How to Prepare Your Data Processes for Basel III (and Stay Compliant in the Future)

Written by
CloverDX
CloverDX

Is your organization fully prepared to meet Basel III regulations? Unfortunately, for some businesses, the answer is still ‘no.’ 

What stands in their way is outdated infrastructures and archaic data strategies. 

Indeed, many organizations have substantial ground to cover and are worryingly behind schedule with their implementation of Basel III standards. 

But what exactly is Basel III and what are the specific barriers to compliance? 

Understanding Basel III and the road to compliance 

Designed to promote stability in the international financial system, Basel III requirements contain important changes designed to improve banks’ regulation, supervision and risk management.  

Primarily, it means firms will have to hold more capital.  

As capital requirements more than double from 2 percent to 4.5 percent financial institutions will need to have more accurate data about their capital, loan book and investment portfolio.  

To achieve this, your firm needs the right data processes. You will struggle to meet the regulation requirements if your business suffers from the following problems: 

  • Manual data processing that is both time-consuming and prone to human error 
  • Obscure processes, data silos and limited visibility or collaboration 
  • Thousands of non-standardized integrations 
  • An ever-growing scale of data that’s overwhelming for legacy systems 

If any of this sounds familiar, you might have some work to do. But, have no fear, we’re here to help. 

Prepare for Basel III by building auditable and transparent data processes 

So, what does it look like to have a data infrastructure that can navigate the difficult seas of regulatory compliance and meet Basel III? 

A strong data infrastructure has auditable and transparent data processes that help you: 

  • Reconcile and validate risk reports 
  • Generate accurate and reliable risk data 
  • Minimize errors as much as possible 
  • Maintain the accuracy and completeness of all data 
  • Standardize data definitions across all departments 

Adding these capabilities is critical for a strong infrastructure. However, implementation of these improvements can be a struggle. 

To help bridge the gap, we’ve put together a list of three key areas you need to strengthen to ensure compliance with Basel III. 

#1 Improve your underlying governance and infrastructure 


Firstly, it’s critical that the senior management team takes ownership of the risk data aggregation process. Then, adequate controls can be put in place. 

Indeed, throughout your organization, proper ownership and accountability for compliance and data processes is necessary. Failing to assign roles can lead to staff ducking responsibilities. Or, staff hoping that someone else is taking care of the data. 

A strong and robust infrastructure is also critical so that in moments of crisis your data processes and reporting remain intact. Data governance is foundational to this infrastructure, but so too is defining your data strategy, documenting data lineage, and regularly assessing performance. 

#2 Aggregate your risk data and address data quality 

Before you do risk calculation, it’s important to ensure your data is of the right quality, and that you have a single point of truth for all your data. 

Improving data quality is best tackled from multiple angles, and includes: 

  • Tracking data quality. Without processes to track data quality, you can’t be confident in your data or its level of risk. Use automated data health checks to get the visibility you need. 
  • Cleaning data at the points of entry. Once bad data is in the system, it can be painstaking to understand how far it’s spread and what affect it’s had. Save time and headaches down the road by cleaning it at the point of entry. 
  • Automating processes. With IT teams spending as much as 60 percent of their time cleaning data, turning the problem over to an automated solution can save a lot of time. This is also a way to remove human error. 

When you have the right architecture, it’s easier to achieve data reconciliation and synchronization. 

#3 Better risk reporting and data modelling 

Improving the quality of your risk reporting will make it more useful for senior business teams and help them make better decisions. 

Sharply defined processes and reporting procedures will also help your business remain compliant. An example of this is specifying the frequency of reporting during both normal operations and when there’s a crisis. 

It’s also essential to have centrally defined data quality measures, definitions, and standards across all departments. This ensures company-wide data quality and prevents problems like duplication and data-silos.  

To achieve this, data modeling is key. Indeed, data modeling helps resolve many of the compliance challenges organizations face. 

Using data models, you can capture and visualize your data infrastructure, including: 

  • Data sources 
  • Data mapping 
  • Transformation logic 
  • Consumer use 

With data modeling, data lineage and data relationships are now visualized. What’s more, data models standardize ‘vocabulary’ and data definitions. This makes data models easy to share and improves collaboration. 

Collectively, these improvements help remove data-silos, duplication, and divergent datasets. In turn, you’ll move closer to the auditable and transparent processes you need. 

Basel III and regulatory compliance brought within reach 

Ignoring Basel III and the necessity of auditable and transparent data processes won’t make the problem go away. And it’s better to build effective systems now rather than having to apply costly band-aid solutions later. 

Improving your data governance, reporting and data quality are the important first steps. As is using data modeling to establish the standardization and visibility you need. But, linking data models to IT operations can be manual, tedious and unreliable. 

However, by using automation where you can, it’s possible to bridge the gap between data models and actionable IT operations. Then, you can turn your data, mapping, and transformation definitions (all captured in data models) into executable runtime objects. 

This delivers efficient, transparent and auditable processes that help bring Basel III and other regulatory compliance within reach. 

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Posted on November 11, 2019
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