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The amount of data that is generated in the banking industry is objectively huge. None of the activities could be performed without being backed up by data analytics. Looking at big data, specialists are able to generate important and valuable insights and use them to improve business scalability. All of the technological developments of the financial industry allow banks to cooperate and work in tandem in order to use the received input to design a smart decision-making strategy.
Accumulating big data in the banking industry
After the crisis of 2008 that strongly undermined the credibility of the titans of traditional banking, experts realized that using big data can be highly beneficial to restore it and build trust from scratch. Due to the changing customer trends and rising competition from digital banking institutions, which have an entirely different value proposition and service range, banks were forced to digitize their operational processes and establish a feasible means for analytics. The latter included a need for a solution that would simplify monitoring and evaluation of big chunks of customer data (personal&security information). The strict framework that traditional banks were put in caused them to reconsider the entire approach to their approach. As the reliance on the technological aspect rose, the customer volumes and amount of transactions did so as well. Working with big data allows companies to use their customers’ information to track behavior in real-time and instantly respond to changes in demand. Such assessment has all the potential to drive up performance indicators and bring higher profits to the institution. The dynamics must be consistently positive in order to leverage all the benefits of the solution and gain a competitive edge.
The Key Pillars of Big Data
When talking about big data it is important to remember the main principles it is based on, which are encompassed in the 3 Vs:
• Variety refers to the types of data that is being processed. Banking institutions work and process vast amounts of data day in and day out, coming from a range of outlets from transaction details to credit scores and risk assessment reports.
• Volume refers to the space that the stored data is occupying. This has a quite technical definition as it refers directly to terabytes of data that is generated daily.
• Velocity refers to the speed that new data is being added to the database with. Simply saying, velocity concerns the volumes of transactions that banks are processing daily and all the data that they generate.
However, all of the latter does not have much context without the 4th V that refers to Value and incorporates the results of big data analysis into real business decisions.
Typically banks make strategic decisions based on the following criteria:
• Customer segmentation
• Cross-selling and Up-selling
• Feedback concerning customer service
• Designing customized offers through pattern identification
• Risk assessment, compliance & reporting that aid to fraud management & prevention
• Identification of customer channel preference for the transactions (e.g. credit/debit card payments and ATM withdrawals).
There are various ways that big data may be beneficial for banking institutions. Thanks to the analytics, they get access to new opportunities for further growth and development, in particular for the improvement of user experience and creation of customized offerings.
New Level of User Experience
In order to convince potential customers to choose a particular banking institution over another, it is important to create a unique and convenient customer experience that sometimes plays a deciding role. Big data analysis allows for high personalization and customization, based on consumer’s personal features and behavior patterns.
As mentioned previously, big data analysis provides an opportunity to create personalized marketing materials and effectively target customers.
Big data allows banks to establish an effective credit management, fraud management,
operational risks assessment, and integrated risk management. Fraud signals are detected and analyzed in real-time in order to raise a red flag at the right moment, before its too late.