This work reviews and tests risk allocation strategies in finance, highlighting Shapley allocation's advantages.
problem Risk allocation in financial institutions with non-additive risk measures and layered structures.
method Systematic review of risk allocation strategies, testing in simplified and realistic settings, including Basel 2.5 and FRTB.
result Shapley allocation offers the best compromise between simplicity, mathematical properties, and computational cost.
MRC improves credit assignment in multi-agent LLM systems, achieving high returns and transparency.
problem Lack of principled credit assignment in multi-agent LLM decision systems, vulnerability to regime shifts, and limited transparency.
method Market Regime Council (MRC) computes exact Shapley credits, uses exponentially weighted performance histories, Bayesian adaptive mixture, and regime-dependent multipliers.
result MRC achieves a Sharpe ratio of 1.51 and a cumulative return of 440.1% over 1,037 trading days, ranking first on CR, SR, and IR.
Federated Machine Learning (FML) creates an ecosystem for multiple parties to collaborate on building models while protecting data privacy for the participants. A measure of the contribution for each party in FML enables fair credits allocation. In this paper we develop simple but powerful techniques to fairly calculat…
The paper introduces a new method for interpreting model predictions by considering both direct and indirect effects.
problem Interpreting model predictions to understand the causes of decisions.
method Proposes a new approach to quantify feature relevance by combining different types of interpretations and measures.
result Integrates various types of interpretations and measures to provide meaningful insights into model predictions.
The Shapley value theory is used for risk allocation in non-orthogonal risk factors.
problem Risk allocation among non-orthogonal risk factors in financial portfolios.
method Using Shapley value from cooperative game theory to allocate risk contributions.
result Explicit formulas and numerical algorithms for calculating risk allocations are derived.
New sampling methods improve Shapley values for explaining machine learning predictions.
problem Computational limitations in calculating Shapley values for complex models.
method Asymptotic normality results and paired-sampling approximations (KernelSHAP and PermutationSHAP).
result Paired-sampling PermutationSHAP provides exact results for interactions of maximal order two and has the additive recovery property.
The paper explains credit decisions using Shapley decomposition for adverse actions.
problem Identifying predictors responsible for adverse credit decisions.
method Develops a simple and intuitive approach based on Shapley decomposition for models with low-order interactions.
result Shows the approach generalizes to Shapley decomposition and Baseline Shapley.
Shapley Flow interprets model predictions using a graph-based approach to feature importance.
problem Existing feature importance methods ignore or hide feature dependencies.
method Shapley Flow considers the entire causal graph and assigns credit to edges.
result Shapley Flow provides a deeper, graph-based view of feature importance.
Study systemic risk measures and capital allocation rules, showing commonalities.
problem Systemic risk measures and capital allocation in financial systems.
method Developed a general framework to embed axiomatic and injective capital approaches, introduced Aumann-Shapley CAR.
result Aumann-Shapley CAR provides a universal method for capital allocation regardless of risk measurement.
New foundation for Shapley value immune to coalitional manipulations.
problem Coalitional manipulations in game theory.
method Revised Shapley value criteria for coalitional games.
result Shapley value is immune to coalitional manipulations under new criteria.
The paper introduces Absolute Shapley Value to handle negative contributions in machine learning model training.
problem Negative marginal contributions in machine learning model training.
method Investigates three philosophies: Original Shapley Value, Zero Shapley Value, and Absolute Shapley Value.
result Absolute Shapley Value significantly outperforms other definitions in evaluating data importance.
This paper describes a practical system for Multi Touch Attribution (MTA) for use by a publisher of digital ads. We developed this system for JD.com, an eCommerce company, which is also a publisher of digital ads in China. The approach has two steps. The first step ('response modeling') fits a user-level model for purc…
New method for interpreting financial model risks.
problem Fairly allocating risk in financial models.
method Extending Shapley value framework for axiomatic risk attribution.
result Risk can be well allocated in financial models.
Paper explores alternative cooperative game theory methods for machine learning feature attribution.
problem Debate over Shapley values' relevance in feature attribution.
method Introduces Weber and Harsanyi sets as alternative allocation schemes.
result Provides a coherent framework for designing robust feature attributions.
New machine learning models improve credit scoring in banks.
problem Improving credit scoring models in heavily regulated financial institutions.
method Gradient Boosting Machines (XGBoost) and Shapley Values.
result Improved performance and default capture rate compared to current models.
