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Big Data Effects on the Banking Industry

The banking industry is poised to use AI in the near future for massive benefits

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  • Written by  Ashley Halsey
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Big Data Effects on the Banking Industry

The banking industry is poised to use AI in the near future for massive benefits. Banks have some of the biggest amount of historical data on their clients and information on transactions that can be analyzed by AI. Banks should be looking at ways to use their data to build customer interfaces to improve the experience and attract new clients. The trends for AI development at the major banks will be geared toward customer service. Here are some of the use cases showing the effects of big data on the banking industry.

Big Data and Anti-Money Laundering

There is a technology called MapR which is a data platform that can support financial services institutions to analyze and collect data for risk management purposes, as well as fraud detection, compliance, anomaly flags, predictive algorithms, and natural language processing. The MapR-DB platform lets banks archive documents on a database that can search and process using machine learning. It can also help making decisions for banks by integrating all the internal and external data and enriching algorithms for business intelligence.

Teradata is another company that provides big data analytics for financial institutions to automate their processes, thereby minimizing their financial fraud and cybersecurity exposure. This occurs because the software collects all available data and then check customer data and transactions and compare them to their previous actions. If there are anomalous patterns, the tool can predict fraudulent applications.

Big Data and Sales and Marketing

There are some companies offering big data services to help institutions with sales and marketing. For starters, Axtria’s service is cloud information management which is geared toward helping banking and financial companies to analyze their data and find out the best customers to target, boost sales and productivity, and increase workflow reporting efficiency. Axtria also helps data scientists find the right data for training machine learning software.

Data scientists typically spend most of their time cleaning and preparing the data instead of analyzing it and building new algorithms. Instead, Robert Weisland, a financial blogger at Writinity and Last Minute Writing, explains that “if a company uses machine learning tools to take on the data preparation roles, data scientists have more time to work on the most important aspect of the job, building and modifying algorithms.”

Axtria has been used in the past to help credit card companies target the right customers through their analytics by understanding their customers and matching them with the best products. Customers that have similar attributes, including their usage habits, purchasing habits, and risk levels are paired together and then sent to the appropriate products. This includes analysis of account balance, spending habits, and more. The AI tool can then predict their future buying patterns to improve sales and targeted marketing campaigns.

McKinsey is another tool that works by using banking data to predict and analyze trends in banking and equity and market to decide what best company should invest in. The dataset includes more than 60 global markets and lets banks decide what markets to go into or leave.

There are also a lot of fintech companies included in its database, which permits banks to understand the right companies to invest in and their link to their products. The AI tool that runs this software has learned more than 100 million data points about these different markets and the algorithm is set up to determine the right data points to predict the best companies or markets for investment. According to Margaret Wilson, a journalist at Draft Beyond and Research Papers UK, “this company has already helped European banks to lower their risk threshold and have better capital planning by integrating AI into the portfolio for the bank. The bank is now able to forecast revenues, analyze the impact of Brexit on their business, and make accurate budget plans.”

These algorithms and software also help banks with its future business decisions and planning for the big picture. There is also a great improvement and growth because banks and financial institutions are able to make decisions with more information and data analysis. The next few years will see an even greater shift in the financial industry as AI and big data gets more integrated in the large banks.


Ashley Halsey, a professional writer with Lucky Assignments and Gum Essays, shares her expertise about the financial sector and banking industry. She has worked on many projects with large insurance companies and financial planners and is at the forefront of the conversation about artificial intelligence and banking.

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