Serviços de Fundos de Investimento

Business Advisory

Bank risk is a balance sheet in motion. Credit migration, market volatility, funding pressure, counterparty deterioration, and operational breakdowns do not arrive one at a time; they compound through capital, earnings, and liquidity. Phoenix helps banks, bank holding companies, and large federally insured credit unions build a single risk discipline across lending, treasury, trading, collateral, and finance so management can act earlier and regulators can see a cleaner chain of evidence.

Re-architect the enterprise framework across credit risk, market risk, liquidity risk, counterparty risk, operational risk, model risk, interest-rate risk in the banking book, concentration risk, and capital planning so exposures are governed as one connected system rather than separate control silos.
Rework methodologies for commercial and consumer portfolios, securities books, derivatives, repo and financing activity, deposits, wholesale funding, contingent liquidity, collateral pools, and cross-entity exposures so risk appetite matches how the bank actually makes and loses money.
Strengthen scenario governance across stress testing, reverse stress testing, CCAR, DFAST, FRTB, trading-book and banking-book shocks, idiosyncratic events, macro downturns, and management overlays so adverse outcomes are anticipated rather than explained after the fact.
Sharpen supervisory and executive reporting for FR Y-14, board and risk-committee packs, risk appetite metrics, concentration dashboards, CECL support, capital and liquidity views, and NCUA stress-testing outputs so reporting reflects decision-useful risk rather than static summary tables.
For organizations with affiliated EU or UK fund-management activities, align risk governance to UCITS and AIFMD requirements around market, liquidity, leverage, and stress-testing oversight without letting those obligations drift away from the group risk framework.

AltsCentralAI Solutions

Legacy risk engines are built to calculate; they are not built to reason. They can shock a curve, score a borrower, or produce a VaR number, but they struggle to absorb covenant waivers, collateral erosion, news flow, model overrides, treasury behavior, and management action in one place. AltsCentralAI gives banks a risk-reasoning layer that sits above those older engines and gradually replaces the weakest static models where machine learning can do the job better, faster, and with stronger early-warning power.

Build a living exposure map that follows loans, securities, derivatives, deposits, collateral, limits, legal entities, and scenarios through time, so risk teams can see how one change in the business propagates across the rest of the balance sheet.
Replace brittle scorecards and static calibrations with ML challengers and production models for early-warning credit deterioration, PD/LGD/EAD behavior, prepayment and deposit-decay dynamics, liquidity runoff, collateral shortfall prediction, counterparty exposure drift, and market-stress sensitivity forecasting.
Use agentic AI scenario crews to assemble CCAR and DFAST narratives, explain capital and liquidity movements, draft FRTB and stress-testing support packs, reconcile model assumptions, and prepare first-pass responses to management, validation, and supervisory questions.
Put RPA on repetitive control work such as source reconciliation, model-input validation, limit-breach evidence capture, scenario refresh routines, workbook population, management-pack production, and issue-routing across first-line and second-line teams.
Apply quantum-ready optimization where the problem is genuinely multi-constraint: collateral allocation, liquidity-buffer construction, hedge selection, balance-sheet deployment, trading-book capital efficiency, and scenario-set design under capital, funding, and concentration limits.
Introduce a staged replacement path in which AI models run as challengers first, then co-pilots, then governed production tools only where performance, explainability, and model-risk evidence justify retiring older non-ML approaches.

Technology Execution & Delivery

Risk programs break when every function runs on a different clock. Treasury needs intraday funding visibility, market risk needs fast recalculation, credit risk works through periodic review cycles, finance wants quarter-end precision, and supervisors expect one consistent answer across all of it. Phoenix builds the operating layer that lets those clocks coexist, so risk becomes industrialized rather than stitched together in parallel spreadsheets.

Lay down a risk data mesh that connects origination, servicing, collateral, treasury, ALM, trading, market data, CCP and custodian feeds, finance ledgers, and regulatory-reporting stores into one governed architecture.
Stand up cloud compute grids for Monte Carlo, FRTB sensitivities and expected shortfall, xVA workloads, CCAR and DFAST stress runs, scenario libraries, and portfolio-level aggregation without forcing teams to wait on desktop-bound processing.
Create model operations rails for feature stores, validation evidence, challenger runs, explainability outputs, threshold control, drift surveillance, approvals, rollback, and full production lineage across risk model inventories.
Build workflow lanes by role for first-line risk owners, treasury, market risk, credit risk, model risk management, product control, finance, internal audit, and examination-response teams so issues move through the bank in a controlled sequence rather than by inbox.
Industrialize recurring regulatory and management output for FR Y-14, stress-testing submissions, board risk dashboards, limit and concentration reports, capital and liquidity packs, and credit-union stress-test artifacts with less manual assembly and stronger auditability.
Add Phoenix execution depth across quant buildout, ALM and liquidity modernization, FRTB implementation support, CCAR automation, DFAST workflow design, model-risk governance, credit-union capital planning, and enterprise risk program rollout.