With deep expertise in both modeling and industry practice, our team provides thorough and rigorous validation processes tailored to your institution's unique needs.
Beyond compliance with SR 11-7, we deliver valuable insights that enhance the reliability and decision-making capabilities of your models.
Our experienced practitioners not only validate your models but also offer actionable recommendations to optimize performance and monitoring, improve model governance, and strengthen overall risk management strategies.
We also offer comprehensive sample-based or full-scope model benchmarking, using both third-party vendor models and proprietary in-house frameworks. This option provides clients with an objective reference point to assess model performance, assumptions, and alignment with industry practices.
Whether you are a bank, credit union, or fintech, our validation services are designed to provide clarity, confidence, and added value in your model risk management efforts.
We validate the following model types:
We deliver comprehensive, independent assessments of your entire IRR/ALM modeling and framework, including:
Chart of Accounts and Data Mapping
We evaluate the integrity and granularity of your chart of accounts and ensure accurate, consistent mapping from core systems to your model.
Assumption Review and Governance
We assess the foundation and support for key behavioral assumptions - including non-maturity deposit (NMD) decay rates and betas, loan and investment prepayment assumptions, and reinvestment strategies - benchmarking them against peer data, historical behavior, and current market dynamics.
Balance Sheet Optionality
Our team analyzes embedded options, such as early withdrawal features on CDs, callable securities, and prepayment risks, to evaluate whether the model captures the economic reality of optionality in both EVE and EAR simulations.
Risk Measurement & Reporting
We evaluate model accuracy and consistency across both earnings at risk (EAR) and economic value (EVE), including stress testing and scenario analysis. We also assess the alignment of model outputs with policy limits, board reporting, and internal risk appetite frameworks.
Model Documentation & Governance
We review model documentation for clarity, completeness, and consistency with actual practices. Our validation includes an assessment of the model governance structure - model ownership, change management, controls, and validation cycles - ensuring alignment with regulatory expectations (e.g., SR 11-7, OCC 2011-12).
Vendor Model Expertise
We have deep modeling and validation experience with the industry’s leading ALM platforms, including, but not limited to:
Moody’s ZMDesk / OnlineALM
Empyrean ALM
BancWare
Profitstar
As well as many other in-house or third-party solutions.
Our team is experienced with all major CECL methodologies, including:
Weighted Average Remaining Maturity (WARM)
Probability of Default / Loss Given Default (PD/LGD)
Discounted Cash Flow (DCF)
Vintage Analysis
Static Historical Loss Rate
We conduct comprehensive, end-to-end validations covering all critical components and assumptions of your CECL framework, including:
Portfolio Segmentation and Pooling Logic
We evaluate whether loan segmentation is appropriate and consistent with shared credit risk characteristics, including reviews of risk rating hierarchies, product types, vintage, geography, and other drivers of expected loss.
Forecasting and Reversion Techniques
We assess the methodology and support for the reasonable and supportable forecast period, the reversion approach (e.g., immediate vs. straight-line), and the appropriateness of the macroeconomic variables used in forecasting.
Behavioral Assumptions
Our reviews include prepayment and curtailment assumptions, contractual cash flows, and amortization schedules to ensure that model calculations properly reflect the timing and volume of expected credit losses.
Qualitative Adjustment Factors (Q-Factors)
We examine the framework and documentation supporting management judgment overlays, including alignment with the Interagency FAQs, historical backtesting, and internal controls around the adjustment process.
Model Documentation & Governance
We evaluate the clarity, completeness, and consistency of model documentation and internal governance structures, including model development rationale, validation history, change control, and model owner responsibilities.
Vendor Model Experience
ValuRisk Partners has worked extensively with the industry’s leading CECL model platforms, including, but not limited to:
Abrigo Sageworks
Invictus Group
nCino
Primatics EVOLV
Moody’s ImpairmentStudio
Models developed in platforms such as Python, R and SAS
As well as many other in-house or third-party solutions.
In an environment where funding risk can materialize rapidly, we help institutions ensure that their liquidity stress testing models are not only technically sound, but also aligned with their Contingency Funding Plan (CFP), internal risk tolerance, and supervisory guidance.