XPER methodology decomposes credit scoring model performance.
problem Monitoring and understanding the key drivers of credit scoring model performance.
method XPER methodology based on Shapley values, decomposing performance metrics into feature contributions.
result A small number of features explain a large part of model performance.
The paper develops statistical inference methods for SHAP values.
problem Lack of statistical inference for SHAP values in model-agnostic feature importance.
method Semi-parametric approach using U-statistics and Neyman orthogonal scores for functionals of nested regressions.
result Asymptotically normal estimates of the pth powers of SHAP values for various p.
The paper extends game theory using Hodge theory on graphs.
problem Generalizing Shapley's value allocation formula for cooperative games on graphs.
method Connecting stochastic path integrals to Hodge-theoretic Poisson's equations on graphs.
result The value allocation operator is the solution to Poisson's equation in combinatorial Hodge theory.
Some research institutions demand researchers to distribute the incomes they earn from publishing papers to their researchers and/or co-authors. In this study, we deal with the Impact Factor-based ranking journal as a criteria for the correct distribution of these incomes. We also include the Authorship Credit factor f…
This paper studies robust payoff allocation in submodular games, especially against replication.
problem Payoff allocation in submodular games, especially robustness against replication.
method Systematically studied replication manipulation in submodular games, introduced replication robustness metric, and validated with empirical ML data market.
result Conditions characterizing robustness of semivalues in submodular games.
AutoML enhances credit decisions with XAI for better transparency.
problem Transparency in AI-driven financial decisions.
method Combining AutoML and XAI (SHAP) for credit scoring.
result Improved efficiency and accuracy in credit decisions with enhanced transparency.
Credit scores misclassify borrowers, especially minorities, leading to inequitable access.
problem Misclassification of borrowers by credit scores, particularly minorities.
method Benchmarked a widely used credit score against a machine learning model.
result Machine learning model improves predictive accuracy for low-quality data, leading to more equitable access.
We introduce Generalized Integrated Gradients (GIG), a formal extension of the Integrated Gradients (IG) (Sundararajan et al., 2017) method for attributing credit to the input variables of a predictive model. GIG improves IG by explaining a broader variety of functions that arise from practical applications of ML in do…
New research on Shapley values for feature attribution in machine learning, considering model vs. data fidelity.
problem Controversy in connecting machine learning models to coalitional games, differing approaches.
method Investigates two approaches: interventional vs. observational conditional expectation Shapley values for linear models.
result The choice between model and data fidelity depends on the specific application.
New method attributes feature uncertainty in ML models using cooperative game theory.
problem Lack of feature-level uncertainty attribution in explainable AI.
method Proposes a novel, model-agnostic uncertainty attribution method using cooperative game theory and conformal prediction.
result Demonstrates improved runtime efficiency and practical utility in real-world applications.
We formulate banks' capital optimization problem as a classic mean variance optimization, by leveraging an accurate linear approximation to the Shapely or Constrained Aumann-Shapley (CAS) allocation of max or nested max cost functions. This reduced form formulation admits an analytical solution, to the optimal leverage…
High precision analytical approximation is proposed for variance-covariance based risk allocation in a portfolio of risky assets. A general case of a single-period multi-factor Merton-type model with stochastic recovery is considered. The accuracy of the approximation as well as its speed are compared to and shown to b…
If the probability of default parameters (PDs) fed as input into a credit portfolio model are estimated as through-the-cycle (TTC) PDs stressed market conditions have little impact on the results of the capital calculations conducted with the model. At first glance, this is totally different if the PDs are estimated as…
Determining contributions by sub-portfolios or single exposures to portfolio-wide economic capital for credit risk is an important risk measurement task. Often economic capital is measured as Value-at-Risk (VaR) of the portfolio loss distribution. For many of the credit portfolio risk models used in practice, the VaR c…
The paper optimizes DIA purchase policies using lifecycle models and asset allocation.
problem Determining the optimal allocation to Deferred Income Annuities (DIAs).
method Employed a lifecycle model with utility of consumption and bequest, formalized optimization process, analyzed results, and extended model to include asset allocation.
result Optimal DIA allocation varies based on refundability, asset allocation, and perceived longevity.
A new method uses Shapley values to select important features for classification.
problem Feature selection for improving classification models.
method Classification game with Shapley value apportioning of hinge loss.
result Threshold 0 on SVEA value identifies significant features.
