Published By - Jessica Marcus

Business Intelligence in Banking: Importance, Benefits and Applications

The banking sector is indeed one of the largest producers of data. The process of collecting, analyzing and extracting information contained in raw data is more advanced than ever. This is all due to business intelligence solutions, which have been gaining remarkable space within companies. Their great merit in making organizations of all kinds make decisions based on data rather than subjective opinions has made their adoption and use increasingly indispensable.

As a new concept in data analysis, big data analytics solutions are being used as important allies in improving management, as well as identifying market trends, new business opportunities, and internal process failures.

One of the industries that has benefited the most from business intelligence solutions is banking.

Importance of Business Intelligence in Banking

One of the biggest benefits of business intelligence for the banking sector is risk management. Using big data, makes it possible to cross-reference various types of data – between structured, unstructured and semi-structured – and create predictive analytics. This type of information is crucial for building a business loan and investment portfolio.

In other words, by using this information we can identify the probabilities of future events. Thus, decision making about financial investments, assignment or credit taking, acquisition or partnership with other organizations and even the purchase of shares on stock exchanges becomes safer and more grounded.

Benefits of Business Intelligence in Banking

Knowing the business deeply and mastering all its instances is crucial for the BI manager to develop good administration.

As previously said big data analytic tools, which collect and analyze huge volumes of data, are able to cross a wide range of structured and unstructured data from disparate sources – such as credit analysis systems, account history, and bank motion sensors – and correlate patterns of information about customer adoption of particular banking service.

With business intelligence, banks can ensure a better understanding of their customers and increase loyalty and trust, the BI manager will be much more accurate in decision making to deliver accurate and segmented services for each customer profile.

Some key benefits of business intelligence in banking include:

  • Strict management of risks and fraud.
  • A better knowledge of the client
  • Improved operational efficiencies.
  • Quick and accurate reporting.
  • Offers a remarkable customer experience.

Business intelligence offers banks the opportunity to become indispensable on a daily basis. Not just because they distribute the funds, but because they can provide relevant and trusted advice that customers need and use every day.

Applications of Business Intelligence in Banking

Improve Customer Service and Satisfaction

Big Data can monitor each step of its customers in their relationship with the bank. This is done by managing and segmenting the best consumers, understanding which products are most in-demand by social class, which services can be launched with good acceptance, which maximum tariff packages each customer profile would be willing to pay, etc.

Predictive Analysis

One of the consequences of the qualitative leap that involves the emergence of machine learning, together with other areas such as big data or data science have a direct consequence on the increased ability to make predictions based on previous experiences.

In a scenario marked by the competition of financial services start-ups, the ability to make predictions will be essential to provide greater adaptive power to companies in the sector so as not to lose direct contact with their client.

Fight Fraud

This is perhaps the purpose for which of the banks use the data analysis solutions. Ongoing monitoring of data centers, data networks, and banking systems can and should be done through data mining.

This process crosses all unimaginable scale access information to human understanding and in real-time, generating usage patterns for each customer. At the slightest sign of deviations, access is blocked. In addition, customer and bank receive alerts informing them of suspected intrusion attempts. It is not to seek identification of the intrusion after it occurs, it is to prevent its occurrence.

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