An embedded treasury AI partner, a coherent model of how your bank takes and manages risk, and live monitoring across every risk family — built for Australian ADIs and mutuals, on your own infrastructure.
The platform on a live bank book — a two-and-a-half-minute tour.
Every install ships with Q — an embedded treasury AI partner with read access to your deal book, system-prompted in treasury vocabulary. It doesn't just chat; it analyses, simulates, reports, and reasons over your positions and your policies.
Analyst-grade reads of the book — concentration, drivers, outliers — citing the exact deal IDs behind each figure.
Ask anything of your live position in plain English. Q resolves it across loans, deposits, derivatives, securities and FX.
Rate shocks, funding stress, counterparty default — run multi-variable simulations across the whole balance sheet in seconds.
Generate board-ready ALCO packs and risk reports, drafted from live numbers and exportable for the committee.
Pose hypotheticals — "what if we add A$200m of 5-year fixed?" — and see the impact on ratios, NII and limits before you act.
Project balances, ratios and exposures forward over time, and track how the book has moved across snapshots.
Ask Q about your own limits, mandates and risk appetite — it reasons over the policies encoded into the platform, not just the data.
Spin up a saved project — a funding review, a hedge proposal, a stress study — and have Q build, organise and revisit the analysis.
A deeper walk through the cockpit — running on the Future Bank demo dataset.
TraiQ is built on one coherent model of the balance sheet. It's not a pile of disconnected reports — every number traces along the same chain, which is also how Q reasons about your book.
Loans, deposits, securities, FX, derivatives — organised the way your institution actually groups them.
The instruments themselves — term loans, CP, bonds, swaps, forwards — each with its own cashflows and terms.
Every product generates exposure — liquidity, rate, credit, FX, capital — across the risk families.
Risk appetite, limits and mandates govern those exposures — and TraiQ monitors utilisation against them live.
This Portfolio → Product → Risk → Mandate model is fully configurable to your institution — the names, the groupings and the relationships are yours, not a fixed template.
Each risk family has an authoritative home in the platform — monitored live, with the regulatory methodology behind it and full drill-down to the underlying positions.
Live LCR, NSFR, funding gap, survival horizon. The full picture before the ALCO pack goes out.
Counterparty concentration, limit utilisation, DV01, RWA, credit migration — every exposure, one dashboard.
Returns assembled from the live position, reconciled, and variance-checked — with every figure traced to source before submission.
Capital adequacy, NSFR, LCR trend, stress scenario output. Numbers fit for a regulator.
Australian ADIs face rising regulatory pressure, faster-moving markets, and growing expectations for clean, explainable data — the pace spreadsheets and manual reconciliation were never built for.
APRA expects accurate, well-controlled, traceable data — not numbers hand-stitched across spreadsheets the night before a deadline. Data quality is now its own scrutiny.
AI is moving from pilot to core infrastructure across the industry. The ADIs that build it into how they see and manage risk now will set the pace others have to match.
Every copy-paste between systems and workbooks is a place a wrong number slips through — and a resubmission or "please explain" is far more costly than catching it first.
The same live positions and risk methodology behind the platform assemble your APRA returns — reconciled, variance-checked and traced back to source before you submit. Every figure carries its own evidence trail — a defensible, documented process, not just a number on a page. Q acts as a second set of eyes across APS 210, APS 117, capital and credit, while your responsible person stays in control of what gets filed.
Built around the standards you report against
Walk through Q, the risk families, and the full balance-sheet model on our demo dataset.