Recognising that the efficacy of advanced mathematical risk modelling is entirely contingent upon underlying data integrity, we offer an exhaustive suite of data engineering, governance and AI-governance solutions designed for the strictest regulated environments.
Data Services functions as the foundational substrate supporting both the banking and insurance verticals. Our practice encompasses Data Architecture & Engineering — establishing the robust pipelines necessary for high-frequency financial modelling — heavily augmented by stringent Data Governance and Data Privacy consulting that keeps institutions compliant with evolving cross-border data sovereignty laws.
Crucially, we assist institutions in achieving sustainable BCBS 239 compliance through the implementation of risk-based approaches to data lineage tracking — and we have formally integrated Generative AI and Specialised Applications into our core service offerings, helping regulated institutions safely research, validate and deploy advanced machine learning within the strict confines of financial compliance frameworks.
A cross-functional discipline that serves both verticals and stands alone as a service line for institutions consolidating their data estates.
Designing and building the robust, high-frequency data pipelines that risk and actuarial models depend on — from ingestion through to the gold-standard layer consumed by your reporting estate.
End-to-end data governance frameworks — ownership, stewardship, quality controls and policy — implemented in a way that scales with the institution and survives regulatory scrutiny.
Privacy programmes that keep your institution compliant with evolving cross-border data sovereignty laws and the increasingly complex demands of the global data-protection landscape.
Risk-based approaches to data lineage tracking — making BCBS 239 compliance maintainable for the long term rather than a one-off compliance exercise.
Targeted analytics applications — affordability modelling, economic forecasting, decisioning engines — engineered to stress-test retail and corporate portfolios against severe macroeconomic shocks.
Ethical AI governance and Sustainable Data Lineage frameworks for the safe, regulator-aligned deployment of large language models and machine learning across financial workflows.
We treat AI inside a regulated bank or insurer as a model-risk problem, not a chatbot problem — every pipeline, every prompt, every output is engineered to be auditable, traceable and explainable.
Our founding team has deployed LLMs and ML inside regulated workflows where evaluation harnesses, lineage capture and challenger review are not optional. Every engagement ships with the governance scaffolding regulators now demand, alongside the engineering that makes the AI actually useful.
Analysis written for Directors of Data Governance, Heads of Data and Chief Data Officers operationalising AI inside compliance frameworks.
Bias, drift and explainability are now operational requirements across every AI deployment — not just classical actuarial and credit models. A coverage map for 2026.
Read the analysis →Generative AI in a regulated bank or insurer is a model-risk problem, not a chatbot problem. Lineage, evaluation harnesses and challenger review are non-negotiable.
Read the analysis →Risk-based lineage that survives reorganisation, vendor swaps and model retraining — the difference between BCBS 239 compliance you can defend and a binder of diagrams.
Read the analysis →Talk to our data and AI leadership about lineage, governance, privacy or Generative AI in regulated workloads — or subscribe to our data & AI briefing.
Or write to us directly: data@wersel.io
