Big Data in Banking: Analysing its Role, Advantages And Challenges

Introduction to Big Data

Data is an important resource. However, if you do not have the right means to process it, then there is not much that you can benefit from regarding its value. Nowadays every organisation today including Banking is data driven, because in order to make effective decisions data analytics and insights are essential.  Big data primarily refers to the data sets that are too large or complex to be dealt with by complex data processing complex software. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, search and updating. Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate manage and process data within a tolerable elapsed time.

Characteristics of Big Data

Big data can be described by the following characteristics:

• Volume: – This is the amount of data that is produced or received by the company in a day. It refers to the quantity of generated and stored data. The size of the data determines the volume and potential insight, and whether it can be considered big data or not. The size of the data is usually in terabytes and petabytes. This can also present a big challenge because it would take an unreasonable amount of time to analyse the data if it is done manually.
• Variety: – Earlier the existing technologies were capable of handling structured data efficiently and effectively. However, with the change in the type and nature from structured to semi structure or unstructured challenged the existing tools and technologies. Data can come from various formats and sources.
• Velocity: – The big data has to be available as close to real-time as possible. The faster that the right people can get to the data, the better advantage they will have to make good decisions for the business. The speed at which data is generated and processed to meet the demands and challenges that lie in the path of the growth and development. The information that one’s collects even an hour ago could end up losing its relevance by the time one can do anything with it. Big data is available in real time as compared to small data and it becomes very difficult to process the Big Data.
• Value: – Value refers to the profitability of information that is retrieved from the analysis of Big Data.

There are three main types of data

a. Structured Data: – It has a predetermined format and length, the pieces of information that come with the structured data are the ones that can be sorted, grouped and organized quickly. A good example of this is what you can find when looking at databases like Access and AQL

b. Unstructured Data: – It is a type of data which does not have a predetermined format. It is very hard for an organisation to be efficient when going through the information. Some of the examples of this would include documents, emails, social media posts, videos and photos.

c. Semi structured Data: – It is a type of data that will not fit into relational databases or data tables, but it will contain some attributes and lags. It is often called as self-describing data

Advantages of Big Data in Banking

Big data can provide an organisation with valuable data that they can use for risk analysis. With the help of the Big Data, organisations can create demand forecasts and supply planning in anticipation and mitigating any variance to resource availability. It plays a very important role in any organisation in improving the operational efficiency. Organisations can collect data and can create model that they can use to improve the efficiency of the organisation if the information is used properly. Big data can help the organisation for better revenue opportunities and whether they are good ideas or not. With an organisation that is trying to grow it can be a big deal. It will help them in improving how efficiently they can do the research and development which helps them in choosing the right products that helps them to get the best results with their customers. Further Big data can help the organisation to improve their customer service.

• Management of Risk efficiently to prevent frauds and risks

With the help of Big Data analytics Banks can analyse market trends and decide whether they should lower or increase the interest rate. With fraud detection algorithms, banks can easily identify customers who don’t have good credit scores and not provide loans to them.

• Personalized Banking solutions

With the help of Big Data Analytics, it can help Banks in knowing customer behaviour based on the inputs received from their investment, expenditure trends, or financial background. It helps Banks to retain customers and also attract many more.

• Optimization and streamlining of Internal process

The use of Big Data allows Banks to optimize and streamline their internal processes, thereby boosting their performance and reducing internal costs.

• Analysis of Customer feedback

As we all know that Banks customers care are clogged with many queries regularly. Big data tools can help in going through high volume of data and respond to each of them swiftly and adequately. It will definitely increase the satisfaction level of customers as they will feel that their feedback is taken promptly. Based on the feedback received the organisation can improve their operations and the services that they provide to the customers according to the information that they received.

Challenges of using Big Data in Banking

While there is a lot of great benefits that come from using Big Data, there are also some risks associated with it, Data analysts and engineers who won’t conduct the proper design and analysis create inadequate data, with a wrong analysis. It ends up with the wrong data that would be used for the decisions in the business. If the information is read in a wrong way, it would create a big loss of resources for the business and would result in many other problems.

The other risk is of Big Data being stolen for nefarious and fraudulent purposes. If this would happen on a large scale for business, it means that the customer will stop trusting the company and money would be lost as well and this can be hard to regain in the future.

Conclusion

It is important for any organisation to realise that while one can get a lot of information out of Big Data, having access to it is not necessarily enough to gain a competitive advantage through data science. Context is just as important, as it gives meaning to what the whole data is about. Organisation needs to know what that data means and be able to interpret it properly or else the organisation is just going to have a bunch of data that is not going to get any organisation ahead of the competition.

Financial institutions have to leverage Big Data properly as per their compliance requirements and high levels of security standards, Banks are making the best use of the data they possess with a view to improve on their services to customers. With the advancement of digital technology, data has become critical and Banks are working hard to embrace and adapt to this change.