Big Data in Banking: Analyzing its Role, Advantages, and Challenges

Ashish M. Shaji

01 Sep, 2020

You will find every business today is data-driven because, in order to make effective decisions, data analytics and insights are essential. It is particularly essential in banking.

 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. The use of big data in banking is bringing positive transformation.

Role of Big data in banking

Banking and financial sectors throughout the globe are discovering new and innovative methods through which they can easily integrate big data analytics into all their processes for optimal output. It has many benefits as far as banking and financial sectors.

With the advancement of digital technology, data has become critical, and banks are working hard to embrace and adapt to this change. The scale of competition is significantly high in the industry, and new tactics have to be adopted in order to survive and thrive in the competitive market environment so that the business grows with success.

The adoption of big data has allowed banks to improve the standards and the quality of services that they provide to their customers. Therefore it is no surprise to us that banking is one of those business domains that make the high investment in big data and its technologies.

Another essential role of big data can be understood through this that amount of data generated and handled by banks and financial institutions are enormous, but because of this technology, as the number of electronic record increases, banks, and financial services is actively using it to store data and improve scalability.

Advantages of Big Data in Banking

Big data brings in a number of benefits to banks. Some of them are mentioned below:

• Management of Risk efficiently to prevent frauds and risks

With the help of big data ^[1] analytics, banks can analyze market trends and decide whether they should lower or increase interest rates.

With fraud detection algorithms, banks can easily identify customers who don’t have good credit scores and not provide loans to them. Using such algorithms, banks can detect fraud and prevent potentially malicious activities.  

• Personalized Banking Solutions

With the help of big data analytics, it can aid banks in knowing customer behavior based on the inputs received from their investment, shopping trends, or financial background. It helps banks to retain customers and also attract many more. Customer loyalty can be won by providing personalized banking solutions.

• 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 operating costs.

• Analysis of customer feedback

As you would know that banks’ customer centers are clogged with many queries regularly. Big data tools can help in going through high volumes of data and respond to each of them swiftly and adequately. With this, customers will also feel valued that their feedback is being taken promptly. 

• Raises overall performance

With performance analytics, the employee’s performance can also be assessed to know if they have achieved the targets. Big data can help in knowing how it would help them to scale better.

What are the challenges with the use of Big Data in banking?

There are certain challenges with its use in banking. Some of them are mentioned below:

• The struggle of legacy systems

The banking sector has been slightly slow to innovate. Most legacy systems can’t cope with the growing workload. In its process of collecting, storing, and analyzing data with the outdated infrastructure, it may put added pressure and can put the stability of the system at risk.

• Security concerns

If we consider the use of legacy systems, there may be a risk as there is dealing with huge amounts of data. Banking providers are required to make sure that the amount of user data they collect and the process must remain safe. The fact of the matter is there are only a few percentages of banks worldwide that can handle the threat.

• The massive proportion of Big data

Banks may find it difficult to cope with a large amount of different kinds of data.

What to ensure when implementing Big Data in banking?

Implementing big data is in the best interest of any financial institution, but as stated above, it has its own challenges as well. Therefore there are some things that banks must be aware of before proceeding.

• Legacy systems lack infrastructure

As mentioned above, the massive amount of big data puts a considerable strain on legacy systems, and many such systems lack advanced analytics in banking. Therefore banks must first look to upgrade their existing infrastructure system before they proceed towards implementing such a strategy like big data.

• Data quality management is critical

Data quality management should be one of the top priorities. Once a bank upgrades its system, data that is inaccurate or incomplete can hamper results. Therefore banks must review and consolidate their existing data before they enter into a new system.

• Consolidation after acquisition

When an acquisition happens, new databases are added to the bank’s data estate. The data spread out across each of the systems must be consolidated in a central repository. By doing that, banks can easily identify unwanted data.

Conclusion

Read our article:Data Analytics in Banking: Application, Challenges & Opportunities