Our validations encompass a comprehensive review of the entire liquidity stress testing framework, including:
Scenario Design and Thematic Coverage
We evaluate the structure and relevance of stress scenarios to ensure a balanced, thematic approach - covering both market-wide disruptions (e.g., systemic events) and institution-specific scenarios (e.g., reputational risk, credit rating downgrades). We assess the severity, duration, and rationales for each scenario, and ensure they reflect evolving risk environments.
Time Horizon, Bucketing, and Liquidity Gaps
We review the timing of stress assumptions and the use of maturity buckets across near-, medium-, and long-term horizons to ensure appropriate liquidity gap analysis. This includes assessing how cash flows, funding availability, and contingent liabilities are distributed and modeled over time.
Key Behavioral and Funding Assumptions
Our review includes:Deposit outflow rates segmented by depositor type, account characteristics, and relationship depth
Loan drawdowns and credit line utilization
Cash inflows/outflows under stress
Haircuts on liquid asset buffers
Availability of funding sources, distinguishing between secured vs. unsecured funding, as well as reliance on wholesale, brokered, and central bank lines
Deposit Granularity and Relationship Analysis
We assess the granularity of deposit modeling, including segmentation by customer type (retail, commercial, public funds), behavioral characteristics, and customer relationship strength - factors critical for estimating stress outflows realistically.
Validation of Funding Access
We ensure that contingent funding lines are periodically tested, with operational readiness confirmed and appropriately documented. This includes assessing the documentation and monitoring of collateral availability and eligibility.
Governance, Policy Alignment & Documentation
We review all related documentation, including the Contingency Funding Plan, liquidity stress testing policies, and model governance frameworks. This ensures alignment between policy intent, model implementation, and board-level reporting.
Model Types and Platforms
We work with a wide range of liquidity stress testing models, including, but not limited to:Excel-based models
Vendor models such as Empyrean, Moody’s ZM, Bancware and ProfitStar
In-house built projection engines
Whether your capital stress testing framework is driven by internal planning, regulatory requirements (e.g. CCAR-like processes), or board-level oversight, our independent validations ensure your models are conceptually sound, data-driven, and aligned with your institution’s risk profile.
Our capital stress testing validations offer a comprehensive review of all key components, including:
Scenario Development and Alignment with Risk Profile
We assess your stress scenarios for relevance, severity, and plausibility, ensuring alignment with your institution’s unique risk exposures, business model, and operating environment. Scenarios typically include baseline, adverse, and severely adverse conditions, and may incorporate both macroeconomic and idiosyncratic stress events.
Forecasting Framework and Model Structure
We review the end-to-end capital forecasting engine, including:Pre-provision net revenue (PPNR) modeling
Credit loss forecasting, including CECL-based integration
Provisioning and allowance impacts
Balance sheet and income statement projections
Capital action assumptions (e.g., dividends, share repurchases, capital raises)
Assumption Review and Data Integrity
We evaluate assumptions related to loan growth or contraction, interest rate sensitivity, fee income volatility, operating expenses, and tax implications. This includes assessment of data sources, historical calibration, and consistency across business lines and scenarios.
Regulatory Capital Metrics and Stress Results
We assess the model's ability to project regulatory capital ratios (e.g., CET1, Tier 1, Total Risk-Based Capital, Leverage Ratio) under stress, including reconciliation with internal capital adequacy targets and regulatory thresholds.
Integration with Capital Planning & Risk Appetite
We ensure that the stress testing framework feeds into your capital planning process, risk appetite statement, and strategic decision-making.
Documentation, Governance & Validation Standards
Our review includes a full assessment of model documentation, change management, and governance processes. We ensure that your institution has a well-documented and supportable framework that meets regulatory guidance such as SR 15-18 (for larger firms), or proportional approaches for community banks and credit unions.
Model Types and Platforms
We work with a wide range of capital stress testing models, including, but not limited to:Excel-based forecasting tools
Vendor models such as Empyrean, Moody’s ZM, Bancware and ProfitStar
In-house built projection engines
In-house built projection engines
Integrated ALM/CECL/capital stress testing platforms
Our independent validations are tailored for institutions using loan-level credit models to assess loss and capital impacts across a range of stress scenarios, whether for capital planning, or risk appetite monitoring.
