Banking on Data: Part I

Wholesale banks, or banks offering financial services to large institutions, know that they need to pivot to client-centricity. To get there, they need a single and holistic view of static client data across their entire organization – static client data being any data relating to a client that has unalterable characteristics, for example legal entity name. Banks have every intention of reaching this goal, but given the magnitude of the challenge, historically, few have been successful. To put things in perspective, how much client data do you think infamous data giants Amazon and Google hold? Well, banks have a whole lot more. 

So, is this good or bad? To answer that, let’s begin with a critical point. Currently, banks are being heavily challenged on their top-line revenue figures. They’re being pressured to boost profits by reducing costs. This is, in part, due to the climate of the wholesale banking industry, where return on equity has been c.9-10% pre-pandemic, which is falling short of investor expectations. If client data was the new oil, banks would be booming. But unfortunately, the reality is quite the opposite. Banks have bad quality data, and what does that mean? Data management issues, and huge upfront cost burdens with the majority of it having very little use. To put it in context, global revenue for investment banking is roughly c.$80bn per year, and bad data costs between 15-25% of revenue; the cost of bad data is huge.

Global revenue for investment banking is c.$80bn per year, and bad data costs between 15-25% of revenue; the cost of bad data is huge.

The majority of the problem is internal. For decades, banks have treated the different business units, and sometimes regions, within their various units as separate entities. This lack of collaboration has resulted in isolated organizational structures, creating a federated approach to how each business line manages static client data. Furthermore, banks have spent millions on implementing data lakes (a system or repository of data stored in its raw format) to try and pool client information together, with the hope that this would be their golden nugget to becoming client centric. However, the lack of data governance has led to data lake ‘pollution’, rendering the data as invaluable. 

In this two part series, we will explore why wholesale banks must put in place a foundation for data-driven client centricity through a well-managed data transformation journey. If this is left too late, or even ignored, banks will be left behind.

The transformation journey to client centricity

When it comes to becoming client centric through data, banks need to learn to walk before they can run. The act of simply offering up large quantities of client data to internal business units can not be deemed as successful, nor will it help in providing exceptional customer experience. Most banks today are also guilty of being ‘data wranglers’, using their data scientists to create a single view of data required for specific use cases through building in-house tools. Not a good utilisation of their expertise. This often leads to the data being unusable for other scenarios therefore having a short shelf life.

Initially, banks need to take specific baby steps. They need to make a simple investment into understanding their client base at the highest level based on static attributes. They need to also break out of the dangerous mindset of being able to solve everything on their own, or they risk being overtaken by the institutions that embrace the fintech ecosystem to unleash innovation and results at speed.

Banks need to break out of the dangerous mindset of being able to solve everything on their own, or they risk being overtaken.

BBVA, a Spanish multinational financial services company, exemplify the value that a data strategy underpinned by a centralized view of static client data can eventually bring. BBVA has a 360-degree view of their clients regardless of which BBVA business unit they interacted with, enabling them to personalise client communications and maximize sales conversion by better meeting their clients’ needs. This didn’t happen overnight, and it started with the fundamental; having a single static client data view.

Achieving a holistic view of your client base is a difficult task and there is no denying that. However, it’s a non-negotiable. Unless they want to get left behind, the wholesale banking industry needs to act now. If they fail to address the problem, they will fall victim to rising client expectations and competition from data-centric fintechs, and will struggle to catch up. 

So what should banks consider when undergoing the journey to transformation? In the first part of this series we will explore two key themes.

Provide management with immediate benefits

The tenure of management at banks is becoming shorter. This means that those in the strategy driving seat are under increasing pressure to deliver tangible short-term benefits, as opposed to undertaking long-term programs. This leads to the question, what incentive does each business unit have to reach a centralized view of static client data if the data on its own does not enable the provision of the “sexy” client experience?

The answer is simple. The incentive is that it begins to repay the investment through starting to address one of the most asked questions within financial services – “how do I improve my KYC processes and at the same time reduce cost”. Banks spend up to £300 million annually on KYC. A single static client data view across business units will start to ensure KYC is sped up and not duplicated, and will provide the foundation to unlock numerous other onboarding benefits.

Address static client data issues

Client data quality is a well-worn phrase within bank transformation initiatives. Yet, it is still one of the largest issues facing banks trying to obtain a single static client view. Failing to solve these issues at source will erode trust in wider future data initiatives. Data ownership and stewardship should be federated to each business unit at source to be accountable for future static client data with common core principles agreed. In the short-term, a team will be required to undertake client static data remediation.

Client data quality is still one of the largest issues facing banks trying to obtain a single static client view.

There are numerous Fintech companies, such as Qlik and Loqate, offering data cleansing and ongoing maintenance that should be utilized to help automate the majority of the data cleansing and will significantly reduce timelines. Banks should start by prioritizing their highest revenue-generating clients, which will ultimately equate to the majority of their total revenue. Going forward, it will be crucial for a centralized team to be in place. On top of this, they’ll need to be assisted by technology and automated rules to manually review static client data on an exceptions only basis. This will enable trust in the data and ensure future static client data remediation programs are not needed.

Conclusion

In part II of our banking on data series, we’ll continue to explore how banks can embrace and optimize the journey to data transformation. Follow us on LinkedIn to make sure you don’t miss it.

Want to find out how this can apply to your organization?