2024 has been another year of unprecedented growth for AI and machine learning in the financial services industry, with a staggering 80% of trading firms now using it. advanced technology.(1) As we approach 2025, what trends will dominate the industry as businesses continue to leverage this technology and harness the power of transaction and market data? Matthew Hodgson, CEO of Mosaic Smart Data, gives his top ten predictions for the year ahead.
1. AI-driven decision making
“Investment banks will increasingly rely on AI-driven insights to make decisions, moving from historical data analysis to predictive analytics. AI models will be used by an increasing number of commercial companies to process large amounts of unstructured data, improving forecasting and strategic planning.
2. Hyper-personalized customer engagement
“Through data analytics, investment banks will continue to improve their ability to deliver hyper-personalized experiences tailored to clients’ investment preferences, risk profiles and financial goals. This type of personalized advice will allow banks to offer tailor-made solutions, deepen relationships and anticipate customer needs, thereby strengthening engagement and loyalty.
3. Real-time decision making
“Real-time data platforms will enable faster, more informed decisions in trading, risk management and client interactions, thereby optimizing performance and reducing execution latency. Combined with machine learning, this will improve risk assessment, allowing banks to manage risk on a transaction-by-transaction basis. Predictive analytics will increasingly be used to flag potential risks before they impact the bank’s portfolio.
4. Digital Transformation in Sales and Trading
“Digital tools and advanced analytics will automate workflows, optimize trade execution and provide insights to increase sales performance and maximize profitability across all asset classes. »
5. Optimized liquidity management
“Banks will use data to better forecast their liquidity needs, align their inventories with customer demand and improve their balance sheet efficiency, particularly in fixed income markets. Promoting inventory in response to the most likely customer demand will become table stakes.
6. AI-powered compliance and monitoring
“Machine learning will enable the integration of transaction and communications monitoring, enabling better detection of fraud, insider trading and other non-compliance risks to increase the identification of bad actors .”
7. Cross-silo data integration
“Investment banks will work to break down internal silos by integrating data from all business units, providing a holistic view of client activity, profitability and operational efficiency. This will significantly improve sales efficiency for selling products across all asset classes.
8. Analysis of customer profitability
“Banks will adopt more sophisticated customer profitability tools, analyzing granular transaction data to identify high-value relationships and allocate resources more efficiently. »
9. Growing use of alternative data
“Non-traditional data sources, such as social media sentiment, satellite imagery and climate data, will play a greater role in investment strategies. Banks will leverage this alternative data to better understand market trends and investment opportunities to generate alpha.
10. Harnessing innovation to improve profitability
“Rather than viewing innovation as a ‘nice to have’ expense, businesses will increasingly look to new technologies to improve the efficiency and profitability of their operations, with an emphasis on return on investment in any new solution they deploy.
