Data Analytics in Commercial Banking

Commercial banking has traditionally been a relationship-driven business. This meant that the business was primarily driven by human-to-human contact. Over the years, banks have acquired the capability to store large quantities of data and then mine the data in order to discover meaningful trends.

There are many modern software tools that allow banks to use the large volumes of transactional data that they generate. These types of tools are generally classified under the heading of “data analytics”

Banks have started using data analytics to gain a sustainable competitive advantage over their competitors. In this article, we will explain the various ways in which commercial banks have started using data analytics in order to improve their operations.

  • Market Research for Products and Services: Commercial banks are always on the lookout to leverage their technology in order to create new products and services which can be offered to corporate clients as well. Data analytics help commercial banks in doing so.

    Commercial banks have access to large volumes of transaction data on behalf of their clients. Data analytics can be used to process this data in order to find meaningful trends which can then become the starting point of research for the product development team. It needs to be understood that financial innovation is the need of the hour for commercial banks who are facing significant challenges from fintech companies who are innovators in this space.

    Banks can also use the high volume of data generated in order to segment their customers into various groups. This segmentation helps the commercial banks to better predict the types of products and services which will be preferred by the corporate customers.

  • High Degree of Automation: Commercial banks can use advanced data analytics in order to better their own operations as well. Banks have been using data analytics to automate tasks such as disbursement of funds as well as underwriting of loans.

    Instead of the decision being made by humans, it can be made in an automated fashion. This helps banks save costs as well as shorten the decision-making cycle. Ultimately, lower costs and faster service times can become a bank’s value proposition in front of prospective customers and can help the bank increase its revenue in the long run.

  • Increased Personalization: Commercial banking has relied on relationship bankers in order to understand the business of their clients. They then use this information to develop personalized offers which are able to add maximum value to the business of their clients. However, the problem is that the entire process is driven by human insight. Hence, if a relationship manager fails to recognize an opportunity, the bank is not able to capitalize on the same.

    There are modern software tools available that can perform the same tasks which are performed by a relationship banker. However, these tasks are performed in an automated manner. Hence, the element of human intervention and the related risks are significantly reduced. Thus, data analytics have the potential to lead the banks toward the concept of mass customization wherein commercial banks can offer personalized offers to every corporation that they provide service.

  • Predictive Intelligence: Banks can deploy software with machine learning as well as artificial intelligence capabilities to analyse the data generated by the clients in order to make financial predictions.

    For instance, the software may be able to help clients identify any fraudulent transactions by reading the patterns which generally lead to such transactions. Similarly, it may be able to help corporate clients identify any upcoming liquidity shocks as soon as possible. They may also be able to foresee future credit needs and may help the corporations make educated decisions based on the predictive capability of the software.

    Predictive intelligence can help create a win-win situation for both parties. On the one hand, it can help clients get better advice whereas, on the other hand, it can help create cross-selling opportunities for the bank. Overall, it can help commercial banks truly engage in consultative selling.

  • Improved Risk Management: Commercial banks can use the predictive capabilities of the data mining software in order to ensure the safety of their capital. Data analytics can help companies identify patterns that can predict default. As a result, banks can start working with their clients to restructure the loan or take any other measure to ensure that the loan does not reach the non-performing asset stage. Commercial banks can also adjust the disbursement of more credit based on the latest information available to them.

The bottom line is that data is the new gold in today’s world and this principle holds true in the commercial banking industry as well. Commercial banks have the financial strength required to invest heavily in information technology which can help them gain a competitive advantage over their peers.

It is a well-known fact that data analytics is going to revolutionize the banking industry in the future. It is for this reason that banks have already started investing heavily in this technology.



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