Is your organization fully prepared to meet Basel III regulations? Unfortunately, for some businesses, the answer is still ‘no.’
Too many organizations are behind schedule with their implementation of Basel III standards due to outdated infrastructures and archaic data strategies.
But what exactly is Basel III and what are the specific barriers 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:
If any of this sounds familiar, you might have some work to do. But, have no fear, we’re here to help.
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:
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.
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.
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:
When you have the right architecture, it’s easier to achieve data reconciliation and synchronization.
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:
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.
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.