135 LSEG supports both traditional modelling, such as rules-based asset pricing and forecasting, and modern AI-driven approaches that learn from large datasets to deliver adaptive, predictive insights. “Having an agnostic model infrastructure – whether for traditional pricing models, machine learning or other types – is crucial,” says Prince. “If clients need to have separate infrastructures for AI and traditional models that doubles cost unnecessarily. Solving historical modelling challenges with a single, AI-capable framework is far faster and more accurate and efficient. Plus, clients don’t need to build everything from scratch, they can access [our] AI-powered tools immediately to deliver highly tailored solutions without the complexity. Through LSEG’s MaaS solution, financial firms are unlocking ‘dormant IP’ by expanding access to proprietary models, which shortens development cycles while enhancing risk management. “Our clients have a lot of what I call dormant IP – this is IP that is limited to specific internal teams,” explains Prince. “This restricts access, meaning that the full value can’t be realised by those who could benefit most.” Microsoft and LSEG co-created the MaaS offering, combining Microsoft’s cloud scalability with LSEG’s modelling expertise to capture specialised financial modelling knowledge and deliver it at scale. “Tools like Microsoft Excel and Power BI are excellent examples of how financial services professionals can input a lot of their own ideas,” says Prince. “These tools are dynamic, constantly evolving with markets change and ongoing evaluation. They allow for a high level of curation across user levels, letting everyone shape the model’s design and operation to reflect their market perspective. “Microsoft’s reach across financial services is unmatched, with tools like Excel and Power BI embedded in every organisation. Its partnership approach and cross-industry insight brings fresh thinking to entrenched challenges, while its AI modelling capabilities support scalable, efficient infrastructure that complements LSEG’s broader platform. This foundation enables users to personalise models and outputs, bringing their own market perspectives to bear.” For example, two portfolio managers may run models on identical data but produce different outcomes due to differing portfolios and pricing pressures. While the models stay static, the outputs are personalised. This reflects the complexity of financial services, where variations in portfolio composition and pricing assumptions shape how each user interprets and applies the model – even when the underlying framework remains unchanged. “That’s why tools like Excel and Power BI, which support this level of curation, are essential,” says Prince. “The industry must ensure everyone can use their preferred tools and still reach consistent outcomes, regardless of the language used. We’re seeing real democratisation. Historically, only a small group could create models or scale ideas due to limited data access. Now, anyone in financial services can build and deploy ideas. AI-powered agents bring us closer to this vision, though challenges around validation and trust remain. “This is liberating for financial services – the small group of people with access to data likely weren’t the only ones with ideas in the past. Democratising ideation boosts inclusivity, driving innovation and creativity.” FINANCIAL SERVICES Photo: iStock/Pharrel Wiliams
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