Action Plan: An action plan in model risk management is a detailed outline of steps to be taken to address findings or issues identified in models. It specifies tasks, responsibilities, timelines, and resources, providing a structured approach to remediate deficiencies, enhance model performance, and ensure compliance with standards.

AI (Artificial Intelligence): Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. It involves learning from data, reasoning, problem-solving, perception, and natural language understanding. AI models can significantly enhance decision-making, automate complex processes, and uncover insights, but they also pose unique challenges in terms of ethics, bias, and transparency.

Algorithm Bias: Algorithm Bias refers to systematic errors in the output of an algorithm, often reflecting existing social inequalities or biases in the training data. It can lead to unfair or discriminatory outcomes and poses significant ethical and reputational risks. Addressing algorithm bias is crucial for developing responsible and equitable AI and machine learning models.

Algorithm: An algorithm is a set of rules or procedures for solving a problem, often used within models for data processing and calculations. It’s the backbone of any model, dictating how inputs are transformed into outputs. The selection and design of algorithms are crucial for the model’s performance, and their complexity can impact the model’s interpretability and transparency.

AML Model: An Anti-Money Laundering (AML) model is designed to detect and prevent illegal money laundering activities. It analyzes transactions, identifies suspicious patterns, and generates alerts for further investigation. AML models are crucial for financial institutions to comply with regulatory requirements, mitigate financial and reputational risks, and combat financial crimes effectively.

Analytics: Analytics involves the systematic computational analysis of data to derive insights and support decision-making. It utilizes various statistical, mathematical, and computational techniques to interpret, visualize, and communicate data patterns. In the context of MRM, analytics is pivotal for understanding model behavior, assessing performance, and identifying areas for improvement.

Anomaly: An anomaly in model risk management is an irregularity or deviation in model behavior or output from expected patterns or values. Identifying anomalies is vital for maintaining the accuracy and reliability of models, enabling timely interventions and remediations.

Audit (Internal and External): An audit is an independent examination, processes and controls to assess their effectiveness, reliability, and compliance with standards. Internal audits are conducted by an organization’s internal audit function, while external audits are performed by independent external entities. Audits are essential for ensuring model integrity, identifying areas for improvement, and maintaining regulatory compliance.Where model validation typically independently investigates a given model’s effectiveness, an audit has a broader scope that examines the process/governance surrounding the models, including the model validation process itself.

Auditability: Auditability refers to the capability of a model to be examined independently to verify its processes, outcomes, and compliance with relevant standards. It is essential for maintaining transparency, ensuring regulatory compliance, and building trust among stakeholders. A model with high auditability has clear documentation, traceable processes, and is structured in a way that its operations and outcomes can be thoroughly reviewed and validated.

Backtesting: Backtesting is a critical validation technique where a model’s predictions are tested against historical data to assess its accuracy and reliability. It helps in identifying any discrepancies between the model’s forecasts and actual outcomes, providing insights into its effectiveness and areas for improvement. Backtesting is essential for refining models and ensuring they are robust and fit for their intended purpose.

Compliance Framework: A compliance framework is a structured set of guidelines that details an organization’s processes to maintain compliance with laws, regulations, and policies. In model risk management, it outlines the procedures, controls, and roles and responsibilities to ensure that models are developed, validated, and used in accordance with regulatory requirements and ethical standards.

Compliance Standards: Compliance Standards are specific criteria and benchmarks that models must meet to comply with regulatory and organizational policies. They set the minimum requirements for model development, validation, documentation, and usage. Adherence to these standards is crucial for maintaining regulatory compliance, ensuring the integrity of models, and safeguarding against financial and operational risks.

Data Analytics: Data Analytics refers to the process of examining, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It employs various techniques and tools to analyze data from different perspectives and summarize it into meaningful patterns and insights. In model risk management, data analytics is instrumental in understanding model behavior, identifying anomalies, and deriving insights that inform risk mitigation strategies.

Data Quality: Data Quality is the degree of accuracy, consistency, completeness, reliability, and timeliness of data used in model development and validation. High-quality data is crucial for building effective models, as it directly impacts the model’s predictions and insights. Ensuring data quality involves addressing issues like missing values, outliers, and inconsistencies, and it is a prerequisite for achieving reliable and trustworthy model outcomes.

