From 18th-21st September 2023, Toronto's Metro Toronto Convention Centre will host Sibos 2023, a premier financial event. Emphasising 'Collaborative finance in a fragmented world', it aims to merge technological advancements with trust amidst global challenges. Discussions will cover a gamut from ESG standardisation to the impact of AI on finance.
In addition to its speaker showcase, Funds Europe has garnered insights from leading figures in the pan-European institutional investment sector, shedding light on topics like banking, cybersecurity and technology.
Yvan Mirochnikoff, head of digital solutions, Societe Generale Securities Services.
Considering the slow adoption of AI in post-trade processes, how can asset managers effectively unify fragmented data systems to harness AI's full potential in the industry?
The post-trade industry has performed dramatic changes in the last few years in order to improve its data management based on Cloud storage systems. The emergence of AI tools based on LLMs (such as ChatGPT) and Machine Learning algorithms highlighted the fact that IT is not sufficient to leverage the huge potential offered nowadays. We currently lack shared business rules, common definitions and enough data quality knowledge to be able to fully address the challenges of our industry, offer a better customer experience, and extract more value from the data. The first AI applications, like chatbots and robo-advisors, suffered from the inexperience of business representatives to harness these tools and efficiently collaborate with data scientists. The solution relies on the training of all actors from our industry and developing capacities to onboard experts on AI projects: investment strategy, fund management, economy, etc., plus regulation, legal and compliance.
We should collectively support all transversal initiatives in our industry when it makes sense. These include common KYC processes, convergence of data models (like GAIA-X) and all works related to the security framework and controls of AI environments in order to reduce potential biases and misuses of such systems. The final objective is not only to harness the potential of AI but also to instil the confidence of our clients and investors in the applications we will deliver.
Evangelos Skianis, managing director and chief technology officer, MUFG Investor Services.
As AI and machine learning gain traction, how can the financial sector balance technology use with maintaining trust?
Harnessing AI and machine learning is poised to bring enhanced efficiency for financial institutions but requires a careful approach rooted in trust. It's wise to first integrate AI into mature, low-risk processes, setting clear metrics to measure its ROI. For example, we're exploring AI's potential in code generation, aiming to bolster development teams. Given our consistent development processes, AI can be adjusted to generate code aligning with our standards. Quality control is also vital as AI models can sometimes err, as seen with tools like ChatGPT. Human oversight ensures the accuracy and logic of AI-generated content. In the previous example on code generation, developers should review and adjust AI-produced code, ensuring quality before moving into the QA stage.
Furthermore, assessing costs, time, and resources for AI implementation is essential. The rapid evolution of this technology means firms should be ready for constant improvements and upcoming superior technologies that might quickly make previous AI investments deemed legacy at a more rapid pace than ever before. In conclusion, the financial sector stands at the intersection of unprecedented technological innovation. Navigating this requires a blend of exploration, caution, and adaptability. These are exciting times.”
Ryan Cuthbertson, global head of custody services, BNY Mellon
How is data intelligence transforming decision-making processes within the funds industry?
Fund clients not only want need only require operational data, but need that data to be insightful, with context and ultimately actionable. Funds are experiencing fee compression, operational pressures as well as industry initiatives to compress settlement timeframes. Fund clients therefore need data solutions to ensure they are equipped to make both intraday and operating model decisions using data as the basis.
As an industry, we are seeing more evolution of digital technologies to further increase the decision-making capabilities based off data. Usage of artificial intelligence, machine learning and natural language processing is quickly becoming table stakes to deliver data that ultimately predicts issues before they become financial impacting events and delivers deeper analytics that allows our clients to think smarted, act faster and ultimately power their front office with better insights to prevent drag on their funds.
We are still at the start of the data journey, relative to where we can go. Cloud-based technologies, real time data streaming, continuous reconciliation, and ultimate end-to-end lifecycle transparency remain focus areas. We need to embrace data and analytics to transform how funds make decisions. This will, in time, reduce or avoid costs, deliver scalable growth, provide resilience in volatile markets, and arm funds with intelligence to prevent unexpected costs of failure.
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