How Are Banks In Singapore Using Big Data?

What Is Big Data?

Anyone would answer in a breath that it is the process of analyzing huge amounts of data for actionable, useful information.

However, in practice, big data is much more difficult to process and navigate. While this technology has immense potential, you have to use it correctly to yield fruitful results. You need to know which dataset would give valuable results and which dataset won’t. With this, is also necessary to know what to use where and in which condition.

Talking about the banking industry, big data is no doubt the perfect platform for it. With a lot of customers, it is easier for banks to collect huge amounts of data every day. This makes the banking industry the right podium for big data. But, this is of no use unless it is utilized effectively. Thankfully, banks of Singapore have already set an example for us. Let’s see how.

Client Processes

Using machine learning and big data, banks can easily automate a lot of processes. For instance, due diligence during client onboarding. It is easier to speed up calculations and tasks to save hours spent on these repetitive, complex task.

Standard Chartered Bank is already automating due diligence. They are sourcing a lot of data from separate resources present on private and public registries.

Citi Bank, on the other hand, is automating reporting and other manual task using machine learning. Big data is being used to enable effective decision-making.

HR Processes

Big data analytics is a tool generally utilized for customer operation and engagement. However, its potential is much more than that. Using big data, the HR team can streamline processes. For instance, during recruitment, big data can be utilized to assess the ongoing trends to analyze what benefits and compensation to offer to candidates.

The DBS bank used big data to enhance the efficiency of HR operations. Using big data, the HR team analyzed the trends of the industry and assessed the risk factors related to hired employees. This helped them in reducing attrition to 18% which was 27% earlier.

Cyber Incidents

Cybersecurity is essential for every organization. With almost every transaction going online, it is unlikely for banks to ignore cybersecurity. Big data helps banks to evaluate the threat patterns and suspicious transactions.

The OCBC Bank utilized predictive analysis and big data to reduce the cases of cyber threats. They were successful in preventing 55% of the threats. The bank additionally resolved 62% issues as soon as they arrived. Further, the power of big data was also used to improve decision-making capabilities.


Identifying whether a bank is at the risk of a breach is not an easy task. Everything from the mobile banking services to the security of the bank’s data and transactions, pose a threat. It can even lead to a huge money theft if not handled soon. This happens when any system in the organization has a security loophole. To rectify that, banks can use big data. Big data can assess the points where things seem odd and it can even analyze customer usage pattern. Using this knowledge, the bank can provide enhanced customer service.

The DBS bank is also using big data to predict pattern and auditing. They were successfully able to figure out when their ATMs would run out of cash to avoid the out-of-service problem. The predictive analysis helped the bank reduce customer complaints by 92%.

Anti-Money Laundering

Big data can be used to constantly monitor the transactions of the bank. As soon as a suspicious transaction happens, a notification is sent to the officials. From here, a bank official can take over the task and analyze whether the transaction is suspicious or not.

The ABS bank observed a 50-60% decrease in the false positives related to the name screening module.


Big data has much more potential than this. If you combine it with other technologies such as artificial intelligence, machine learning, etc., it is possible to extract the full potential of the technology. Like Singapore, other banks can also use this technology to improve operational efficiency, reduce money laundering cases, and increase customer engagement.