Data-Driven Decision Making: Data-driven decision making involves using empirical data and analysis to inform and guide decisions. It emphasizes the role of data in evaluating options, assessing impacts, and determining strategies. In model risk management, leveraging data-driven insights is crucial to evidence the soundness of models in a given context of use, for managing risks, and aligning decisions with organizational objectives.

Decision-Making Support: Decision-making support involves providing information, analysis, and insights to assist individuals or organizations in making informed and effective decisions. In model risk management, models serve as decision-making support tools, providing predictions, simulated scenarios, and assessments to inform strategies, manage risks, and achieve objectives more optimally.

Efficiency: In the context of model risk management, efficiency refers to the ability of a model to produce accurate and reliable results with minimal resource utilization. It involves optimizing computational processes, reducing redundancies, and enhancing the speed and accuracy of models. An efficient model not only meets its objectives but also aligns with organizational resource constraints, contributing to overall operational effectiveness.

Financial Data: Financial Data consists of numerical information representing financial transactions and monetary values. It is often used as input in financial models to analyze financial performance, assess risks, and make informed decisions. The quality, accuracy, and reliability of financial data are paramount for developing effective financial models and ensuring the validity of their predictions and insights.

Financial Institutions: Financial Institutions are organizations engaged in activities related to monetary transactions, such as banks, investment companies, insurance firms, and credit unions. These institutions often rely on models to assess risks, value assets, and make decisions. Model risk management is especially critical in financial institutions due to the potential impact of model errors on financial stability and regulatory compliance.

Financial Models: Financial models are analytical tools used to simulate financial scenarios, assess risks, and inform financial decisions. They employ mathematical equations, algorithms, and data to analyze financial variables and forecast outcomes. Managing financial models involves addressing complexities, uncertainties, and regulatory requirements to ensure their accuracy, reliability, and compliance.

Finding: A finding in model risk management is a conclusion or determination reached after evaluation or investigation, often during model validation or audit. Findings may reveal issues, risks, or areas for improvement in models, and addressing them is essential for maintaining model integrity, compliance, and effectiveness.

Fintech: Fintech, or Financial Technology, refers to the integration of technology into financial services to enhance their accessibility, efficiency, and innovation. It encompasses a range of technologies and applications, from digital payments to blockchain. In fintech, managing model risks is crucial for maintaining the integrity, security, and reliability of technology-driven financial solutions.

First Line of Defence: In risk management, the first line of defence refers to the functions that own and manage risks, such as business units and model owners. They are responsible for implementing controls, conducting assessments, and ensuring compliance with policies and standards.

Governance Framework: A Governance Framework is a structured approach to align an organization’s governance processes with its objectives and values. It outlines the roles, responsibilities, and procedures for decision-making and control. In the context of model risk management, a governance framework ensures the effective oversight of models, addressing risks and ensuring compliance with organizational policies and external regulations.

Machine Learning Algorithms: Machine Learning Algorithms are computational methods used to train models on data. They define the way models learn patterns and make predictions or decisions. Different algorithms, such as regression, clustering, and neural networks, are suitable for different types of problems, and their selection and tuning are crucial for the performance and reliability of machine learning models.

Machine Learning: Machine Learning is a field of artificial intelligence focused on developing algorithms that enable computers to learn from and make predictions or decisions based on data. It includes various techniques and models, such as neural networks and decision trees. Managing machine learning models involves addressing challenges related to interpretability, bias, and generalization to ensure their ethical and reliable application.

Market Risk Model: A market risk model evaluates the potential impact of market movements on the value of an investment or portfolio. It assesses risks related to changes in interest rates, exchange rates, stock prices, and other market variables. Market risk models are crucial for informing trading strategies, managing financial exposures, and complying with market risk regulations.

Materiality: Materiality gauges the significance of a model in terms of its potential consequences. It’s a measure of the potential impact, be it financial, reputational, or operational, that a model’s error or misuse could have on an organization. Determining materiality is crucial for risk assessment and helps prioritize resources and attention to models that pose the most significant risks.

