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Practical applications for analytics in financial services

Practical applications for analytics in financial services Analysts, technologist, consultants, media and vendors are all mouthing the benefits of analytics tools. This near incessant bombarding of messages is creating a self-sustaining demand for the technology. But outside the hyberbole of benefits and claims of ease-of-use, there remain unanswered fundamental questions like fit-for-need, the right tool for the right job, among others.


Toos Daruvala, a director in McKinsey’s New York office, believes that banks today have a rare opportunity to reinvent themselves with data and analytics. “Every single major decision to drive revenue, to control costs, or to mitigate risks can be infused with data and analytics,” he commented.  He further predicts that the use of data analytics as a differentiator for some period of time.


Fintech Innovation recently touched based with Jason Demby, director of business development, financial services at Datameer, for a practical discussion on the application of analytics in financial services.

In a nutshell, describe the fundamental function of an analytics tool as it applies to banks?

JD: The fundamental function of an analytics tool as it applies to banks is to provide increased transparency, insights, and answers to business questions based on data.  This data could be big or small, structured or unstructured, human generated or machine/systems generated–and relates to the full front-to-back operations of the bank.  This ranges from understanding customer behavior across various channels to optimizing back office operational, compliance, and technology functions.

Are all analytics tool equal?

JD: The market for analytics tools is extremely crowded, and there are a number of factors that differentiate the value of each tool.  The major factors include the following:

1. What questions are you trying to answer?

2. Who are the intended users of the tool?

3. What is the size and shape of the data being analyzed?

·        Some data analytics and visualization tools are great for a single user trying to answer a relatively simple question on a small amount of data on their desktop.  As the data grows and the questions become more complex, the need for more advanced analytics solutions and infrastructures also grows. 

Companies are increasingly moving to Hadoop infrastructures to handle large amounts of structured and unstructured data and facilitate the ability to process data at scale and with speed.  Analytics tools that can enable advanced analytics and take full advantage of the power of Hadoop are becoming more prominent in the market.  While these tools are being deployed–it is also important that they enable a wide range of users with varied skill sets. 



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