Data Analytics Platforms

Data analytics platforms are used by banks to rapidly identify and remediate issues that emerge. They also help to prevent problems from happening in the first place, by detecting unusual activity that otherwise would have slipped through the cracks. Besides helping to protect against risk and fulfilling regulatory requirements, data analysis also gives banks a huge amount of information that they can use to better serve their customers. 

The use of data analytics platforms is usually driven by three main factors. Cost and efficiency are the first two considerations, as using such platforms generally provides cheaper and faster analysis. A third factor is regulatory risk – banks need to ensure that they are not inviting more regulatory risk, so using data platforms can be safer as they as are focused on specialised areas. 

In terms of their adoption of data analytics platforms, banks in Hong Kong are doing well compared to global standards. Part of this is due to encouragement from the HKMA, who have made it a regulatory focus in recent years and have also been keeping track of adoption and activity. 

But while data analytics platforms are not a new development for banks, the technology changes constantly as new innovations emerge, and banks need to keep up to date. Banks have become skilled in using traditional and existing data sets in recent years. However, to really benefit from data analysis they should also be making use of the new types of data sets that are emerging, to better understand customer behaviour and to spot any anomalies.

A key challenge currently for banks is ensuring that they are able to use the new data that is becoming available as a result of the changes in the overall banking infrastructure and the increasing use of mobile devices for banking by customers. If they are to take advantage of this wealth of new data, banks will need to have the right platforms and qualified people in place – whether inhouse or by using third party service providers. 

Hong Kong’s new virtual banks have an advantage in some respects when it comes to data analysis. As these banks are entirely online, their customers’ footprints are therefore entirely digital. This gives the virtual banks a more complete picture of customer activity, with whole new sets of data points including IP addresses, location and even biometric footprints. For example, they can identify if a customer is logging in from a different device than usual. 

Traditional banks are also benefitting from these new data sets as their customers are also increasingly using mobile devices for banking. In addition, seeing some of the headway that the virtual banks are making in this area has nudged the bigger banks to adopt some of the new technology. The major banks in Hong Kong also have the advantage of more data to use from their large customer base.

As data analytics becomes ever more important for banks, a number of questions are emerging about how to move forward. One of the key areas for consideration is the use of outsourcing.

Banks have been using data analytics for some time, but as the landscape evolves there are good reasons for banks to take a fresh look at how they are using data analytics platforms and the related staff. Over the years, as the technology initially developed, banks hired a lot of data scientists and other experts in areas including cybersecurity and forensics to run their data analytics platforms. These highly qualified staff members also require high salaries, so come at a significant cost.

But these earlier types of data science have now become “business-as-usual” for banks, so they are now asking if their high-salaried staff members need to continue focusing on these areas. If such types of data analysis can be outsourced, this will save costs, and allow the banks’ top-level data scientists to focus on the next generation of technology and innovation. 

There are a number of well-established third party vendors in the market that can provide specialised data analytics platforms that offer a more cost-effective option. 

The second key consideration involving data analytics platforms is around workforce transformation. Many areas of banking have now become data driven and analytics focused, so banks are realising that the number and type of staff that they need in today’s workplace may be quite different, even compared with only a few years ago. 

One of the key benefits of data analytics platforms is that they have removed a lot of the mundane work for banking staff. Banks therefore do not need so many employees engaged in low-skilled activity as in the past.

More recently, as the technology has become more sophisticated, data analytics platforms are increasingly able to carry out some of the more complex analytic work. This is freeing data scientists to use their expertise to look ahead and focus on identifying the new analytics development that will help banks strengthen their resilience and improve their services.

Now that the pandemic restrictions have been removed in Hong Kong, it is a good opportunity for banks to review their workforce and operations. This should include the use of data analytics platforms, which can not only improve efficiency and save costs, but will also free up banking employees to do more meaningful work.