Model: A model, as defined by Yields.io, is a simplified representation of a system or phenomenon, designed to enhance understanding and predict behavior. It involves mathematical equations, algorithms, and data to simulate real-world scenarios. Models are essential tools for analysis, decision-making, and problem-solving, and their management involves addressing risks related to accuracy, reliability, and misuse.

Model Accuracy: Model Accuracy refers to the degree to which a model’s predictions match the actual outcomes. It is a critical metric in assessing a model’s reliability and effectiveness. High accuracy indicates that the model is performing well, while low accuracy signals potential issues with the model’s design, inputs, or assumptions, necessitating further investigation and refinement.

Model Architect: The Model Architect is responsible for the technical design and structure of the model. They ensure the model is technically sound, scalable, and aligns with the overall system architecture. The architect collaborates with developers and other stakeholders to address technical challenges and ensure the model’s seamless integration into organizational workflows.

Model Attribute: Model attribute refers to a specific characteristic, property, or feature of a model. It can include elements such as inputs, outputs, parameters, algorithms, and metadata. Understanding and managing model attributes are crucial for developing, documenting, and validating models effectively.

Model Certification: Model certification is the formal approval process a model undergoes to verify its accuracy, reliability, and compliance with applicable standards and regulations. It involves rigorous testing, validation, and review by relevant authorities or experts, ensuring that the model is fit for its intended purpose and meets organizational and regulatory requirements.

Model Classification: Model classification involves categorizing models based on their characteristics, purposes, risks, or complexities. It provides a structured approach to managing models, allowing organizations to apply appropriate governance, validation, and monitoring based on the classification.

Model Complexity: This refers to the level of intricacy and sophistication inherent in a model’s structure, relationships, and algorithms. Managing model complexity is essential to balance the trade-off between accuracy and interpretability and to mitigate risks related to overfitting and operationalization. Typical inputs to model complexity estimates are lines of code, number of dependencies, amounts of parameters, etc

Model Creation: Model creation encompasses the entire process of developing a new model. It starts with the ideation phase, where the foundational concept of the model is established, followed by development, validation, and finally, deployment. Throughout this process, the model is refined, tested, and adjusted to ensure it meets its intended purpose and organizational needs.

Model Developer: The Model Developer is tasked with building, testing, and refining the model. They work closely with the owner to ensure the model aligns with its intended purpose and organizational needs. The developer is responsible for acquiring and processing data, and documenting the development process, ensuring the model’s reliability and effectiveness.

Model Documentation Template: A model documentation template provides a standardized format for recording information about a model, including its development, assumptions, limitations, and validation. Utilizing such templates ensures comprehensive and consistent documentation, facilitating model review, audit, and compliance.

Model Documentation: Model Documentation involves creating comprehensive records detailing a model’s development, usage, validation, assumptions, limitations, and performance metrics. It is essential for maintaining transparency, ensuring knowledge transfer, and facilitating model review and validation. Proper documentation is a cornerstone of model governance and is crucial for regulatory compliance and effective risk management.

Model Expectation: Model Expectation refers to the anticipated outcomes or performance a model is designed to achieve. It sets the benchmark for evaluating the model’s effectiveness and aligns the model’s objectives with organizational goals. Clear and realistic expectations are crucial for model development, validation, and monitoring, ensuring that the model meets its intended purpose and contributes value to the organization.

Model Governance: Model Governance refers to the specific policies, procedures, and responsibilities that ensure the effective management of models within an organization. It involves overseeing the development, validation, deployment, and retirement of models to ensure their compliance with regulatory and organizational standards. Model governance is a subset of overall governance and typically operates within the broader governance framework of an organization.

Model Input: Model Input consists of the data, parameters, and assumptions fed into a model for processing. The quality and accuracy of inputs directly impact the reliability of model outputs. Ensuring accurate and relevant inputs is paramount in model development and validation, and it involves addressing data quality issues, validating assumptions, and managing uncertainties.

Model Inventory: Model Inventory, as defined by Yields.io, is a comprehensive repository containing all relevant information about each model within an organization. It includes metadata, documentation, performance metrics, and other essential details. A well-maintained model inventory is crucial for effective model governance, risk management, and regulatory compliance, providing a centralized view of models and their statuses.

