The financial services industry deals with increasingly large volumes of data. With data volumes growing, buy-side and sell-side firms are exploring data science techniques to better understand risk and opportunity, but may struggle to maximise value from these projects due to limitations with systems, skills and resources.
This survey was conducted during a webinar hosted by Quantifi and Risk.net on “The Future is Now: How Data Science is Revolutionising Risk Management and Finance”. The guest speakers were Francois Mercier, Vice President, Senior Data Scientist, Citi, Victor Daniel Casas Hernandez, Global Head of Data Science, Machine Learning & Artificial Intelligence – GTI, JPMorgan Chase & Co, and Michael Natusch, Chief Science Officer, Prudential. More than 250 individuals from across the financial services industry registered for the webinar and were invited to participate in the survey.
Data science is playing an increasingly pivotal role across capital markets and has the potential to transform decision-making in investment securities, an area with vast and growing volumes of unstructured data.
To what extent do you agree that data science delivers better decisions in investment securities than previously possible?
Technology is central to modern investment decision-making and over the past two decades there has been a fundamental change in the way that data is analysed. The combination of an increasingly complex world, the abundance of data and the pressing desire to remain ahead of competition has prompted firms to focus on using sophisticated analytics for driving strategic business decisions. Rather than relying on human-based intuition, data science is helping firms streamline their investment process by allowing them to better manage, assess, and refine idea generation, portfolio construction and risk.
98% of respondents agree that data science delivers better decision-making than has been previously possible. This suggests that the use of data science is accelerating as firms look to make better, more informed investment decisions.
Quantifi supports data science and provides clients with the ability to do complex data analysis and flexible reporting using Python, Jupyter and other popular data science tools. Clients benefit from complex user-driven analysis, strategy back-testing, ad hoc portfolio what-if analysis – all using mixed data sets from diverse sources.
Buy-side and sell-side funds are grappling with the tools to better understand risks and opportunities in systematic market making, algorithmic trading, risk management and compliance and regulatory reporting.
I am using data science tools in (select all that apply):
The financial industry deals with large volumes of unique data and this data comes with some characteristics that other industries do not share. This has a huge impact on the different applications of data science within finance. Of the various use cases included in the survey, participants selected risk management and compliance (87%) as the most common use case, followed by regulatory reporting (35%).
Risk management and compliance is an integral part of any financial firm and is highly dependent on data. The new age of data science offers a level of sophistication that is helping firms better understand the dynamics of their business, anticipate market shifts and manage risks. One example of how firms can gain an advantage using data science is when they need to run highly complex calculations and simulations on large portfolios/large data sets for pricing and valuations.
Quantifi has stayed ahead of the competition by continuing to make smart investments in emerging technologies and next-generation approaches including data science. A common use case Quantifi has seen amongst its clients is back-testing, whereby clients combine Quantifi’s analytics and risk tools with mixed datasets from diverse sources and open source tools such as Python, Jupyter, and RStudio. This creates an ideal platform for back-testing analysis for portfolio and product structuring. If a portfolio manager constructs a portfolio or a trader structures a product, before they execute on the portfolio or product they back-test it against current historical data or stressed historical data to anticipate how the portfolio or product would perform.
Quantifi provides open, seamless and easy to use integration with popular data science tools. This gives clients a powerful and flexible environment for the next generation of analysis and reporting.
What is driving data science in investment securities?
People (45%) and data (35%) are seen as most significant drivers of data science, while the market (20%) is also considered a significant contributor.
Several years ago, the first of the data scientists were developing interesting proof of concepts, however, they were never used because no one on the business side was involved in the development, and therefore did not understand the benefit. Today, data science has become a real game-changer in how data is absorbed and analysed. Data science has one primary limitation: context. While data science is seen to be extremely effective, it is meaningless without the people that know how and/or where to apply it efficiently. This is where individuals that are capable of both analytics and critical thinking come into their own.
Whilst there are many proponents of data science, people are also the main opponent at many firms. These opponents are the ones that need to be convinced of the value of what data science can do by seeing small pieces of value iteratively, that can then be expanded and extended across other areas of activity. From this perspective, the people element is important and many individuals working in data science can end up feeling like salespeople for their own skillset.
The proliferation of data and the tools required for processing this data, combined with the advances in available computational capacity, is also driving data science applications even further.
Lastly, market structure is rapidly changing in such a way that automation is becoming a prerequisite to remain competitive. This is only possible by implementing processes centred on data science and AI that are designed to condense and automate lengthy manual tasks into a few seconds. This, in turn, allows individuals handling large amounts of data to focus their time on running analysis concentrate on critical business outcomes.
