Rising capital requirements, increased competition, margin pressure and declining profitability are all factors that continue to put sell-side institutions under increasing pressure to use limited resources to maximize efficiency. To achieve this, technology and data are critical, but not all.
Bloomberg is proud to profile the following innovative explorers in the banking and securities industries whose innovative use of technology and data is driving the transformation of sell-side institutions in Asia Pacific. From building new digital operating models, enhancing customer experience to automating manual processes and developing data-first strategies in business rollouts, they are true industry innovators
Trading & Execution
Traders create more time for high-value tasks while increasing revenue, enhancing distribution and accuracy.
Data Management & Infrastructure
Data-first advocates have enabled creative use of data within the enterprise
Astute and forward-looking leaders use the latest technology to implement a comprehensive and proactive risk management strategy.
Wealth managers use data and technology to enhance their business competitiveness and identify new ways to drive growth.
Sell-side digital transformation
Global Chief Risk Officer | Bank of Singapore
Alexandre has over 20 years of banking risk management experience in investment banking and private banking.
Climate change is one of the greatest risks we face today, but it also presents opportunities. This may be our last chance to change the situation and avoid repeating the same mistakes.
Q. How have risk factors changed over the past few years in addressing the digital future?
When I first started in the early 2000s, the focus of the risk management field was on financial risks, such as credit and market risks. But then, non-financial risk management, such as operational, commercial and conduct risk, began to develop and become a more important part of today's private banking landscape. In fact, I currently spend about 80% of my time managing non-financial risks, whereas ten years ago they accounted for far less than 50% of my energy.
With the growing importance of non-financial risks, we have to move away from traditional methods and use data to manage risk more aggressively. The key challenge is getting clean, accurate data. Technology is definitely needed to complement the system, help risk managers develop solutions and create the right decision-making tools.
While we have successfully used data to create more accurate financial models and more effective stress tests, there is still work to be done on non-financial risks. Less "tangible" risks have been difficult to predict in the past through modeling or traditional data. But now, with the application of alternative data and artificial intelligence models, this piece is better than before. For example, models applied in climate prediction, spatial data and other fields can already tell us the growth of crops and their profitability prospects in the next 6-12 months, farmers can better manage production, and analysts can also use this to inform the profitability of food companies. make more accurate estimates.
Q. The Bank of Singapore recently announced a transaction and communication monitoring tool based on artificial intelligence and data. Can you expand on the tool?
The solution is designed to connect spatial data from structured and unstructured data sources, and its textual analysis enables the Bank of Singapore to delve deeper into conversation topics, sentiment and due diligence with customers. We can get an overall overview of each client's situation, eliminating the need for risk control teams to manually evaluate sample accounts. Moreover, this advanced analysis will also help the team discover unknown behavioral patterns and relationships.
Considering that our communication with customers is carried out through multiple channels and in various forms, including audio, video and text exchanges, it is intuitively important to save and properly handle communication records, and we want to ensure that the quality of these processed content is usable. at the level of analysis. We definitely want to avoid a "garbage in, garbage out" situation.
Another area we focus on is people. People are often one of the most important factors behind the success of any transformation project. Some employees are initially hesitant to use machine learning-based tools because they feel they may be at risk of being squeezed out of their jobs, but that’s not going to happen because we still need risk managers to make decisions. The system simply prompts when an abnormality occurs. After that, people need to investigate whether something went wrong, and only human intelligence can do this very accurately.