Dr RisQuant™

Send us the portfolio. We return the analysis.

The quantitative arm of Phoenicia Consulting. Send us your deal or portfolio and we return the analysis. It is calibrated against real government and multilateral portfolios, documented to survive a validator, and delivered as results you own.

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DFI Economic Capital: try it live

Adjust a single deal and watch the economic capital recompute live. Nothing is sent anywhere; the maths runs entirely on your device. The full assessment adds CreditRisk+, Monte Carlo, portfolio aggregation, and stress testing on your own book.

Single-deal economic capital

Basel IRB · preferred-creditor adjusted
Effective PD
after preferred-creditor adj.
Expected loss
PD × LGD × EAD
Economic capital
Capital saved
vs commercial treatment
Commercial treatment (PD × 1.00)
Preferred-creditor treatment

This is the differentiator. No commercial Basel engine recognises that MDBs and DFIs default less than commercial lenders to the same sovereign. The preferred-creditor adjustment is calibrated from IFC, EBRD, and ADB portfolio data. Figures are illustrative; the full assessment calibrates to your portfolio.

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Assessment 01

DFI Economic Capital

The problem

Development finance institutions manage over $2 trillion in combined assets but rely on methodologies designed for commercial banks. Standard Basel IRB ignores preferred-creditor status, sovereign risk concentration, and concessional lending structures. No commercial software addresses this gap.

The solution

A three-engine economic capital framework calibrated for DFI portfolios:

  • Basel IRB engine. Regulatory capital with asset-correlation and maturity adjustments.
  • CreditRisk+ engine. A gamma-mixed Poisson loss distribution, analytically tractable.
  • Monte Carlo Gaussian copula. 10,000+ simulations, correlated defaults, full loss distribution.

The preferred-creditor adjustment, which sets us apart: institution-type PD multipliers (MDB 0.4×, bilateral 0.5×, ECA 0.6×, NDB 0.7×) calibrated from historical recovery data.

What you receive

  • Economic capital per exposure under all three engines, with the method comparison
  • Preferred-creditor-adjusted capital and the saving versus commercial treatment
  • Loss distribution with VaR and Expected Shortfall at 99% / 99.5% / 99.9%
  • Marginal EC contribution per deal, giving you a defensible basis for limit setting
  • Concentration analytics: HHI by country and sector, effective N, top-10
  • Stress results across baseline, adverse and severely adverse scenarios
  • Written report plus a methodology annex your validator can challenge
  • Results as Excel and CSV, yours to keep and reuse

Delivery: 3–5 working days · enquire for pricing

Market-gap extensions
  • Recovery-rate modelling for DFI collateral (sovereign / partial credit guarantees, PRI)
  • Portfolio optimiser using marginal EC contributions for limit setting
  • Multi-currency aggregation with FX overlay
  • Blended-finance structuring: optimal public/private capital mix
  • OECD DAC reporting integration for ODA eligibility
Assessment 02

Climate Risk ICAAP

The problem

PRA SS5/25 requires regulated firms to complete an internal review and gap analysis by 3 June 2026 with a credible remediation plan. Most challenger banks lack internal capability to quantify transition and physical climate risk within ICAAP. The Big Four charge £150,000+ for this work.

The solution

  • NGFS scenario engine. Four pathways (Net Zero 2050, Delayed Transition, Current Policies, NDCs), calibrated to the published NGFS Phase 5 carbon-price and macro data.
  • Sector carbon-intensity model. 12 sectors with carbon-price-to-PD transmission.
  • Physical hazard transmission. Four hazards (flood, heat stress, drought-driven subsidence and wildfire), each routed to PD and LGD through its own transmission channels (collateral value, borrower resilience, insurance availability), calibrated to UKCP18 regional projections and PRA SS5/25.
  • PRA SS5/25 gap analysis. 25 mapped requirements with automated RAG status, remediation roadmap.

