Data control
Every instrument, assumption, and market input flows through one governed data layer - normalized to a canonical dataset, 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
Five governed stages, one flow - each step controlled, logged, and reproducible.
Ingest
Multi-source feeds - core, securities, derivatives, deposits.
Validate
Completeness, validity, and tolerance checks at the door.
Normalize
One canonical instrument record with cashflows and terms.
Govern
Assumptions versioned to the model-risk registry.
Serve
One dataset feeds every metric - same numbers, same time.
Data functions
The data layer is not plumbing - it is a governed control surface, with quality, lineage, and access enforced on every run.
Multi-source ingestion across core, securities, derivatives, and deposits - normalized to a single instrument schema.
One authoritative record per instrument: contract terms, cashflows, and behavioral parameters in one place.
Completeness, validity, and tolerance checks at ingestion - failures are flagged and scored for review.
Every assumption versioned and written to the model-risk registry at each run, with owner and effective dating.
Trace any reported figure back through every transformation to the source record.
Curves, ratings, FX, and prices managed with effective-dating, vendor fallback, and staleness checks.
Automated reconciliation to the general ledger and across the platform, with exception reporting and sign-off.
Role-based access and segregation of duties across data, model, and reporting functions.
Every run is a versioned snapshot - re-run any prior date with the exact data and assumptions used.
By design
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.
Data-quality snapshot
IllustrativeEach imputed assumption carries a bounded risk that flows through to the metric it touches - quantified.
Fits your environment
Bulls-Eye ingests from your existing core, securities, derivatives, and deposit systems - normalized into one canonical dataset. It's designed to sit alongside your existing systems, with modular deployment and standard data interfaces.
Right-sized for community and midsize institutions as well as larger institutions - deploy the modules you need, staffed to your team.
Works with your stack
Standard data interfaces across core, securities, derivatives, and deposits.
Modular deployment
Stand up the modules you need first, add the rest on your timeline.
Alongside your systems
Sits alongside your systems of record - no rearchitecting to get value.
Right-sized to you
Practical to implement and run, whatever your size or staffing model.
Ready to see it live?
A guided demonstration using public filings and disclosed assumptions. Institution-specific risk estimates depend on data availability; production analysis requires more detailed inputs.
Walk through scenarios, assumption changes and attribution in the platform.
Architecture review for risk, technology, and model-risk leadership - Model risk management and integration design.
Capital, liquidity, and reporting capability review for chief risk officers and regulatory-affairs teams.
About us
Bulls-Eye Solutions builds the enterprise platform for financial institutions - one platform that unifies risk, capital, liquidity, funds transfer pricing and attribution on one shared dataset, with optimization on deck. Founded by veterans of top-tier bank treasury and risk management, we pair the platform with experienced treasury and risk practitioners, delivered as Risk-as-a-Service.