How is data analytics used in banking?
The banking sector is obliged to change methods to get to know its customers better and improve cooperation to cope with significant and rapid changes in their economic environment. In such a highly competitive industry, banking institutions see data analytics as a real opportunity, especially since online banking. Data analytics allows them to find new levers of competitiveness to develop customer relationships and thus return on investment. Therefore, the time has come for the banking industry to move to a higher gear and align its business models.
Data processing is not banks’ core business, but they have always been created and used to provide their services. While previously banking institutions did not have the technical means to use data, which has not stopped multiplying tenfold in recent years, the advent of new technologies such as artificial intelligence and the increase in storage capacity now allows them to take advantage of this new black gold.
When bank customer retention rates are becoming more volatile, data analytics is becoming more and more important to differentiate yourself from the competition. The challenge will be to process all this mass of information. However, the benefits are not the smallest. Customer knowledge will allow banks to:
- Understand customer needs (in terms of the product, but also the use of different digital channels)
- Offer products and services that are customized and personalized for each customer according to their application
- Improve bank decision making
Data and banking: What are we talking about?
Data analytics in finance refers to petabytes of structured and unstructured data that can predict customer behavior and create strategies for banks and financial institutions. The banking sector is one of those with the most significant data volume. This data comes in many forms, including:
- structured data: cash withdrawals from ATMs, payment by credit card, browsing the account on a smartphone
- partially structured data: customer journey on the web, e-mails
- unstructured data: call point with a bank advisor, call center
Therefore, data analysis opens up new horizons for the banking sector while at the same time providing a real opportunity to increase turnover. Currently, banks are increasingly using data analytics consulting to use and compare a lot of structured and unstructured data to monitor the quality of their services and identify possible causes of customer leave.
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Data analytics: Specific use cases in banks
Banks around the world better understand big data and the possibilities that data analytics opens up for them. Financial institutions were not born into the digital landscape: they had to undergo a long conversion process that required behavioral and technological changes. In recent years, big data in finance has led to significant technological innovations that have resulted in practical, personalized, and safe solutions for this sector. Let’s look at some practical examples.
PERSONALIZATION OF CUSTOMER EXPERIENCE
In an economic context marked by intense competition, banks seek to retain their customers and establish close ties to reduce the churn rate. Therefore, advertising targeting is an excellent, even textbook example of data analysis by entities in the banking sector. For example, potential clients looking for information about real estate on the Internet can be offered advertisements for real estate loans. In this way, data analytics enables banks to precisely personalize their relationships with customers, being as close to their needs as possible.
Previously, fraud identification relied on fragmented detection systems and procedures requiring the client to undertake additional verification. That significantly worsened the quality of customer service. Moreover, these traditional tools often fail to detect untested frauds, which the cardholder cannot prove or that he notices too late. Currently, the risk related to the security of credit cards has been reduced thanks to analytical processes that interpret shopping habits. If credit card details are stolen, banks can immediately block the card and transaction and notify the customer of the security threats.
A better understanding of their customers also allows banks to protect themselves against payment arrears. By examining consumer behavior (bank and non-bank consumption), financial organizations can set strong trends that will enable them to predict customers’ actions, particularly those that will consist of withdrawing funds and possibly changing banks. In addition, significant financial decisions such as investments and loans can be based on reliable data thanks to data analytics. The calculated decisions based on predictive analytics consider everything from economics to customer segmentation and business capital to identify potential risks such as inadequate investments or bad payers.
IMPROVED CALL CENTER SCENARIOS
When a customer calls, the consultant should access all relevant information about the clients’ journey. Among other things, the consultant should know that, for example, the day before, the client simulated two consumer loans on the bank’s website or that he uses a smartphone or tablet more often to contact the bank. Based on that information, the consultant knows that the loan proposal should be sent to that customer by e-mail, not post. Also, the ability to determine where the client is in the decision cycle is beneficial. Data analytics allows customer service professionals to find out about the best way to serve various customers.
Data analytics consulting in the service of the banking sector
For banks to be able to use the potential of their data entirely, they must, on the one hand, surround themselves with appropriate resources and, on the other hand, rethink their organization and obtain the proper technology. Very often, it is not access to data that is a problem, but its skillful use. Building bridges between data, business goals, and actions is frequently complicated based on your analysis results. That is why institutions of the financial sector, especially banks, use the services of experienced data analytics consulting companies.
Each financial organization has its level of use and capability of big data. Data is becoming the second currency for financial organizations that need the right tools to monetize it. Teams specializing in data analytics consulting help companies worldwide use the massive amount of collected data to extract useful information from them. Data analysis is an excellent way to protect, develop and optimize the company’s bank processes through the most advanced, high-level technologies possible on the market.