Analytical, free of time consuming Monte Carlo simulations, framework for credit portfolio systematic risk metrics calculations is presented. Techniques are described that allow calculation of portfolio-level systematic risk measures (standard deviation, VaR and Expected Shortfall) as well as allocation of risk down to…
Analytical, free of time consuming Monte Carlo simulations, framework for credit portfolio systematic risk metrics calculations is presented. Techniques are described that allow calculation of portfolio-level systematic risk measures (standard deviation, VaR and Expected Shortfall) as well as allocation of risk down to…
Paper proposes hybrid approach for transparent credit scoring models.
problem Lack of transparency in machine learning models limits their use in regulated environments.
method Post-hoc interpretation of black-box models guides feature selection, followed by training glass-box models.
result Reduces feature usage from 106 to 10 while maintaining comparable performance.
The negative externalities from an individual bank failure to the whole system can be huge. One of the key purposes of bank regulation is to internalize the social costs of potential bank failures via capital charges. This study proposes a method to evaluate and allocate the systemic risk to different countries/regions…
This paper enhances credit risk management using explainable AI techniques.
problem Lack of transparency and explainability in AI models for credit risk management.
method Implement LIME and SHAP for explaining ML-based credit scoring models.
result Demonstrates practical challenges and solutions for XAI methods in finance.
FedCM measures contributions in real-time for federated learning.
problem Fairly allocating contributions in federated learning systems.
method FedCM calculates impact based on current and previous rounds with attention aggregation.
result FedCM is more sensitive to data quality and quantity in real-time.
This paper improves credit line impact analysis by considering spending as a distribution.
problem Previous studies on credit lines' impact on spending have overlooked the distributional nature of spending.
method Developed a distribution-valued estimator framework to extend existing real-valued estimators.
result Credit lines positively influence spending across all quantiles, but more towards luxuries as they increase.
The paper uses SHAP for interpreting machine learning models in hospital data.
problem Interpreting machine learning models in healthcare.
method SHAP for feature importance and feature packing techniques.
result SHAP provides better interpretability of machine learning models in healthcare.
Paper improves KNN-Shapley for privacy-friendly data valuation.
problem Privacy challenges in data valuation methods.
method Introduces TKNN-Shapley, a privacy-friendly variant of KNN-Shapley.
result TKNN-Shapley offers superior privacy-utility tradeoff compared to naively privatized KNN-Shapley.
The insufficient understanding of the credit network structure was recognized as a key factor for regulators' underestimation of the destructive systematic risk during the financial crisis that started in 2007. The existing credit network research either took a macro perspective to clarify the topological properties of…
For credit risk management purposes in general, and for allocation of regulatory capital by banks in particular (Basel II), numerical assessments of the credit-worthiness of borrowers are indispensable. These assessments are expressed in terms of probabilities of default (PD) that should incorporate a certain degree of…
Paper uses ML to predict SME defaults with interpretability.
problem Lack of interpretability in ML models for SME default prediction.
method Model-agnostic approach using Accumulated Local Effects and Shapley values.
result eXtreme Gradient Boosting algorithm provides highest classification power with interpretability.
Study competition in OTC CDS market through CCP and interdealer choice models.
problem Analyze competition dynamics in OTC credit default swap market.
method Developed models for CCP choice and interdealer counterpart selection using semi-supervised learning and game theory.
result Introduced novel metrics and algorithms for understanding market dynamics.
The standard theory of coherent risk measures fails to consider individual institutions as part of a system which might itself experience instability and spread new sources of risk to the market participants. In compliance with an approach adopted by Shapley and Shubik (1969), this paper proposes a cooperative market g…
Shapley values criticized for feature selection, leading to new insights.
problem Using Shapley values for feature selection is problematic.
method Introduced and critiqued Shapley values as feature selection tools, using counterexamples and simulations.
result Shapley values may not always align with feature selection goals.
Introduces joint Shapley values to measure feature importance in models.
problem Measuring the importance of feature sets in machine learning models.
method Extends Shapley's axioms to measure a set of features' average contribution to a model's prediction.
result Joint Shapley values provide unique insights and are more consistent with local intuitions.
Unified and noise-reduced data valuation framework for machine learning.
problem Quantifying the contribution of individual data points in machine learning.
method Beta Shapley, a generalization of Data Shapley, relaxes the efficiency axiom.
result Beta Shapley outperforms state-of-the-art data valuation methods on various ML tasks.