high match confidence
Sentence-level differences:
- Reworded sentence: "Examples of model use include determining the pricing of various products, identifying potentially fraudulent or suspicious transactions, marketing to potential customers, grading loans and extending credit, measuring interest rate and other market risks, predicting or estimating losses, and assessing capital adequacy."
- Reworded sentence: "For example, our models may not be effective if historical data does not accurately represent future events or environments or if our models rely on erroneous, incomplete, biased, or otherwise flawed data, formulas, algorithms or assumptions and our internal model review processes fail to detect and address these flaws."
- Reworded sentence: "Finally, flaws in our models that negatively impact our customers or our ability to comply with applicable laws and regulations could negatively affect our reputation or result in fines and penalties from our regulators."
Current (2026):
We use financial and statistical models throughout many areas of our business, relying on them to inform decision making, automate processes, and estimate many financial values. Although it currently impacts a minority of the overall number of models that we use, we increasingly…
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We use financial and statistical models throughout many areas of our business, relying on them to inform decision making, automate processes, and estimate many financial values. Although it currently impacts a minority of the overall number of models that we use, we increasingly use models related to how we do business with customers and for internal process automation that leverage AI/machine learning algorithms. These models can be more predictive, but because of the complex way in which the many variables in AI/machine learning models interact, the results of these models are often less interpretable than traditional statistical models. Examples of model use include determining the pricing of various products, identifying potentially fraudulent or suspicious transactions, marketing to potential customers, grading loans and extending credit, measuring interest rate and other market risks, predicting or estimating losses, and assessing capital adequacy. We depend significantly on models for credit loss accounting under CECL, capital stress testing and estimating the value of items in our financial statements. Models generally predict or infer certain financial outcomes, leveraging historical data and assumptions as to the future, often with respect to macroeconomic conditions. Development and implementation of some of these models, such as the models for credit loss accounting under CECL, require us to make difficult, subjective and complex judgments. Other models are used to support decisions made regarding how we do business with customers. Poorly designed or implemented models present the risk that our business decisions based on information incorporating model output will be adversely affected due to the inadequacy of that information. For example, our models may not be effective if historical data does not accurately represent future events or environments or if our models rely on erroneous, incomplete, biased, or otherwise flawed data, formulas, algorithms or assumptions and our internal model review processes fail to detect and address these flaws. Models, if flawed, could cause information we provide to the public or to our regulators to be inaccurate, incomplete or misleading. Some of the decisions that our regulators make, including those related to capital distribution to our shareholders, would likely be affected adversely if they perceive that the quality of the relevant models we use is insufficient. Finally, flaws in our models that negatively impact our customers or our ability to comply with applicable laws and regulations could negatively affect our reputation or result in fines and penalties from our regulators. Moreover, our use of AI/machine learning algorithms is subject to a variety of existing laws and regulations, including intellectual property, privacy (including with respect to automated decision making), consumer protection and federal equal opportunity laws and regulations, and additional new laws and regulations, and new applications or interpretations of existing laws and regulations, related to AI/machine learning algorithms may impact our ability to develop, use and commercialize AI/machine learning algorithms. The PNC Financial Services Group, Inc. – 2025 Form 10-K 21 The PNC Financial Services Group, Inc. – 2025 Form 10-K 21 The PNC Financial Services Group, Inc. – 2025 Form 10-K 21
View prior text (2025)
We use financial and statistical models throughout many areas of our business, relying on them to inform decision making, automate processes, and estimate many financial values. Although it currently impacts a minority of the overall number of models that we use, we increasingly use models related to how we do business with customers and for internal process automation that leverage AI/machine learning algorithms. These models can be more predictive, but because of the complex way in which the many variables in AI/machine learning models interact, the results of these models are often less interpretable than traditional statistical models. Examples of model uses include determining the pricing of various products, identifying potentially fraudulent or suspicious transactions, marketing to potential customers, grading loans and extending credit, measuring interest rate and other market risks, predicting or estimating losses, and assessing capital adequacy. We depend significantly on models for credit loss accounting under CECL, capital stress testing and estimating the value of items in our financial statements. Models generally predict or infer certain financial outcomes, leveraging historical data and assumptions as to the future, often with respect to macroeconomic conditions. Development and implementation of some of these models, such as the models for credit loss accounting under CECL, require us to make difficult, subjective and complex judgments. Other models are used to support decisions made regarding how we do business with customers. Poorly designed or implemented models present the risk that our business decisions based on information incorporating model output will be adversely affected due to the inadequacy of that information. For example, our models may not be effective if historical data does not accurately represent future events or environments or if our models rely on erroneous data, formulas, algorithms or assumptions and our internal model review processes fail to detect and address these flaws. Models, if flawed, could cause information we provide to the public or to our regulators to be inaccurate or misleading. Some of the decisions that our regulators make, including those related to capital distribution to our shareholders, would likely be affected adversely if they perceive that the quality of the relevant models we use is insufficient. Finally, flaws in our models that The PNC Financial Services Group, Inc. – 2024 Form 10-K 25 The PNC Financial Services Group, Inc. – 2024 Form 10-K 25 The PNC Financial Services Group, Inc. – 2024 Form 10-K 25 negatively impact our customers or our ability to comply with applicable laws and regulations could negatively affect our reputation or result in fines and penalties from our regulators.