Data You Can Defend: Basel 3.1 Credit Risk Data Foundations
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Better models begin with better-controlled data
Key topics covered by this white paper:
- What Basel 3.1 means for IRB and the output floor
- Why the model is only as good as the data
- How small data defects become big RWA problems
- Four principles for controlling credit risk data
- Why data quality and matching work together
- How to turn exceptions into evidence
- Lessons from BCBS 239 and FSCS regulated programmes
Is your IRB data ready for Basel 3.1?
Basel 3.1 is changing how UK banks calculate and manage credit risk. For banks with suitable portfolios, an internal ratings-based (IRB) approach can measure risk more precisely and may improve capital efficiency. With a 72.5% output floor now limiting how far internal models can reduce capital, the data underneath has to stand up to scrutiny. Credit-risk models use data from across the bank: customer and account records, financial statements, payment histories, facilities, collateral, guarantees and historical outcomes. If a record is missing, inconsistent or linked to the wrong place, it can affect segmentation, model inputs, ratings and risk-weighted asset (RWA) calculations. It can also mean manual investigation, remediation and more conservative treatment. In Data You Can Defend, Tom Carroll (Datactics) and data governance expert Piotr Pietrzyk (Data Governance Universe) look at how data quality monitoring, matching and issue remediation can give banks more confidence in the data behind their credit-risk models, calculations and reporting.
What you can expect to learn from this white paper
How to identify what matters most. Not every field carries the same risk. You’ll see why banks should prioritise the critical data elements behind segmentation, ratings, risk parameters, and collateral or guarantee treatment, each with defined standards, named owners and proportionate controls.
How to control data throughout its journey. Profile and validate data close to where it originates, then again after key transformations. This catches source problems before they spread and finds defects introduced later. One anonymised example: at a major European bank, a significant shift in a credit-risk outcome was traced back to a handful of records missing a critical classification.
How to connect the records that belong together. Reliable links between customers, exposures, collateral, guarantees and historical events reduce the risk of fragmented exposures, missed protection and double counting, using the same matching discipline proven in FSCS Single Customer View and BCBS 239 programmes.
How to build an audit-ready remediation trail. Finding an error is only the start. You’ll learn how to give each exception context, an owner, a priority and a route to resolution, so you can show what failed, what it affected and whether the root cause was fixed.
Where to start without a major transformation programme. A targeted assessment of your highest-risk data elements and relationships, followed by repeatable controls you can expand as your requirements develop.
Who it’s for: Chief Risk Officers, Heads of Credit Risk, IRB programme leaders, Chief Data Officers and Heads of Data Governance.