Offerings and Technologies That are Enabling and Powering Data Science in Financial Institutions

Create a vendor selection project & run comparison reports
Click to express your interest in this report
Indication of coverage against your requirements
A subscription is required to activate this feature. Contact us for more info.
Celent have reviewed this profile and believe it to be accurate.
12 May 2020
Cubillas Ding

Celent recently released a body of COVID-19 response related resources and perspectives, and this includes my recent report pertaining to the expanding role of NextGen QuantTech and Data Science technology offerings to enable more dynamic, timely and accurate financial and risk modelling. So far, we’ve had positive responses and interest from colleagues, clients and non-clients. After all, it’s a convoluted and rapidly evolving ecosystem of technologies and services that is often confusing to navigate – a minefield of hype and buzz, with a universe of commercial and opensource product offerings, tools and sub-components with variable depth and breadth of capabilities. I am hoping this blog will unravel a bit of this for you.

Understanding the landscape of solutions

Celent views the solution types and technologies associated with the disciplines of QuantTech and Data Sciences from both broad and narrow perspectives. The landscape of offerings is broad, varied, and rapidly evolving, with each category of firm also differing by the degree of use case and domain-specific functionality, content, and data applicable to a specific industry such as financial services (illustrative use cases and vendors are shown below):

In general, the vendor landscape can be characterized by established players (usually incumbent BI/ visualization, and data management vendors) and upstart firms (typically focused on AI / machine learning and incorporating open-source components) — both with strong ambitions to capitalize on the renewed appetite for digitization, Data Sciences, and Artificial Intelligence. Firms can be delineated along the following lines.

  1. Data analytics / business intelligence offerings that are evolving to augment or incorporate requirements associated with Data Science approaches.
  2. Pure play Data Science players that are focused on data aggregation, normalization, and enablement.
  3. Pure play AI / machine learning-led software platforms that help firms with the development and testing of the machine learning algorithms.
  4. Independent software vendors (ISVs) that are re-purposing their data/content assets to be “powered” by Data Sciences.

Celent makes these delineations because we believe that the heritage of vendors and their solutions matter significantly in terms of the strengths of their offerings and their industry alignment. When looking to pursue fit-for-purpose solution strategies (whether for the immediate or longer term), financial institutions need to understand a vendor’s DNA and solutions heritage as well as the trajectory of where investments and roadmaps for specific product domains are headed.

Understanding Yourself

Given that this space is rapidly evolving, financial firms must ensure they understand what they need, their user constituents, and the scope of the tasks to be achieved using these tools. For instance:

  • Are you adopting an in-house strategy to implement and incorporate next-generation capabilities, or are you reliant mostly on your ISV to embed these features through future upgrades?
  • How broad or narrow are your use cases? Are there specialized models required for your tasks, and if so, how would you integrate and co-exist appropriately?
  • If you are considering or using open-source QuantTech and Data Science tools, how confident are you with the maintainability and reliability of their outputs?
  • If you are selecting a solution for immediate consumption (e.g., as part of a pandemic financial impact analysis), how prevalent is the vendor in the domain that you are looking at?
  • Does a solution have a significant partner and implementor network? If you do not need advanced AI / machine learning, would sophisticated BI / visualization tools suffice for your purposes?

These are considerations that financial institutions must weigh when selecting and implementing next-generation QuantTech and Data Science environments.

As we stated in our study, we see forward-thinking firms deploying Data Science offerings to develop and achieve stronger organizational kinetics to aid risk assessment and business response strategies. This is underpinned by the notion that the rapid onslaught and global progression of the current (and a future) pandemic will demand a paradigm shift in terms of how firms assess, respond to, and mitigate such scenarios.

We believe that NextGen Data Science platforms will show their mettle and demonstrate efficacy for the tasks ahead, but only when financial firms exercise the right wisdom to employ them correctly. In this day and age, an urgent and versatile approach to business planning and risk management is critical.

For clients, the full report is available here: Accelerating Business Analysis & Response Kinetics in Uncertain Times: How NextGen Quantitative-Data Science Will Prove Their Mettle

We will be publishing upcoming briefing notes, reports and case studies around market trends and technology dynamics in QuantTech and Data Sciences. Please sign up to keep in touch; or if you have further thoughts or suggestions, please feel free to contact us at or with me directly at We would like to hear from you.

For further Celent resources and perspectives around COVID, please see Celent COVID Hub page.

Insight details

Capital Markets, Corporate Banking, Retail Banking, Wealth Management
Subscription(s) required to access this Insight:
Banking, >>Retail & Business Banking, >>Corporate Banking, Insurance, >>Life/Annuities Insurance, >>Property / Casualty Insurance, Capital Markets, Risk, >>Financial Services Risk
Insight Format
Geographic Focus
Asia-Pacific, EMEA, LATAM, North America