Model Inventory Rationalization: Model Inventory Rationalization, as defined by Yields.io, is the process of optimizing the variety and management of models within an organization. It involves evaluating the necessity, performance, and risks of models to streamline their usage, enhance their governance, and allocate resources effectively to maintain them.

Model Issue Classification: Model Issue Classification involves categorizing issues or problems identified during model validation based on their severity and impact. It helps in prioritizing remediation efforts and allocating resources effectively. Addressing and resolving classified issues are vital for maintaining model integrity and mitigating risks associated with model errors or inadequacies.

Model Lifecycle: The Model Lifecycle, as defined by Yields.io, encompasses the various stages a model goes through, from initial development to eventual retirement. Each stage requires specific management activities, including development, validation, deployment, monitoring, and retirement. Managing models effectively throughout their lifecycle is essential for ensuring their reliability, compliance, and continuous alignment with organizational objectives.

Model Metadata: Model metadata provides a deeper understanding of a model by offering contextual and additional information. Think of it as the “label” on a product, detailing its ingredients, usage instructions, and more. For models, metadata might include its assumptions, limitations, performance metrics, and other essential details. This information is crucial for validation, governance, and effective utilization of the model.

Model Monitoring: Model Monitoring is the continuous oversight of a model’s performance and operational integrity after its deployment. It involves tracking the model’s outputs, assessing its predictive accuracy, and detecting any deviations or anomalies. Regular and effective monitoring is crucial for maintaining model reliability, ensuring ongoing compliance, and identifying and addressing issues promptly.

Model Oversight: Model oversight involves the governance and review mechanisms put in place to ensure that models are developed, validated, and used appropriately and effectively. It is essential for maintaining the integrity, reliability, and compliance of models, fostering accountability and continuous improvement in model risk management.

Model Owner: The Model Owner is the individual or entity accountable for the model throughout its lifecycle. They are responsible for ensuring the model meets its intended purpose and complies with organizational policies and regulatory requirements. The owner oversees the model’s development, validation, deployment, and eventual retirement, ensuring its overall effectiveness and addressing any issues that arise.

Model Risk: Model Risk, as defined by Yields.io, is the potential for adverse outcomes arising from model errors, misuse, or inadequacies. It can impact financial stability, regulatory compliance, and organizational reputation. Managing model risk involves identifying, assessing, and mitigating risks associated with the development, deployment, and use of models, ensuring their reliability and integrity.

Model Risk Management: Model risk management is the discipline of dealing with the risk associated with the development, deployment, and usage of models. It involves identifying, assessing, mitigating, and monitoring model risks to ensure organizational objectives are met, and regulatory compliance is maintained.

Model Risk Management Framework: The Model Risk Management Framework is a structured approach designed to identify, measure, and manage the risks associated with the use of models in decision-making. It encompasses policies, procedures, and practices to ensure models are developed, validated, and used responsibly. The framework aims to mitigate the potential adverse impact of model risk on an organization’s financial stability and reputation.

Model Risk Measurement: Model Risk Measurement involves quantifying the risks associated with using a particular model. It assesses the potential impact of model errors, uncertainties, and limitations on organizational objectives. Quantitative assessment of model risk is crucial for prioritizing risk mitigation efforts, allocating resources, and making informed decisions regarding model deployment and usage.

Model Risk Quantification: Model risk quantification involves measuring the potential impact and likelihood of risks associated with models. It provides a numerical representation of model risks, informing risk management strategies, resource allocation, and decision-making processes. Quantifying model risk is essential for prioritizing risks, assessing vulnerabilities, and ensuring the effective mitigation of adverse outcomes in line with risk appetite.

Model Specification: Model Specification involves detailing the structure, variables, parameters, and equations of a model. It provides a clear and comprehensive description of how a model operates and what it aims to achieve. Proper specification is essential for model development, validation, and interpretation, ensuring that the model is transparent, understandable, and aligned with its intended purpose.

Model Status: Model status denotes the current phase of a model within its lifecycle, such as development, validation, deployment, or retirement. Monitoring and managing model status are essential for effective model governance, ensuring that models are only used when they have been run through the proper process.

