Data Science and Banking

August 3, 2020

Data Science and Banking

Data stored on spreadsheets and displayed using two-dimensional pie charts and line graphs were once seen as the best options for data analysis. Advances in technology and the increasing power of analytics for data analysis
has modernised the data science field. The increased use of coding languages and business intelligence programmes such as R, Python, Apache Hadoop, NoSQL, Power BI and Tableau within the data science field are changing the way information is collected, analysed, displayed and used for decision making and forecasting.

The world is moving from focusing on volumes of data, to appreciating the value of data and the positive spinoffs of data analytics and predictive analysis are increasingly recognised. Advocates for the data science field view data as the ‘new oil’, as computing power is driving machine learning and transforming the business environment. Data science involves the blending of algorithms and machine learning to analyse structured and unstructured data sets to extract useful information and draw conclusions. Data science is used in fields such as sport, health care, politics, business, economics and finance, as actionable knowledge is extracted from large data sets (big data) and analysed to reveal patterns, trends and associations. Data science extends from data collection and organisation conducting analysis and acquiring insights, and ultimately to identifying relevant questions and then developing and implementing practical solutions.

Financial institutions can grasp the enormous potential by incorporating analytics into their business operations.
Deloitte has noted that insurance companies have used predictive algorithms and data analytics to improve customer segmentation, develop targeted offers, enhance risk assessment in underwriting and reduce the cost of claims. Banks have also increased savings by combining the use of algorithms to predict the cash required for ATMs with route-optimisation techniques. Credit losses can be reduced with the use of digital credit assessment, advanced early-warning systems, next-generation stress testing, and credit scoring analytics.

Machine-learning algorithms have also been used to improve predictions of customer behaviour and demand for financial services. The increased analysis of customer behaviour such as spending patterns can also improve the detection of fraudulent purchases involving credit cards or unusual ATM withdrawals. Deeper and more detailed profiles of customers, together with transactional and trading analytics, can also improve the acquisition and retention of existing customers. For example, in the article “Analytics in Banking” by Garg, Grande, Miranda, Sporleder and Windhagen it was identified that a bank improved its commission, revenue of its merchants and value to its customers by using the data obtained during credit card use at its terminals and those of other banks. This data was then used to develop offers that gave incentives to customers who made regular purchases from one of the bank’s merchants.

In conclusion, the potential for improvement in profit margins, return on investment, return on equity, risk modelling and fraud detection are recognised as part of the increased benefits of leveraging new technology and utilising the tools of data science in banking and finance. Skilled data scientists are now increasingly valuable to companies. As a result of this recognition, the demand for skilled data scientists and the need for individuals to train themselves to command powerful analytical tools to advance in this now highly demanded field, are evident.


Chen, T. (2019). Do you know your customer? Bank risk assessment based on machine learning. Applied Soft Computing, 105779. doi:10.1016/j.asoc.2019.105779

Das, S. (2016). Big Data’s Big Muscle. Finance & Development, 53(3), 26-28. Retrieved from

Deloitte. (2018). Insurance Analytics: Organizing Analytics capabilities to get value from Data Analytics solutions. A
Deloitte point of view on Data Analytics within the Dutch Insurance industry. Retrieved from

Garg, A., Grande, D., Miranda, G., Sporleder, C., & Windhagen, E. (2017, April). Analytics in banking: Time to realize
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Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2018). Stock price prediction using support vector regression on daily and up to the minute prices. The Journal of Finance and Data Science, 4(3), 183-201. doi:10.1016/j.jfds.2018.04.003

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The Economist. (2017, May 6). The world’s most valuable resource is no longer oil, but data. Retrieved from


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