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Data Analytics in Banking

Banking is getting branch-less, contemporary and digital at a very fast pace. As banks compete to gain competitive advantage, the need for managing big data and analytics becomes more relevant.

Big Data has transformed the way traditional banks worked in the past and has been very helpful in informing decision-making. Through associated big data tools, banks can gain greater visibility into customers’ behaviors, assess the probability of risk and help small businesses. Big Data combines various data sources like the company, its channel partners, customers, suppliers, social media and even external data suppliers. Typically, the data collected in banks is so complex that it is beyond the ability of any traditional data software tool to manage it. Analytical tools solve this issue of storing, managing and analyzing complex and large data. With its increased accuracy and efficiency, banks are starting to realize Big Data’s value and are slowly adapting to this new change. For example, Wells Fargo has been able to cut the time spent on reshaping data and now uses that time to analyze it.

Improved Customer Relations

Banks have moved from traditional to digital banking and are trying to gain a larger customer base by widely advertising new ways to bank with them. A bank’s customer base can be voluminous and gets more complicated with a number of different financial products that the bank sells. These include mortgages, car loans, home loans and other financial products. Traditionally, when a bank tries to sell its products to its customers, it completely ignores the fact that the product may be irrelevant to the customer. In modern banking, it has become important for banks to remain updated on the preferences of their customers.

Data Analytics in Banking

Big Data helps banks create relevant databases that could potentially be of value. The transition can help a bank shift from traditionally structured datasets that hold little value to more appropriate ones. By successfully accumulating Big Data, a bank can understand its customers’ habits and lifestyles. Through social media and integrating data from online and offline channels, banks can gain more knowledge about what kind of financial products are appropriate for their customers. Big Data and customer analytics can help maximize the value of available customer data by combining transactional, behavioral and social data.This leads to higher customer satisfaction since the banking experience for clients will be more customized and relevant than it was previously.

Banks can also become more efficient and save time by targeting the right customers with the right kind of product. Banks can use Big Data technologies for not only improving the customer’s experience but by creating an environment where they can tactfully make quick alterations in case a customer shifts their habits and/or lifestyle. By keeping track of deviations in demand, banks can get more organized. Big Data analytics allows banks to look at the past buying behavior, demographics and sentiment analysis through social media in real time. All this helps improve the customer experience and gains the loyalty of the client. Big Data Analytics also helps banks limit customer attrition so that an early identification can save banks from suffering huge losses, even if it comes at a certain cost. The world’s largest bank, Wells Fargo has invested millions of dollars in Big Data in order to enhance customer experience and mitigate risk. With over 70 million customers and 8700+ locations, it aims to understand the customer journey and make data-driven decisions.

Internal Controls and Risk Management

Risk management remains a high priority across banks since banks are going through rigorous regulatory requirements. A risk is best assessed with more information in hand and Big Data can help in efficiently managing such risks. As banks become more diversified with their products and expand globally, the risks associated with their activities also increases. According to Economist Intelligence Unit Survey in 2014, markets are so interconnected and volatile, (especially during an economic downturn) that information can travel within seconds and create a market disruption. With strict compliance and regulatory framework, banks need to document each swap trade it does. Big Data helps in implementing this key provision of the Dodd-Frank Act by using a ‘deal monitoring system based on a new generation of data technology.’

In a survey conducted by Ernst and Young, Big Data has made its way into ‘compliance, internal audit and fraud-risk management related publications’ and 72% of respondents firmly believe that Big Data technologies can address the issue of fraud management. Small data sets can overlook rare events where any kind of fraud occurs especially if the event is infrequent. But through Big Data technologies, banks can keep track of the smallest of rare discrepancies by using predictive analytics.

The integration of advanced technologies can help banks reduce credit risk and help them make better decisions based on thousands of risk variables. IBM’s big data and analytics platform enable the banks to manage credit risk and avoid situations of default. Real-time fraud detection through data and analytics tools can help prevent credit and liquidity risk as it could keep a close supervision on borrowers in order to predict a loan default. Analytics solutions can help in making informed decisions that are entirely based on risk analysis and transparency. High-risk accounts can be detected using big data and a good example of that was seen by Bank of America. The Corporate Investment Group (CIG) is responsible for calculating the probability of default (PD) on 9.5 million mortgages which helped Bank of America forecast losses arising from loan defaults. The bank was also able to increase its efficiency through reduction in loan default calculation from 96 hours to just 4 hours.

Data Analytics in Banking

Helps Small Businesses

Small businesses have been an important part of economic growth in the US.  Small businesses have partnered with banks to take advantage of big data. By doing so, small businesses get a clear insight of the potential competition they face in the marketplace. Big data tools like SizeUp(used by Wells Fargo)  help small businesses compare the salaries of their employees in their industry. This competitive intelligence tool helps small businesses make smarter decisions on their next advertising campaign and where the maximum value can be achieved. Smart Data helps small business owners determine whether their business is under performing or outperforming in the industry. Hence, through big data, banks can expand their services and gain a broader clientele base.

Challenges and Opportunities

Banks have realized that big data technologies will help them not only perform better but will help strengthen their defenses against high-tech attackers. While most banks are adopting new technologies, many still remain in the experimental stage. According to a customer survey by Capgemini and EFMA in 2013 in North America, 60% of financial institutions felt that Big Data could be a competitive advantage, but only 37% had practical experience in managing Big Data and still others remain in their initial phase of experimenting with customer analytics.

While adapting big data remains a matter of choice for many, alarming mega-breaches have lowered the effectiveness of Big Data and analytics. Issues of customer privacy have also been a concern. Due to rising security breaches, banks need to apply new approaches, risk rules and strong defense mechanisms on a much larger scale than the current ones. The biggest challenge in applying Big Data technologies is that the smallest oversight in the organization can lead to the loss of volumes of customer data and this digital disruption can have adverse effects.  Any security breach comes with a huge cost to banks, which in turn affects their reputation and customer relations.

Data and analytics tools should be used with due diligence and efficacy by the appropriate team of information security professionals. For example, in February 2014, Wells Fargo hired Charles Thomas after creating the new position of a “Chief Data Officer” to supervise the data strategy. Other banks like Citigroup and HSBC are looking to create similar positions to capture, crunch and analyze hidden data that could be very valuable through Big Data.

With an increase in fraudulent and cyber crimes, Big Data and analytics should be looked at as more of a compulsion than an option for banks.