Transactions made by credit card users round the clock generate data at great speeds. The large bulk of data is referred to as “big data.” Big data grow at non-stop rates and are often products of data mining. To make analysis an ease, big data are categorized as unstructured or structured. Structured data are information already contained by organizations in their database. They usually come in the form of numbers. On the other hand, unstructured data is information that lacks organization and does not fall under any existing format. This type of data includes information gathered from social networking platforms, a common source of consumer insights.

How do financial institutions use big data?

The financial services industry commonly refers to big data as customer analytics, real-time analytics, or predictive analytics. However, the financial industry terminologies differ in meaning. Customer, real-time, and predictive analytics are specific data analyses in the financial sector on existing big data to recognize patterns and increase business value. Information gathered from analyzing data can open paths to new businesses, improve customer satisfaction rates, gain insights on what today’s customers are looking for, and put customers under specific categories.

Along with the medicine, transportation, construction, and retail sector, the banking sector is on top of all other industries when it comes to big data maximization. Bank of America (BOA), an American multinational investment bank and financial services provider, has engineered and launched the artificial intelligence-powered Erica. Erica is a virtual assistant geared towards helping the financial institution’s customers view the history of their bank transactions and billing information. Artificial intelligence (AI) can analyze large amounts of data through machine learning from data inputs. The case of BOA’s Erica is based on this specific ability in AIs. According to BOA, as its customers continue to utilize Erica’s features, it gets smarter.

Banks also use big data to reduce instances of fraudulent activities. Identity fraud is one of the most prolific types of fraud. In 2019, over 14.4 million individuals consumers became victims of identity theft. In the US alone, 33% of adults have been victims of this type of fraud, twice more than the global average. Big data generated from customers and their transactions play a part in fraud prevention. An example of this is when a customer informs the card issuer about an upcoming trip abroad. This is done to make sure that transactions that require authorization are user-initiated. Banks verify the location and date of travel to compare with transactions that will be initiated during the dates provided by the card user.

How is big data used in profiling?

The main use of big data within banks is to create various consumer segments. Big data helps provide the information needed to make detailed client profiles that provide banks insight into the unique but grouped characteristics based on factors such as the customer’s demographic, the number of bank accounts under their name, their relationship with other existing customers, behavioral and service patterns, and offers they have rejected and accepted. The information is used by financial institutions to curate offers, promotions, and services that are likely to be utilized by the target consumer segment.

Another application of data analytics in the banking sector is credit card scoring or FICO. Credit card scoring is the measurement of a borrower’s creditworthiness based on credit history. Credit history includes repayment history, the total amount of debt, the number of accounts under the borrower’s name, and other factors. Using credit scores is for potential lenders to evaluate the debt payment probability of a potential borrower.

How does data security factor in?

data security

The amount of personal information in big data makes it a prime target of hackers. In 2019, Capital One had a major data breach that affected approximately a hundred million people in the US and six million in Canada. The data included social security numbers and bank account numbers. This poses an undying threat to consumer data. However, industries, especially the financial industry, have mechanisms to protect and recover from such attacks to stored consumer data.

How do consumers perceive big data generation?

A study by Pew Research Center found that 91% of adults in America agreed that consumers had lost power over how their personal information is gathered and used by companies. The same study also found that only 37% is confident that their credit card providers have the ability to keep their data secure.

Big data is used in consumer profiling, security, and the enhancement of customer service. They function to provide businesses insights on what consumers want and need. This then helps businesses come up with strategies to increase revenue and to attract customers. Insights from social media are especially helpful because trends usually rise from their platforms.

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