What you receive

  • Baseline versus stressed ECL under all four NGFS pathways, loan by loan
  • Capital impact quantified for your ICAAP, with scenario comparison
  • Transition-risk hotspots by sector and physical-risk hotspots by UK region
  • PRA SS5/25 gap analysis: 25 requirements with RAG status and evidence gaps
  • A phased remediation roadmap with named owners and target dates
  • Board-ready summary plus the methodology annex behind every number
  • Results as Excel and CSV, yours to keep and reuse

Delivery: 3–5 working days · enquire for pricing

Market-gap extensions
  • TNFD nature-risk module (biodiversity, water stress, land use)
  • ILAAP climate liquidity stress (deposit outflow, funding cost)
  • Scope 3 financed-emissions calculator
  • Regulatory reporting automation (PRA climate returns, EBA Pillar 3 ESG)
  • Board training package with interactive scenario exploration
Why property-level physical risk pays for itself

Better data doesn't remove the hazard, it removes the uncertainty capital

Assessing physical climate risk at the level of the individual property, rather than the portfolio average, does not make a flood-exposed home safer. What it does is convert information uncertainty, the capital a prudent firm must hold against exposure it cannot see, into measured residual risk it can price, manage and defend. The supervisory expectation under PRA SS5/25 is moving in exactly this direction.

In our framework a book assessed only at portfolio level carries a materially larger physical-risk capital uncertainty add-on than the same book assessed property by property. That difference is a direct capital saving, and it funds three concrete actions: informed LTV decisioning on hazard-exposed lending, insurance-coverage monitoring, and targeted adaptation finance.

Grounded in the public evidence base (NGFS Climate Scenarios, PRA SS5/25, IPCC WGII, the Bank of England's climate stress testing, and ESRB financial-stability work) and delivered with a full methodology annex.

Assessment 03

Synthetic Data Service

The problem

Financial institutions cannot share portfolio data for validation, benchmarking, or regulatory exercises due to confidentiality. That bottlenecks model risk management, and it bites hardest at smaller institutions without diverse internal datasets.

The solution

A dual-method platform grounded in peer-reviewed research published in Springer Computational Economics:

  • Gaussian copula generator. Automatic marginal fitting via AIC, Cholesky correlation structure, inverse-CDF transform.
  • Block bootstrap generator. Time-series aware, it preserves autocorrelation and tail dependence.
  • Validation suite. KS tests, correlation preservation (Frobenius), moment comparison, VaR/ES benchmarking.
  • Privacy guard. Distance-to-closest-record, re-identification risk, k-anonymity.

What you receive

  • A synthetic dataset preserving your marginals, correlations and tail behaviour
  • Validation report: KS tests per variable, correlation preservation, moment comparison
  • VaR and Expected Shortfall benchmarked against the real data at 95% / 99% / 99.5%
  • Privacy certificate: distance-to-closest-record, re-identification risk, k-anonymity
  • Methodology annex grounded in peer-reviewed research and written for validator scrutiny
  • Data delivered as CSV, yours to share, publish or hand to a regulator

Delivery: 3–5 working days · enquire for pricing

Market-gap extensions
  • Conditional synthetic data (e.g. "this portfolio in a 2008-style crisis")
  • Multi-table relational synthetic data with referential integrity
  • Fairness-aware data for bias testing in credit scoring
  • Synthetic time series for IFRS 9 lifetime-PD backtesting
  • On-demand API generation for institutional clients
Did you know

Assessment 04 · New

Country Risk & Cross-Border Premium

The problem

Almost every credit model prices distance at exactly zero. Our research on 13,317 transactions worth $11.8 trillion shows that is wrong: cross-border private credit defaults at 6.46% against 2.56% domestically, a premium of 3.91 percentage points and a hazard ratio of 2.86.

The solution

We apply our own peer-reviewed coefficients to your book:

  • Deal-level premium in basis points, by corridor and sector.
  • Hazard multiple. How much faster cross-border exposures fail.
  • Corridor concentration. The top 10 corridors carry 68% of market volume, and we show where yours sit.
  • Incremental expected loss. The cost your current model is not capturing.

What you receive

  • Cross-border premium per exposure, in basis points
  • Hazard multiple and adjusted PD by corridor and sector
  • Corridor concentration and the exposures driving it
  • Incremental expected loss versus domestic-equivalent treatment
  • Written report plus an audit trail citing the research and its limitations
  • Results as Excel and CSV, yours to keep and reuse

Delivery: 3–5 working days · enquire for pricing

Methodology and honesty

Coefficients are applied, not re-estimated on your data, and every report states this plainly. The distance elasticity (−2.41) is estimated on bilateral flow volumes and is used as a corridor-thinness signal, not as a PD driver. Where you do not supply a domestic PD, the sample average is used and will not reflect your underwriting.

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