Model Template: A model template serves as a standardized blueprint for capturing information on models, incorporating predefined structures, variables, and parameters. Using model templates ensures that model information is collected with consistency and comprehensively from all model stakeholders.

Model Tier Classification: This system categorizes models based on their complexity and materiality. By classifying models into different tiers, organizations can ensure that each model receives the appropriate level of scrutiny and validation. The tier classification aids in risk management by highlighting models that require more rigorous oversight due to their potential impact.

Model Type: Model type refers to the classification of models based on their mathematical structure, functionality, or application. Different types of models, such as predictive models, simulation models, and financial models, serve various purposes and require specific approaches for development, validation, and management. Understanding model types is crucial for applying appropriate methodologies and addressing the unique challenges and risks associated with each type.

Model Uncertainty: Model uncertainty refers to the inherent limitations and unknowns in models arising from approximations, assumptions, and data limitations. It acknowledges the variability and unpredictability in model outcomes due to the incomplete or imprecise set of information available. Managing model uncertainty involves addressing data quality, validating assumptions, conducting sensitivity analysis, and maintaining transparency about model limitations often through understanding the impact of model parameters variability

Model User: The Model User is the individual or team that actively utilizes the model for decision-making processes, forecasting or any other organization function. They interpret the model’s outputs and apply them in alignment with the model’s intended purpose and organizational objectives. The user’s feedback is essential for continuous improvement and ensuring the model remains relevant and valuable to the organization.

Model Validation Frequency: Model Validation Frequency refers to how regularly a model undergoes validation to ensure its continued reliability and effectiveness. The frequency is often determined by the model’s complexity, usage and regulatory requirements. Regular validation is essential for identifying and addressing issues promptly, ensuring compliance, and maintaining the model’s alignment with organizational objectives.

Model Validation: Model validation is the set of processes and activities intended to verify that models are accurate, consistent, and fit for purpose. It is essential for ensuring the reliability, robustness, and performance of models, addressing their limitations, and maintaining compliance with regulatory standards. Model validation is best done by a team that is independent from the model development team.

Performance Metrics: Performance Metrics are quantitative measures used to assess a model’s effectiveness and efficiency in achieving its objectives. They provide insights into the model’s accuracy, reliability, and overall performance. Selecting appropriate metrics is crucial for evaluating models objectively and making informed decisions regarding their development, refinement, and deployment.

Predictive Modeling: Predictive Modeling involves the use of statistical algorithms and data to forecast future outcomes. It’s a type of modeling that leverages historical and current data to make predictions about unseen or future events. It’s crucial in various domains, including finance and healthcare, to inform decision-making, assess risks, and identify opportunities.

Process: In the context of model risk management, a process refers to a series of actions or steps taken to achieve a particular end, such as developing, validating, or monitoring models. Defining and following structured processes are crucial for maintaining consistency, quality, and compliance in managing models throughout their lifecycle.

RACI Matrix: The RACI Matrix is a responsibility assignment chart that clarifies roles and responsibilities during a project or organizational process. Standing for Responsible, Accountable, Consulted, and Informed, it ensures that every task or process has a clear owner and that stakeholders are appropriately engaged. It’s particularly useful in model risk management to delineate responsibilities throughout the model lifecycle.

Regulatory Compliance: Regulatory Compliance is the adherence to laws, regulations, and guidelines relevant to an organization’s operations. In model risk management, it involves ensuring that models are developed, validated, and used in accordance with applicable regulatory standards or guidance, mitigating legal and reputational risks and promoting responsible and ethical practices.

Regulatory Guidelines: Regulatory Guidelines are recommendations issued by regulatory bodies that outline the principles and procedures organizations should follow. In model risk management, these guidelines often provide a framework for developing, validating, and using models responsibly, ensuring transparency, accountability, and adherence to ethical and legal standards.

Regulatory Reporting: Regulatory Reporting is the submission of accurate and timely information to regulatory bodies to demonstrate compliance with applicable regulations. It’s a critical aspect of governance in financial institutions and other regulated entities, ensuring transparency and accountability and enabling regulators to monitor risks and compliance effectively.

Regulatory Self-Assessment: Regulatory self-assessment is the process by which organizations evaluate their compliance with applicable laws, regulations, and standards. In model risk management, it involves assessing models against regulatory requirements and guidelines, identifying compliance gaps, and implementing remediation measures.

