A wave of AI tools is reshaping finance, starting with the tedious work that keeps junior bankers at their desks until midnight. Rogo, a new AI platform covered in an OpenAI case-study published this week, has automated $4.7 billion worth of analyst work since its 2024 launch, saving users 10+ hours a week.
By fine-tuning OpenAI’s latest models with financial data from S&P Global and Crunchbase and FactSet, Rogo has grown to serve over 5,000 bankers. The platform uses different AI models for different tasks, matching the right tool to each job. GPT-4o handles the analysis of earnings calls and market trends. The fast o1-mini model processes millions of documents to find relevant information quickly. For the really challenging work like valuations and deal structuring, they use the more powerful o1 model. Looking ahead, Rogo has hired Joseph Kim from Google’s Gemini team to work on predicting M&A success rates. Early tests on 10,000 past deals show 89% accuracy in forecasting regulatory issues two months before they’re announced.
Meanwhile, Microsoft have been identifying wealth management as a sector ripe for disruption. “Portfolio construction can be handled by conventional AI,” says Martin Moeller, Microsoft’s head of AI for financial services in EMEA. UBS appears to agree, CEO Sergio Ermotti recently highlighted AI’s potential to increase productivity and simplify jobs.
Behind the scenes, AI is handling ever more critical processes. Credit scoring processes are using AI-driven alternative credit assessments leverage non-traditional data points. Risk management systems inspect millions of transactions in real-time with AI, with one bank reporting they catch 92% of suspicious activities before any losses occur.
The next phase will be “agentic AI” and potentially systems that make independent investment decisions. For now, though, the focus remains on augmenting rather than replacing human judgment. As one private equity business noted, they’re closing deals 45% faster, but still need human expertise for the final decisions.
Takeaways: The financial sector’s embrace of AI extends far beyond chatbots and robo-advisors. Its real impact lies in areas customers rarely see – from using alternative data for credit scoring to automating complex regulatory compliance. While challenges around regulation and cybersecurity persist, banks can’t afford to wait. The most telling sign comes from major institutions building custom AI workflows and starting to adopt in-house designed agents rather than just buying off-the-shelf solutions. This suggests they see AI not just as a cost-saving tool, but as something that could expand their workforce and redraw competitive lines. In an industry where information advantages typically last minutes or seconds, the ability to analyse and act on data faster than rivals could prove more valuable than traditional advantages like size or brand.