Executive readout
Technology-title share of titled profiles (%)
Data table
| Bank | Share % | Wilson low | Wilson high | n titled |
|---|
Operating-model gap
Location concentration
Talent-flow advantage
Decision implications
1. Operating-model benchmark
Where is each bank structurally over- or under-weight in technology, risk, branch, lending, analytics, and leadership?
Function mix (100% stacked, titled profiles)
Gap-to-peer index (selected ÷ peer median)
Index 1.0 = peer median. Values above 1.2 are over-weight; below 0.8 under-weight. Sparse cells (n < 30) marked.
Function mix data table
| Bank | Function | n | Share % |
|---|
2. Talent-flow intelligence
Who wins talent from whom, in which functions, and from which external employers?
Inter-bank move matrix (share of observed moves)
Selected-bank inflow Sankey
Observed previous big-four employer → current bank. Width is move count among profiles with a prior bank mapped.
External feeders into selected bank
| From employer | Observed moves |
|---|
Top external prior employers excluding blank labels. Counts are observed profile transitions, not hires.
3. Career-path and mobility
Which prior functions and tenures precede moves into priority roles?
Prior function → selected destination
Median prior tenure into current function (months)
Median months in the prior role before the observed current function. Sparse destinations marked in the table.
Transition data table
| From function | To function | n | Sparse? |
|---|
4. Branch-to-digital transformation
Where is physical-network contraction occurring alongside a stronger digital or technology talent footprint?
APRA branch network index (2017 = 100)
Official ADI points of presence, June years. Indexed so every bank starts at 100 in 2017.
State snapshot: footprint density vs technology share
Snapshot only — state branch time series is not in this release. X = observed AU profiles per branch; Y = technology share of titled profiles in that state. Selected bank highlighted.
Branch index data table
| Bank | Year | Branches | Index 2017=100 |
|---|
5. Geographic talent hubs
Which cities and states show concentration, whitespace, or reliance on offshore hubs?
Selected-bank state footprint (observed profiles)
AU city hubs — profiles per 100k residents
Big-four combined observed profiles ÷ ABS regional population × 100,000. Year in source payload.
India hub signals
| Bank | Function | City | n |
|---|
Most India rows have no city in the snapshot; Bengaluru is the only named hub. Treat as location signals, not headcount.
6. Education and supply pipeline
Which institutions feed the workforce, and where could the next capability pipeline be built?
AU provider and Group of Eight shares
Share of education records matched to TEQSA AU providers and Group of Eight. Denominator = education records with an institution string.
Top AU institutions for selected bank
| Institution | Records |
|---|
Field-of-study is not in this release; partnership decisions should treat this as institution mix only.
Observed current-role starts (normalized)
These are start years on the current role among observed profiles — not annual hiring volumes. Each series is the share of that bank’s own observed starts in the selected period.
Next-data modules (not in this snapshot)
Evidence drawer — definitions, coverage, scoring, sources
Defensibility ranking
Score blends coverage (40), median cell size (35), and non-sparse share (25). Decision products score high because they only keep claims that already cleared n ≥ 30.
| Rank | Workstream | Score | Tier | Coverage | Median n | Sparse |
|---|