03 Data
Financial data and analytics.
Most financial institutions in these markets have data. Few have data they can put in front of a regulator or a board with confidence. The same number differs between the core system, the risk engine and the finance ledger. Nobody owns the definition. The regulatory return is assembled by hand every month, and the board pack is assembled separately, from a different extract.
We treat the regulated number as the unit of work: who owns it, where it comes from, how it is reconciled, and how the institution defends it when a supervisor asks.
Capabilities
- 01
Data governance and lineage
Ownership, definitions and a single authoritative source for each regulated number. Lineage from the return or the board figure back to the transaction, so the institution can defend a figure to a supervisor and show how it was produced.
The work is organisational before it is technical: a data owner for every regulated metric, a definition the finance and risk functions both accept, and a control that fires when the two systems disagree.
- 02
Regulatory and supervisory reporting
Automating the returns the central bank actually asks for, with reconciliation to the general ledger and to the previous period built in, so the monthly pack is produced rather than assembled.
Every submitted figure carries its source, its reconciliation and its sign-off, and a change in the regulator's template is a configuration change rather than a month of rework.
- 03
Risk, credit and portfolio analytics
Portfolio quality, concentration, early warning, expected loss and the analytics a credit committee needs to make a decision it can later explain. Models are documented, thresholds are governed, and the inputs behind each figure are logged.
The same lineage that supports the regulatory return supports the credit committee, which is what lets the two conversations agree.
- 04
Executive and board intelligence
One authoritative view across finance, risk, operations and customers for the chief executive and the board, derived from the same source as the regulatory numbers so the two never disagree.
Each executive role sees the view it is accountable for, drawn from one place, rather than a set of slides reconciled by hand the night before the meeting.
- 05
Data platforms for regulated institutions
Lakehouse and warehouse architecture designed for data residency, retention, auditability and the outsourcing frameworks that apply when the platform is on cloud. Where the data lives is decided against the regulator's outsourcing rules before a vendor is chosen.
The platform is built so that provenance, access control and retention are properties of the architecture, not policies applied afterwards.
Case study: Cloud architecture and governance for a regulated bank
Data For the client
What this means for a client.
i
The regulator's numbers and the board's numbers must come from the same place.
Two extracts produce two answers. A single authoritative source, with ownership and definitions agreed, is what lets the return and the board pack tell the same story.
ii
Analytics without lineage is an audit finding waiting to happen.
A dashboard that cannot show where its figures came from will not survive an inspection or an internal audit. Lineage is designed in with the first metric, not reconstructed after the finding.
iii
Where the data lives is an outsourcing decision before it is a technology one.
Residency, retention, access and exit are governed by the central bank's outsourcing and cloud frameworks. The platform decision is made against those rules first and the vendor shortlist second.
Data Related case studies
The record behind this work.
Who we work with
Banks, microfinance institutions, payment institutions and non-bank lenders that report to a central bank, and the boards and audit committees that rely on the numbers.