Integrating Quantifi’s cross-asset analytics and best-of-breed Big Data and Data Science tools to analyse and visualise results.
Complex Data Analysis
Data has a huge influence on the financial services industry and today, the volumes of data accumulated are so large that traditional evaluation and analysis methods are no longer suitable. Quantifi supports data science and provides clients with the ability to do complex data analysis and flexible reporting using Python, Jupyter Notebooks and other popular data science tools. Integrated with Quantifi’s proven portfolio management solution, users benefit from complex client-driven analysis, strategy back-testing, ad hoc portfolio what-if analysis – all using mixed data sets from diverse sources.
Demonstrate alpha and support tailored investment solutions
Improve investment decisions with machine learning and AI
Lower operational costs with an integrated self-service architecture
Improve Investment Decisions
Mix Diverse Data Sources
Easily mix diverse data sources for complex reporting, including structured & unstructured data.
Flexible Self-Serve Reporting
BI and visualisation which includes use of third-party tools such as Power BI and Tableau.
Processing capabilities can easily scale up or down to accommodate a client’s demands & business needs.
Complex Ad-hoc Analysis
Modern Python API
Combine Quantifi risk results, data and models with other data science & machine learning tools.
Data Science Enabled
Portfolio Management Solution
Integrated with Quantifi’s proven portfolio management solution, users benefit from complex client-driven analysis, strategy back-testing, ad-hoc portfolio what-if analysis – all using mixed data sets from diverse sources.
Adoption of Data Science
Firms are now recognising that big data technologies, like data science, are the way forward. Using data science can help them focus their resources efficiently, make smarter decisions, and improve performance. This survey was conducted during a webinar Quantifi hosted, featuring Celent, on ‘Next Generation Risk technology Powered by Data Science’. Over 180 individuals from the financial services industry registered for the webinar and were invited to take part in the survey.
Navigate major trends & developments shaping the industry
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.
Version 18 includes 304 new features and enhancements, providing superior performance, new BI reporting capabilities and includes the next-generation of risk analytics powered by data science. Version 18 also introduces features to help clients accelerate their IBOR transition programme with confidence.
Part 1 in this blog series explores data science in the context of risk management technology and operations. This blog reflects on how the financial environment, and the broader landscape, has changed over the last decade and the market trends that are driving the rise in data science approaches. Harsh Realities from the Financial Crisis […]
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