Illustrative banking and financial services data diagnostics — IFRS 9 and credit model inputs, fraud scoring noise from bad transaction data, fragmented KYC and FICA records, and regulatory reporting that cannot be reconciled to source. Independent advisory scenarios for South African lenders and financial groups.
These are illustrative scenarios. They reflect patterns seen across retail banks, niche lenders, mutual banks, and financial services groups operating in South Africa. Names, figures, and identifying details are not drawn from specific clients.
The purpose is to show what a data diagnostic examines in a banking context, what it typically finds, and what it produces — before systems are replaced, models are retrained, or major compliance projects are funded.
For the governance and strategy frame, see Banking Data Strategy.
A bank is a data-processing institution: loans, deposits, transactions, customer identity, risk models, and regulatory returns all depend on consistent, traceable data. Yet most organisations accumulate product silos, legacy cores, bolt-on channels, and decades of reporting shortcuts. The symptoms surface as model debates, audit findings, customer friction in onboarding, and returns that three teams cannot reconcile.
In South Africa, POPIA, FICA, prudential reporting, and conduct expectations raise the stakes: the regulator and the board ask not only what the number is, but how it can be proved from systems of record.
These diagnostics are relevant where:
A mid-tier lender’s IFRS 9 staging and ECL outputs looked plausible in committee — until an internal review asked which loan book snapshot had been used and discovered three competing definitions of “outstanding exposure” across origination, collections, and finance.
A retail bank’s transaction monitoring rules and risk scores produced alert fatigue: investigations cleared the same patterns every week. The diagnostic found missing and miscoded transaction attributes — the model was doing what it was told on bad inputs.
A financial services group could not produce a single customer due diligence trail for a sample pulled by internal audit: branch scans, digital onboarding, and product systems each held part of the record, with no authoritative identity key.
A prudential submission was challenged internally when finance discovered the same metric at different values in risk, finance, and regulatory reporting packs — each team had built logic on a different extract date and filter set, none of it documented.
This work does not implement core banking changes, build data warehouses, or retrain production models. It produces governance clarity and decision-ready findings upstream of those investments.
The framework for these scenarios is on Banking data strategy — that hub links to the guides below.