Big Data: A New Era for Banks

Manon Meekel, Joost Mulders, Linsey Post

February 2017

"The financial industry faces more change in the next 10 years than there has been in the past 200". This quote by Lord Blackwell, chairman of Lloyds Banking Group, captures the expected impact of the so-called digital revolution banks are faced with. The emergence of this digital revolution is mainly due to the fact that the substantial amounts of information are very complex and the long-established tools are no longer adequate to deal with the data sets. Confronted with such large bulks of data, the buzzword "Big Data" was introduced. The term suggests not just a bit more data, but a volume that is beyond what we could have imagined. In recent years, this topic received a lot of attention, but the business value of Big Data has yet to be identified. In this article, we specifically focus on the advantages of Big Data for the banking sector and consequently how this will change the banking landscape.

What is big data?

Big Data could be defined as the tool which allows an organization to create, manipulate and manage very large data sets and the storage required to support the volume of data (Srivastava & Gopalkrishnan, 2015). Three main features of Big Data are volume, variety and velocity. Volume refers to the quantity of available data, while variety refers to the fact that information comes in many forms and the information is no longer limited to structured data collected by the financial firm itself (Connors et al, 2013). Unstructured data from social media is also key in understanding customers. Velocity denotes how fast data is processed (Inukollu et al. 2014).

The amount of structured and unstructured data is so large that the traditional small-scale databases of banks are not able to process these bulks of information. Advanced data storage, management, analysis and visualization technologies are required to deal with Big Data and Big Data analytics (Chen et al, 2012).

According to a study conducted by Capgemini, 60% of financial institutions in North America believe that Big Data can create a significant competitive advantage and eventually will even separate the winners from the losers in the future. Therefore, it is crucial for banks to unlock the possibilities of Big Data.

How do financial institutions benefit from Big Data?

Although Big Data is characterized by a large and complex volume of information, the goal of implementation is actually to simplify tasks. When a name or account number is entered into the system, it filters all the data and only comes up with the required data. This enables banks to streamline work processes and leads to both time and cost savings. (Prinzlau, 2016).

Big Data could help banks in deriving value in various ways. Evry, a Nordic IT company, elaborates on how Big Data could be utilized to control the processes of acquiring, engagement and retaining of customers.

One of the ways to derive value is by customer acquisition. Customer acquisition is a priority for banks but is becoming more complex since most financial services and products are commodities. The banking industry has shifted to the online world causing consumers to take care of their own financial matters. Brand loyalty has reduced in recent years. With just a few clicks, customers could shift their business to one of the many competitors. Nowadays, consumers choose the most suitable and inexpensive online banking option instead of seeking counsel from the local service provider (Gutierrez, 2014).

However, due to the presence of Big Data, banks are able to understand the behavior and preferences of customers. Personal interactions with customers are no longer needed, since the process of acquiring customers takes place digitally (Gutierrez, 2014). Banks are provided with such detailed information to analyze that they are able to align the right financial product with the right customer or even offer customer products they did not know they wanted or needed.

Big Data could also play a role in keeping customers engaged. It could be a useful tool for banks for the segmentation customers. By dividing clients into groups with similar characteristics or behavior. This way, banks can create marketing and loyalty programs as well as different pricing for each group based on their characteristics (Evry, 2014).

Providing a next best offer is another feature of Big Data that could drive customer engagement. This would imply analyzing a client's purchasing behavior and adjust the kind of offers as well as timing of these offers that the client receives (Evry, 2014). This could for example mean that the bank notices a cyclical pattern in a client's funding need, prompting them to offer a working capital loan to the client. Big Data could also be used to offer clients a more complete customer journey. By finding a pattern in channel usage, banks can use this to serve their client through the optimal channel, improving conversion and effectiveness of marketing campaigns (Evry, 2014).

Lastly, Big Data could contribute to retaining customers that might want to transfer or withdraw their money. By the use of Big Data, banks can build more sophisticated models of customer behavior. By doing this, an in-depth profile of a customer can be constructed by examining the actions clients undertake. The actions of your customer could then predict what their next actions might be, and subsequently a bank could take action accordingly. For example, a client might be about to stop doing business with the bank and move their funds elsewhere. To prevent this from happening the bank could step in on time and tackle the problem before it even arises (Evry, 2014). Besides, receiving feedback from customers about the services has also become more convenient. Examples of this include feedback buttons on the bank's mobile app, or the social media presence of the bank. Features like these enable a customer to give direct feedback to the bank about their services.

In short, it all comes down to knowing your customer and boosting customer engagement by the means of data intelligence. The valuable insights in the customer point to a more customer centric approach and could bring about increased revenues.

In addition to monetizing these customer insights, financial institutions can also take advantage of Big Data with respect to customer risk analysis. Models of customer behavior and spending habits provide banks with a comprehensive view of customer risk. In that way, Big Data enhances risk management for the lender (Connors et al, 2013).

Another compelling application is that when used right, Big Data can detect and even prevent fraud. Since banks are often targets of cybercrime, detecting fraud in an early stage could lead to potentially big savings for the bank and raise the level of safety and security in the banking industry (Prinzlau, 2016). An example of how to prevent fraud by the use of Big Data is the introduction of the Incident Warning System. This system contains personal data of individuals and legal entities who are involved in fraud or are considered as a threat to the financial system. With this system, banks are able to verify whether future customers or employees are registered as such intruders. According to the Dutch Banking Association, the system was consulted more than 13 million times in 2015, and it occurred that further investigation was needed 35,000 times (Dutch Banking Association, 2016).

Important to note, is that when threats occur, there is not only financial loss at stake, but also customers losing trust in their banks.

Big Data technologies can contribute to a secure banking system through the following ways: understanding activity patterns among customers, sharing of data and increasing reliance on data to predict attacks based on trends that are targeting the industry (Gutierrez, 2014).

Challenges for implementation

For successful implementation of Big Data, a few challenges have to be addressed.

First, most people are concerned with privacy matters. In connecting the dots between different chunks of information personal information could be revealed. Also, outsourcing data analysis means that data has to be distributed and this is accompanied by security risk. Concerns about data privacy puts off customers and causes banks to be cautious in the implementation of Big Data (Capgemini, 2014).

Second, Big Data analytics require new skills. It's about the combination of the IT-side, so understanding Big Data analytics, and communication with decision makers. It appears that these resources are not widely available. Besides, the end-users of Big Data also need training to understand how Big Data could contribute to their final decisions. (Capgemini, 2014). Working with Big Data goes beyond having access to the data and it is also about asking the right question of the data.

Lastly, banks need to incorporate Big Data into their strategy. Big Data is mostly viewed as a new IT-project, while it should be considered as new ways to generate income streams; it is a business opportunity (Capgemini, 2014; Connors et al, 2013). Big Data will succeed when it is not an isolated project but incorporated into the strategy of the bank. This could be challenging for implementation since it requires a cultural change.

Conclusion

As we discuss in this article, Big Data is bound to become vital for the banking industry in the next few decades. Using big data will enable banks to better understand and help their customers, as well as keeping the banking industry safe from various security threats. Applying Big Data on a large scale might take time, since there are hurdles to overcome. Banks should not jump into Big Data without addressing issues such as privacy concerns. However, we are convinced that this innovation will transform the banking landscape for the better and will be a critical determinant for the success of financial institutions.


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