Optimization
Select an optimization below to see an illustrative worked example - reduce EVE? risk, strengthen liquidity, or deploy excess capital - all illustrative; the same platform models any balance-sheet decision. Bring your own trades, or let it solve.
Every figure shown is illustrative and represents a hypothetical bank - not any actual institution.
Optimization & capital allocation
Every constraint, every objective, on one platform - with flexible outputs that turn the same dataset into management decisions evaluated against enterprise objectives and constraints. Built currency-agnostic; non-USD extension is configuration, not code.
Input your own desired trades and the platform prices the full impact - or let it solve for the least-cost set inside your constraints. Recomputed on each run as positions and markets change, with point-in-time history - so every decision is reproducible as-of any date.
Worked example · EVE-risk optimization
Objective
Minimize EVE exposure to a +300 bp shock
Subject to
Strategy option
Interactive · click to exploreOutput A
Output B
| Metric | Before | After |
|---|---|---|
| EVE Δ · +300 | −8.2% | −4.1% |
| NII · 12-mo | $570M | $562M |
| LCR | 118% | 116% |
| CET1 | 12.4% | 12.3% |
EVE risk roughly halved for an $8M NII give-up - inside every constraint.
Output C
Dashed line is the efficient frontier. Blended sits on it - most risk cut per dollar of NII.
Impact by option · before → after
| Metric | Before | Opt 1 · Swap | Opt 2 · Shorten | Opt 3 · Blended |
|---|---|---|---|---|
| EVE Δ · +300 | −8.2% | −5.6% | −4.8% | −4.1% |
| NII · 12-mo | $570M | $566M | $564M | $562M |
| LCR | 118% | 118% | 118% | 116% |
| CET1 | 12.4% | 12.4% | 12.3% | 12.3% |
All three reduce the up-rate EVE loss and stay inside every constraint - the selected option is highlighted.
Worked example · funding-mix optimization
Objective
Strengthen liquidity (TTF, LCR, NSFR) at minimum NII cost
Subject to
Funding strategy
Output A
Output B
| Metric | Before | After | Δ |
|---|---|---|---|
| NII · 12-mo | $570M | $567M | −$3M |
| Stressed TTF | 41d | 72d | +31d |
| LCR | 118% | 128% | +10 |
| NSFR | 121% | 132% | +11 |
Compare all options
| Metric | Before | Term debt | FHLB ladder | Deposit (FTP) | Blended ✓ |
|---|---|---|---|---|---|
| NII · 12-mo | $570M | $562M | $566M | $567M | $565M |
| Stressed TTF | 41d | 66d | 60d | 62d | 74d |
| LCR | 118% | 123% | 121% | 124% | 128% |
| NSFR | 121% | 131% | 128% | 129% | 133% |
Worked example · capital-deployment optimization
Objective
CET1 at 12.4% - ~$675M above the 11.0% target. Deploy the excess across growth, dividends and buybacks to improve risk-adjusted return within capital targets.
Subject to
Deployment strategy
Recommended allocation
Output A
Output B
| Metric | Before | After | Δ |
|---|---|---|---|
| Net income · annual | $720M | - | - |
| EPS · annual | $6.00 | - | - |
| Return on equity | 12.0% | - | - |
| CET1 ratio | 12.4% | - | - |
| Capital returned | $0 | - | - |
| Book value / share | $53.33 | - | - |
| Market value of equity | $8.64B | - | - |
Book value / share = GAAP common equity (assets − liabilities, $6.4B - above CET1, which deducts intangibles and applies the AOCI filter) ÷ shares. CET1 is the regulatory measure; dividends and buybacks reduce both.
Compare all options
| Metric | Before | Grow | Balanced ✓ | Return capital |
|---|---|---|---|---|
| Net income · annual | $720M | $796M | $769M | $736M |
| EPS · annual | $6.00 | $6.68 | $6.56 | $6.36 |
| Return on equity | 12.0% | 13.8% | 13.7% | 13.6% |
| CET1 ratio | 12.4% | 11.0% | 11.0% | 11.0% |
| Capital returned | $0 | $203M | $371M | $574M |
| Book value / share | $53.33 | $52.05 | $51.44 | $50.32 |
| Market value of equity | $8.64B | $9.55B | $9.22B | $8.83B |
Worked example · liquidity & collateral optimization
Objective
Meet reserve & collateral needs at the lowest cost-to-deliver
Subject to
Strategy option
Output A
Output B
| Metric | Before | After | Δ |
|---|---|---|---|
| HQLA carry cost · 12-mo | $22M | $15M | −$7M |
| Collateral capacity | $3.6B | $5.0B | +$1.4B |
| LCR | 118% | 122% | +4 |
| Intraday buffer | $0.6B | $0.9B | +$0.3B |
Compare all options
| Metric | Before | Reallocate | Collateral | Blended ✓ |
|---|---|---|---|---|
| HQLA carry cost | $22M | $14M | $20M | $15M |
| Collateral capacity | $3.6B | $3.9B | $5.2B | $5.0B |
| LCR | 118% | 121% | 119% | 122% |
| Intraday buffer | $0.6B | $0.7B | $0.9B | $0.9B |
Worked example · cash optimization
Objective
Deploy $2.0B surplus cash to the highest risk-adjusted return
Subject to
Deployment option
Output A
Output B
| Metric | Before | After | Δ |
|---|---|---|---|
| Annualized yield | 4.40% | 4.62% | +22 bp |
| Incremental income · 12-mo | $0M | +$4.4M | +$4.4M |
| Intraday buffer | $0.8B | $0.6B | −$0.2B |
| Cash deployed | $0.0B | $2.0B | +$2.0B |
Compare all options
| Metric | Before | Mortgage rolls | Reverse repo | Blended ✓ |
|---|---|---|---|---|
| Annualized yield | 4.40% | 4.66% | 4.68% | 4.62% |
| Incremental income | $0M | +$5.2M | +$5.6M | +$4.4M |
| Intraday buffer | $0.8B | $0.7B | $0.5B | $0.6B |
| Cash deployed | $0.0B | $2.0B | $2.0B | $2.0B |
Worked example · shadow pricing
Objective
Maximize risk-adjusted return; price the marginal value of each move
Subject to
Strategy option
Output A
Output B
| Metric | Before | After | Δ |
|---|---|---|---|
| RORWA | 1.35% | 1.58% | +0.23 |
| Net interest margin | 292 bp | 305 bp | +13 bp |
| Economic value added · 12-mo | $0M | +$21M | +$21M |
| CET1 | 12.4% | 12.3% | −0.1 |
Compare all options
| Metric | Before | Reallocate | New product | Blended ✓ |
|---|---|---|---|---|
| RORWA | 1.35% | 1.52% | 1.49% | 1.58% |
| Net interest margin | 292 bp | 300 bp | 298 bp | 305 bp |
| Economic value added | $0M | +$14M | +$16M | +$21M |
| CET1 | 12.4% | 12.3% | 12.2% | 12.3% |
Shadow pricing · two-tier
The shadow price of every binding constraint - capital, liquidity, funding - quantifies what one more unit is worth, so the current book reallocates to its highest risk-adjusted return.
Prices the marginal value of each new capability or product - drawn from a library of capabilities not yet held - and the stability of that value generation, ranking initiatives into a quantified roadmap by both the upside they unlock and how durable it is.
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 governance 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.