Data control

One canonical state. Full lineage, every run.

Every instrument, assumption, and market input flows through one governed data layer — normalized to a canonical state, quality-checked at ingestion, and traceable from each reported number back to source.

Every figure shown is illustrative and represents a hypothetical bank — not any actual institution.

The data pipeline

From source to canonical state.

Five governed stages, one flow — each step controlled, logged, and reproducible.

1

Ingest

Multi-source feeds — core, securities, derivatives, deposits.

2

Validate

Completeness, validity, and tolerance checks at the door.

3

Normalize

One canonical instrument record with cashflows and terms.

4

Govern

Assumptions versioned to the SR 26-2 registry.

5

Serve

One state vector feeds every engine — same numbers, same time.

Data functions

Control at every step.

The data layer is not plumbing — it is a governed control surface, with quality, lineage, and access enforced on every run.

Ingestion & normalization

Multi-source ingestion across core, securities, derivatives, and deposits — normalized to a single instrument schema.

Canonical instrument model

One authoritative record per instrument: contract terms, cashflows, and behavioral parameters in one place.

Data-quality controls

Completeness, validity, and tolerance checks at ingestion — failures are flagged and scored, never silently dropped.

Assumption registry

Every assumption versioned and written to the SR 26-2 registry at each run, with owner and effective dating.

Lineage & audit trail

Trace any reported figure back through every transformation to the source record — fully reproducible.

Reference & market data

Curves, ratings, FX, and prices managed with effective-dating, vendor fallback, and staleness checks.

Reconciliation

Automated reconciliation to the general ledger and across engines, with exception reporting and sign-off.

Access & segregation

Role-based access and segregation of duties across data, model, and reporting functions.

Versioning & reproducibility

Every run is a versioned snapshot — re-run any prior date with the exact data and assumptions used.

By design

Consumes incomplete data. Prices the gap.

Real bank data is never complete. Missing fields are imputed against the canonical model, and the resulting assumption risk is expressed in basis points — so gaps are visible and bounded, never hidden.

  • Imputation against the canonical instrument model, not blank cells.
  • Assumption risk quantified in bps and surfaced on the dashboard.
  • Data-quality score travels with every figure into reporting.

Data-quality snapshot

Illustrative
Field completeness96%
Validation pass rate99.2%
Imputed exposure4%
Priced assumption risk3 bps

Ready to see it live?

See your own numbers, computed live.

A guided demonstration using your institution's publicly available financial data — your own NII, EVE, FTP, and capital metrics, across all 12 scenarios.

Platform demo

Live walkthrough of the Phase 1 screens — institution selector, scenario toggle, assumption overrides in real time.

Technical briefing

Architecture review for risk, technology, and model-risk leadership — SR 26-2 governance and integration design.

Regulatory review

Capital, liquidity, and reporting capability review for chief risk officers and regulatory-affairs teams.

About us

Built by people who have managed risk.

Bulls-Eye Solutions builds the enterprise financial engine for modern institutions — one platform that unifies risk, capital, liquidity, funds transfer pricing, attribution, and optimization on a single canonical state. Founded by veterans of top-tier bank treasury and risk management, we pair production-grade software with decades of hands-on enterprise experience, delivered as Risk-as-a-Service.