Inthe banking industry, there are a lot of transactions and services happeningdaily, hence huge volumes of data are being generated. To stay ahead of thecompetition, banks are constantly analyzing those data collected as part oftheir strategy to gain a competitive edge over the other competitors. Oneexample would be monitoring their customer’s transaction behaviours constantlyin real time and providing resources to them immediately which will boostoverall profitability.
Functions of data analytics: Curb fraudulent transactions – One of the major concerns banks like Citi are facing is actually dealing with fraudulent transactions. Banks uses data analytics to differentiate legitimate and fraudulent transactions made, providing a higher level of safety and raising security standards. Customers’ transactional behaviour history is used to distinguish from unusual behaviour indicating fraud that does not match with usual documented pattern and will then raise an alarm to the bank. Easy customer segmentation – As Citibank have a huge database of different customers with all kinds of different financial requirements, financial behaviours and spending habits, data analytics can help to categorise customers into distinct segments based on various parameters such as daily transactions, commonly accessed services and preferred credit card expenditures etc. Segmentation would benefit the Citibank in targeting certain segment of customers for different marketing promotions and improve customer relationships without spamming every customer’s inboxes. One example would be that the Citibank does not need to call every customer with an offer for a loan anymore, eliminating the time and efforts wasted on non-eligible persons.
Manage risk – Risks faced by Citibank may vary, from fraudulent activities to bad loans or even failed investments etc. Early detection of such risks is an important factor in risk management and can help to prevent huge losses from incurring. With data analytics, it is easier to manage and reduce the amount of risks. One example is to apply predictive analytics to customers’ social data that can help in identifying potential defaulters, and then take appropriate measures to mitigate the risk. Featuresof data analytics: Predictive analysis – Citibank aims to offer services that surpasses other banks, by learning not only their customers’ current needs but also their wants in the future so as to be ready with the right products and services. Gain customer insights – Data collected from social media such as monitoring of posts, number of likes and comments provides bank with how customer perceives their new products and services or existing products and services.
With the use of analytics to drill down that data, invaluable information to business problems could be provided which will in turn, make customers feel more engaged with the bank.Customer retention – Citibank, just like any other banks tries hard in keeping theircustomers which would then results in one less prospect within competitor’sgrasp as well as acquiring new customers by offering promotions and benefitsthat people likes to attract them.Machine Learning – Citibank uses machine learning algorithms while analysing data totarget promotional spending and keeping customers protected from cyber-securityto customer service, fraud detection, marketing and web analytics providingnewer functionalities. Spotting anomalies through predictive modelling – With predictivemodelling, Citibank can identify and spot anomalies quickly and even predictdefects before it happens with the use of data analysis.Remove unwanted data – As massive amount of data are being added into the database daily,Citibank should accommodate the ever growing of bundles of data by removingunwanted data as much as possible, especially those not necessary ones.Improve data analysis techniques – SQL which currently isthe standard tool used for data analysis. However, Citibank can upgrade toboost the current data analysis structure by using emerging tools such asSpark.