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13 Use Cases for Data-Driven Digital Transformation in Finance

Financial institutions can seamlessly integrate digital technologies with data-driven insights.
Nov 2020  · 4 min read

Over the past decade, big data and digital technologies have disrupted industries and consumer behavior alike. IDC and Statista estimate that the volume of data generated yearly rose from two zettabytes in 2010 to 59 zettabytes in 2020, marking a thirtyfold increase in data generated in the past 10 years alone (Statista). This data deluge is only expected to grow, with projections predicting 149 zettabytes produced yearly by 2024.

Data in the financial services industry

While various industries are vying to take advantage of the data deluge with business intelligence, data science, and machine learning, the financial services industry is best equipped to benefit from big data. Data is at the heart of the financial services industry—across retail banking, investment banking, and insurance. Financial services organizations produce and store data on their customer transactions, detailed customer profiles through compliance processes, insurance claims, stock market exchanges, and more. The amount of data generated is astounding: The New York Stock Exchange alone produces one terabyte of trade data daily (Investopedia).

Real-life examples

We’ve already seen fintech startups take advantage of shifting consumer behavior and the financial industry’s data deluge. Digital banks such as N26, Revolut, and Monzo abandoned the brick-and-mortar model and opted for a purely digital banking experience, relying on data to improve user experience and automate workflows (Revolut). Klarna, Europe’s most giant fintech unicorn, provides interest-free installment options with automated approval or rejection using machine learning (CNBC). The data deluge has not only opened up space for disruptive, innovative services—it’s opened the door for data-enabled digital transformation across the industry.

A digitalized financial industry

Disruptive digital-first startups across all industries have prompted many incumbents to invest heavily in digital transformation. The financial services industry is no exception. An Accenture and Oxford study in 2018 found that 87% of retail banking executives have developed a long-term plan for technology investment and digital transformation (Accenture). This is especially true in the COVID-19 economy, which has moved consumers purchasing online and accelerated digital transformation programs across all industries.

This acceleration is exceptionally pressing in the financial services industry. A recent study from the Economist Intelligence Unit cites that 45% of banking executives believe building a “true digital ecosystem” is the best strategic response to the pandemic. In the same survey, 66% of respondents believe that new technologies such as machine learning and artificial intelligence will bring the most significant impact on the banking industry by 2025.

Taking an example from the ground, the urgency of using contact-less financial tools ushered an 84% increase in Citibank’s daily mobile check deposits, and a tenfold increase in activity on Apple pay (Forbes). This has prompted Jane Fraser, president of Citigroup and CEO of its consumer bank, to declare, “Banking has changed irrevocably as a result of the pandemic. The pivot to digital has been supercharged. [...] We believe we have the model of the future—a light branch footprint, seamless digital capabilities, and a network of partners that expand our reach to hundreds of millions of customers.”

White paper: Digital Transformation in Finance

The success of such digital transformation programs pivots on the seamless integration of digital technologies with data-driven insights and high-impact data science use-cases. What are these high-impact use cases and what are the challenges standing in the way? In our white paper, Digital Transformation in Finance: Upskilling for a data-driven age, we dissect 13 high-impact use cases spread across domain and sector and the challenges large financial institutions face to becoming data-driven.


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