Steps Ahead

Ten trends shaping AI-driven data analytics in 2025

2024 has been another year of unprecedented growth for AI and machine learning across the financial services industry, with a staggering 80% of trading firms now using this cutting edge technology.[1] As we head into 2025, what will be the trends that dominate the industry as firms continue to leverage this technology harness the power of transaction and market data? Mosaic Smart Data CEO Matthew Hodgson gives his top ten predictions for the year ahead.

By Matthew Hodgson, CEO, Mosaic Smart Data

1. AI-Driven Decision-Making

“Investment banks will increasingly rely on AI-driven insights for decision-making, moving from historical data analysis to predictive analytics. AI models will be used by a growing number of trading firms to process vast amounts of unstructured data, improving forecasting and strategic planning.”

2. Hyper-Personalised Client Engagement

“Using data analytics, investment banks will continue to improve their ability to offer hyper-personalised experiences tailored to clients’ investment preferences, risk profiles, and financial goals. This kind of personalized advisory will enable banks to deliver tailored solutions, deepen relationships, and anticipate client needs, boosting engagement and retention.”

3. Real-Time Decision-Making

“Real-time data platforms will drive quicker, more informed decisions in trading, risk management, and client interactions, optimising performance and reducing latency in execution. Coupled with machine learning, this will enhance risk assessment, allowing banks to manage risks 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, optimise trade execution, and provide insights to increase sales performance and maximise profitability across asset classes.”

5. Optimised Liquidity Management

“Banks will use data to better predict liquidity needs, align inventory with client demand, and improve balance sheet efficiency, particularly in fixed-income markets. Promoting inventory to the most probable client demand will become table stakes.”

6. AI-Powered Compliance and Surveillance

“Machine learning will enable the integration of transaction and communication surveillance, enabling better detection of fraud, insider trading, and other compliance risks to increase the identification of bad actors.”

7. Cross-Silo Data Integration

“Investment banks will focus on breaking down internal silos by integrating data across business units, enabling holistic insights into client activity, profitability, and operational efficiency. This will significantly improve sales effectiveness to cross sell products across asset classes.”

8. Client Profitability Analytics

“Banks will adopt more sophisticated client profitability tools, analysing granular transaction data to identify high-value relationships and allocate resources more effectively.”

9. Expanding Use of Alternative Data

“Non-traditional data sources, such as social media sentiment, satellite images, and climate data, will play a more significant role in investment strategies. Banks will leverage this alternative data to gain insights into market trends and investment opportunities to drive alpha generation.”

10. Harnessing innovation to drive cost-effectiveness

“Rather than viewing innovation as a ‘nice to have’ expense, firms will increasingly look to new technologies to drive the efficiency and cost-effectiveness of their operations, with a laser focus on ROI for any new solutions they deploy.”

[1] https://www.globaltrading.net/gt-survey-is-ai-our-future/?utm_campaign=Global%20Trading%20SOCIALS&utm_content=299576501&utm_medium=social&utm_source=linkedin&hss_channel=lcp-2663831