We provide a comprehensive review across all key components of the credit stress testing framework:
Scenario Development and Risk Alignment
We assess the design and severity of macroeconomic and idiosyncratic scenarios to ensure they are tailored to your credit portfolio’s risk profile and business model. Scenarios typically include stressors like rising unemployment, declining property values, rate shocks, and sector-specific downturns (e.g., CRE, C&I, consumer).
Loan-Level Credit Loss Forecasting
Our team evaluates the stress testing engine’s ability to forecast expected credit losses at the loan level, including:Probability of Default (PD) modeling under stress
Loss Given Default (LGD) assumptions and haircuts
Exposure at Default (EAD) projections
Incorporation of behavioral assumptions such as prepayments, curtailments, and utilization of unfunded lines
Segmentation and Risk Drivers
We review the appropriateness of loan segmentation, risk drivers, and model inputs such as LTV, DSCR, FICO, internal risk ratings, and industry classifications. Our validations ensure that credit deterioration is realistically modeled based on historical relationships and forward-looking macroeconomic paths.
Assumptions and Model Transparency
We examine the credibility and support for all key assumptions, including credit migration paths, cure rates, recovery lags, and portfolio dynamics under stress. We assess whether overlays or qualitative adjustments are documented, repeatable, and grounded in analysis.
Capital Impact and Integration
We validate the linkage between loan-level losses and projected capital impacts, ensuring stress results appropriately flow into earnings, allowance, and capital forecasts. This includes alignment with CECL provisioning, capital buffers, and internal planning thresholds.
Governance, Documentation & Policy Alignment
Our review includes the model’s alignment with your credit risk management framework, stress testing policies, and capital planning processes. We assess governance controls, version tracking, change management, and the clarity and completeness of model documentation.
Modeling Platforms & Environments
We work with a variety of modeling platforms, including:In-house loan-level stress testing engines
CECL-integrated stress testing models
Vendor tools embedded within credit or ALM systems
Python, R, SAS, or Excel-based models with statistical or rule-based approaches
Whether you're using custom-developed models, bureau-based models, or vendor platforms, we deliver rigorous validations tailored to your institution’s credit products, customer base, and risk strategy.
Our validations cover a full range of credit scoring and decisioning models, including:
Application Scorecards (Consumer & Commercial)
Behavioral and Account Management Scorecards
Collections and Recovery Scorecards
Credit Line Management Models
Machine Learning and Hybrid Models
We perform a comprehensive, end-to-end assessment of your model, including:
Conceptual Soundness and Model Design
We evaluate the model’s purpose, structure, and development methodology, ensuring it aligns with the institution’s lending strategy, credit policy, and risk segmentation needs. For statistical models, we assess variable selection, binning, and transformations for logic, stability, and predictive power.
Data Quality and Variable Integrity
We review the source, sufficiency, and consistency of input data used in model development and implementation. This includes assessing the handling of missing values, outliers, and potential biases, as well as the mapping of bureau data and internal attributes.
Model Performance & Discriminatory Power
We test the model’s performance using standard validation metrics such as:
KS Statistic
Gini Coefficient
Population Stability Index (PSI)
Characteristic Analysis (IV, WoE)
Override and approval rate tracking
Our analysis includes both development sample backtesting and out-of-sample / out-of-time validation where available.
Decisioning Logic and Cutoff Strategies
We validate the logic and thresholds used in automated decisioning rules, including approvals, declines, referrals, and exceptions. We assess whether decision strategies are calibrated to portfolio goals, regulatory constraints, and risk appetite.
Fair Lending and Bias Testing
If requested, our validations include reviews for disparate impact and unintended bias across protected classes (e.g., race, gender, age), consistent with fair lending regulations. We assess both model inputs and outcomes for compliance and ethical soundness.
Model Governance and Documentation
We assess model documentation, development records, version control, and the model’s integration into your model risk management (MRM) framework. We also review policies and procedures for model monitoring, periodic revalidation, and exception handling.
Platform & Vendor Coverage
ValuRisk Partners supports validations across a wide range of environments, including:Vendor platforms (e.g., FICO, Experian Decision Analytics,)
Custom-built SAS, R, Python, or SQL scorecards
Excel-based rule engines and hybrid approaches
Whether you're using a vendor solution or in-house model, our validation framework ensures your AML program's analytical components are effective, transparent, and defensible in the eyes of regulators and internal stakeholders.
