Written by Chris Probert, Partner and Global Head of Data & Generative AI
The arrival of DeepSeek’s R1 open-source model has rocked the AI industry. Chris Probert, Partner and Global Head of Data & Generative AI at financial services consultancy Capco, comments on its implications for financial services firms.
“R1’s launch underlines both that AI innovation is still accelerating, and that predictions that smaller language models would be a compelling option for organisations was correct.
“If they felt that large LLMs were previously the only game in town, the R1 model is now a real option for addressing organisations’ problem statements. For example, smaller models give firms the opportunity to leverage and curate their own training datasets, due to the lower data requirements needed to train smaller language models.
The ability for organisations to use their own training data – rather than relying on hyperscaler datasets –can enable several key benefits:
- It will enable financial services firms to develop more fine-tuned and relevant models. This is particularly valuable for institutions who may need to accommodate diverse customer and compliance needs across multiple sectors and countries.
- It will reduce concerns about data security and privacy, where organisations no longer need to leverage hyperscaler models that operate in the cloud and can control where data is stored and how it is used.
- It will drive greater opportunities for competitive advantage and differentiation, allowing financial services organisations to leverage proprietary data to create unique AI models.
- It will increase AI transparency and explainability, giving firms greater visibility of how and why the model generates a specific output and can audit its data sources. This will be critical in a highly regulated industry such as financial services.
“There has already been plenty of discussion around the benefits of building AI capability in an agnostic way – that is, avoiding vendor lock in to ensure firms have sufficient flexibility to adapt to market changes and benefit from ongoing AI innovation. The R1 model underlines the importance of this agnostic perspective.
“Firms should focus on scalable enterprise solutions that allow easy model swaps, providing flexibility while also minimizing transition costs. By embedding modularity and interoperability into the solution architecture early on, organisations can future-proof their AI investments against rapid advancements in the field. Building AI capability in a way that recognizes the pace of change is likewise key.
“To build for adaptability and high pace of change you can’t be locked into a single vendor, and a ‘model of models’ approach will be the optimal path forward. Robust model benchmarking will be crucial, allowing financial services organisations to evaluate which AI models best align with their specific use cases, maximise performance, and deliver the highest return on investment.
“R1 has been described as AI’s ‘Sputnik moment’—and just as Sputnik triggered a massive acceleration in change, we will now see the same in AI. The main challenge for the financial services industry will be keeping pace.”