Resilience: In model risk management, resilience refers to the ability of models and the organization to adapt and recover from disruptions, errors, or failures. It is crucial for maintaining the continuous and reliable functioning of models and for fostering organizational stability and adaptability.

Retirement Phase: Every model has a lifespan. The retirement phase marks the end of a model’s active use. Models are typically retired when they become obsolete, are replaced by more effective alternatives, or no longer align to current business practices. 

Reverse stress testing : This is a risk assessment technique that starts with a catastrophic scenario and works backward to determine how a model failure, error, or misjudgment could lead to this extreme scenario. It helps identify the conditions under which a model’s limitations could have severe adverse consequences, allowing organizations to assess their vulnerability and the potential impact on financial stability or other critical outcomes. This process aids in enhancing model robustness and risk mitigation strategies to prevent or minimize such extreme adverse events, ensuring model reliability in critical decision-making processes.

Risk Assessment: Risk Assessment is the process of identifying and analyzing potential risks that could impact an organization. It involves evaluating the likelihood and impact of different risks and informing the development of strategies to mitigate them. In model risk management, risk assessment is crucial for understanding the potential adverse effects of models and prioritizing risk mitigation efforts.

Risk Management: Risk Management is the coordinated activities to direct and control an organization with regard to risk. It involves identifying, assessing, managing, and monitoring risks to protect organizational assets, achieve objectives, and ensure compliance with laws and regulations. In the context of models, risk management encompasses the strategies and practices to mitigate the risks associated with the development, deployment, and use of models.

Risk Mitigation: Risk Mitigation involves implementing strategies and actions to reduce the impact and likelihood of identified risks. It’s a crucial component of risk management, aiming to manage risks proactively and minimize their adverse effects on organizational objectives. In model risk management, it involves addressing the risks associated with models through validation, governance, and continuous monitoring.

Scenario Analysis: Scenario Analysis is a method of predicting future value movements by examining different possible future states of the world. It’s used extensively in risk management and financial planning to assess potential outcomes and to develop strategies for various future scenarios. In model risk management, scenario analysis helps in understanding the potential impacts and vulnerabilities of models under different conditions.

Second Line of Defence: The second line of defence includes functions that oversee risk management and compliance, such as risk management and compliance departments. They provide support, guidance, and independent oversight to ensure effective risk management across the organization.

Simulation: Simulation is a numerical technique used to study the behavior of a system by projecting its operation over time under different scenarios. It’s used in model risk management to assess the performance and reliability of models under varying conditions and assumptions, providing insights into their adaptability and robustness.

Stakeholder Communication: Stakeholder Communication involves the exchange of information between individuals or groups who have an interest in a particular issue or organization. Effective communication is crucial for building relationships, managing expectations, and ensuring transparency and understanding among stakeholders, especially in the context of model development, validation, and use.

Strategic Planning: Strategic Planning is the organizational management activity used to set priorities, focus energy and resources, and ensure that employees and other stakeholders are working toward common goals. In model risk management, strategic planning involves considering the role and impact of models in achieving organizational objectives and managing associated risks.

Third Line of Defence: The third line of defence comprises independent functions that provide assurance on the effectiveness of governance, risk management, and internal controls, such as internal audit. They conduct independent assessments and audits to verify compliance and identify areas for improvement.

Tool (Referring to Model vs Tool): In the context of model risk management, a tool refers to a simpler analytical solution or calculation used to support decision-making processes, distinct from more complex models. Distinguishing between tools and models is important for applying appropriate governance, validation, and oversight based on complexity and risk.

Transparency: Transparency in model risk management refers to the clarity and openness regarding the methodologies, assumptions, and limitations of models. It’s essential for building trust among users and stakeholders and for ensuring the responsible and ethical use of models. Transparent models have clear documentation, understandable methodologies, and disclosed limitations.

Validation Reports: Validation Reports are comprehensive documents that among other things detail the methodology, findings, and recommendations of the model validation process. They are crucial for communicating, possibly with an independent view, the reliability, accuracy, and limitations of models to stakeholders and for evidencing compliance with external regulatory and internal organizational standards.