data scientists from a cross-section of industries said their results aren’t used by business decision-makers.

The advent of ChatGPT may create a path towards a more strategic AI, though we still some distance away from AI computations replacing boardroom decisions.

Conclusion

With the progression of AI, adequate infrastructures need to be in place to upgrade legacy operations and make smarter decision making. This will depend on the accessibility of cloud-based computing power and high-performance networking, the availability of large and varied datasets, data storage and analysis backed by secure on-premises and cloud options.

A multi-cloud approach with data residing in the cloud can be highly secure when backed by AI and ML powered data analysis and good practice. If cloud migration is to remain a major part of the financial industry, traditional banks and emerging fintechs will need the technologies to transition to cloud services, enabling them to be scalable and more effective.

AI and ML will remain critical to this transition. It was reported at the Financial Cloud Summit 2023 that ChatGPT had been asked whether an incumbent bank should move 100% of their infrastructure to the cloud. Its response was that the bank should be multi-cloud. Irrespective of the businesses model, institutions must take ownership of their cloud transformation, adopt SaaS and low code, and be data driven. By building security into a company’s culture and operations, and by applying AI to data analysis, cloud will continue to support outstanding business performance in financial institutions. ChatGPT and generative AI could revolutionise payments, banking, and insurance. Whether it can and will, depends on how cloud is used and developed.

Leackstat 2023