We provide comprehensive validations of key BSA/AML model components, including:
Transaction Monitoring Systems
We assess the model's ability to detect potentially suspicious activity by reviewing:Scenario logic and typology coverage (e.g., structuring, layering, rapid movement of funds)
Thresholds, parameters, and tuning
Alert generation rates, effectiveness, and false positive metrics
Coverage analysis to ensure all relevant products, channels, and customer types are monitored
Data mapping and ingestion from core and ancillary systems
Customer Risk Rating Models
We review methodologies used to assign initial and ongoing risk ratings to customers, including:Risk factor weighting (e.g., geography, occupation, entity type, product usage)
Scoring logic, segmentation, and overrides
Consistency across onboarding and periodic reviews
Governance over changes to rating models and risk classifications
OFAC / Sanctions Screening Systems
We assess the effectiveness of real-time and batch screening models, including:Matching logic (fuzzy, phonetic, exact)
List management and updates (OFAC, custom lists)
Hit review workflows
Model tuning and effectiveness of name-matching algorithms
Data Integrity & Mapping
We perform a detailed review of source data quality, field mapping, completeness, and transformation logic. This ensures AML models are relying on accurate, timely, and consistent information across transaction types, accounts, and customer profiles.
Model Governance and Documentation
We evaluate the AML model lifecycle management process, including:Model documentation and change control
Threshold tuning and scenario governance
Periodic backtesting and effectiveness reviews
Integration into the broader Model Risk Management (MRM) framework
Vendor & Platform Expertise
We validate AML models across a wide range of platforms, including, but not limited to:Actimize
Verafin
Patriot Officer
BAM+
Yellow Hammer
In-house SQL or rules-based systems
Whether you're using traditional credit scorecards, automated decision engines, or advanced machine learning models, our validations help ensure your systems are transparent, consistent, and free from bias—from applicant targeting through final loan disposition and reporting.
We perform comprehensive, independent validations of Fair Lending analytics across all model types and use cases:
1. Underwriting and Credit Decisioning Models
We evaluate credit models for potential disparate impact across protected classes (e.g., race, ethnicity, gender, age) using methods such as:
Adverse impact ratio testing
Proxy methodologies (e.g., BISG, surname/geocoding)
Comparison of approval, decline, and referral rates
2. Pricing and Rate Assignment Validations
We review pricing models for fair and consistent application across similarly situated applicants by evaluating:
Risk-based pricing logic
Fee and rate dispersion analysis
Exception tracking and discretionary pricing practices
Alignment with rate sheets and policy documentation
3. Marketing, Targeting, and Prequalification Models
We review models and segmentation strategies used in marketing or pre-screening to ensure:
Inclusive outreach across demographics and geographies
No disparate targeting or exclusionary practices
Transparent eligibility criteria for offers and outreach campaigns
4. HMDA & LAR Data Integrity Reviews
Accurate and complete HMDA reporting is critical for fair lending compliance. We conduct a detailed end-to-end validation of your Loan/Application Register (LAR), including:
Data mapping and transformation reviews from origination and servicing platforms to the LAR
Field-level accuracy checks (e.g., race, ethnicity, sex, age, income, action taken, denial reasons, pricing)
Cross-system reconciliation to ensure consistency between application systems and HMDA submissions
Review of edit check resolution processes and exception tracking
Verification of applicant data input sources, including the accuracy of collected demographic information
Our validation ensures that your LAR reflects actual application and decision data accurately and meets CFPB reporting standards.
5. Governance, Monitoring, and Remediation
We assess the governance and oversight of your Fair Lending and HMDA programs, including:
Model documentation and version control
Ongoing disparate impact monitoring
Internal and external Fair Lending audit trails
Board and management-level reporting
Procedures for remediation and change management
Platform & Vendor Experience
ValuRisk Partners supports a wide range of platforms and environments, including:
Fair Lending and LAR/HMDA systems from RiskExec, Fair Lending Wiz, Ncontracts, etc.
Custom models built in Python, R, SAS, SQL, or Excel