Regulators, internal stakeholders, customers, and investors are demanding more transparency with understanding of front office, risk, and capital models - from trading algos, capital models to counterparty risk models that incorporate statistical learning approaches. Transparency demands are required not only at an analytical level, but also in development workflows and lifecycle activities associated with risk models and data. With these developments, one imperative that we believe to be significant in the coming years is the emergence of next-generation risk technology powered by data science approaches.
Co-hosted by Quantifi & Celent
- Market Imperatives & Business Drivers
- The Quantitative and analytics Challenges
- What is Data Science & How Can it be Applied?
- Key Takeaways for Firms
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- Avadhut Naik, Head of Solutions, Quantifi
- Cubillas Ding, Research Director, Celent