Technical paper/Artificial intelligence
The digital sentinel: artificial intelligence and the mitigation of corporate litigation risk
The authors investigates relationships between strategic AI adoption and corporate litigation risk, finding that increased strategic adoption leads to a net reduction in this form of risk.
The future of risk and insurability in the era of systemic disruption, unpredictability and artificial intelligence
The authors demonstrate the fragile nature of traditional risk management techniques in the face of frequent high-impact shocks and advocate for a new approach that treats disruption as systemic rather than episodic.
The robot-labeling phenomenon: robot-ready modern operational risk management
The author highlights misuse of the term "robot" in banking practice and the literature, proposes the robot-labelling phenomenon and recommends a shift in approaches to operational risk management to address challenges of the synthetic era.
Artificial intelligence in crisis management: a bibliometric analysis
The authors carry out a bibliometric analysis of academic papers in the field of artificial intelligence applications in crisis management and propose potential new directions for researchers in this field.
Quantum cognition machine learning: financial forecasting
A new paradigm for training machine learning algorithms based on quantum cognition is presented
Quantifying credit portfolio sensitivity to asset correlations with interpretable generative neural networks
This study introduces a method for assessing the impact of asset correlations on credit portfolio value-at-risk using variational autoencoders (VAEs), offering a more interpretable approach than previous methods and improving model interpretability.
Dynamic class-imbalanced financial distress prediction based on case-based reasoning integrated with time weighting and resampling
The authors put forward a dynamic class-imbalanced CBR FDP model which is shown, using data from Chinese listed companies, to outperform static and dynamic CBR FDP models without resampling or time weighting.
Asset allocation with inverse reinforcement learning
Using reinforcement learning to help replicate asset managers' allocation strategy
Explainable artificial intelligence for credit scoring in banking
The authors put forward an explainable machine learning model predicting credit default using a real-world data set provided by a Norwegian bank.
Technology risk management in fintech: underlying mechanisms and challenges
This study focuses on the foundational technology of fintech to address the challenges posed by its specific form of risk.
A survey of machine learning in credit risk
This paper surveys the impressively broad range of machine learning methods and application areas for credit risk.
Axes that matter: PCA with a difference
Differential PCA is introduced to reduce the dimensionality in derivative pricing problems
Goal-based wealth management with reinforcement learning
A combination of machine learning techniques provides multi-period portfolio optimisation
From use cases to a big data benchmarking framework in clearing houses and exchanges
In this paper, we propose a conceptual framework that links the technical and business benchmarks in the domain of clearing houses and securities exchanges.
A hybrid model for credit risk assessment: empirical validation by real-world credit data
This paper examines which hybridization strategy is more suitable for credit risk assessment in the dynamic financial world.
Toward reducing the operational risk of emerging technologies adoption in central counterparties through end-to-end testing
This paper discusses the software-testing challenges of traditional central counterparties as well as the risks, biases and problems related to new technologies. It also outlines a set of requirements for an end-to-end validation and verification…
An alternative statistical framework for credit default prediction
This study compares the gradient-boosting model with four other well-known classifiers, namely, a classification and regression tree (CART), logistic regression (LR), multivariate adaptive regression splines (MARS) and a random forest (RF).
Deep learning calibration of option pricing models: some pitfalls and solutions
Addressing model calibration and the issue of no-arbitrage in a deep learning approach
Yield curve fitting with artificial intelligence: a comparison of standard fitting methods with artificial intelligence algorithms
In this paper, the author expands standard yield curve fitting techniques to artificial intelligence methods.
Global perspectives on operational risk management and practice: a survey by the Institute of Operational Risk (IOR) and the Center for Financial Professionals (CeFPro)
This paper presents survey results which represent comprehensive perspectives on operational risk practice, obtained from practitioners in a wide range of countries and sectors.
Machine learning for trading
Gordon Ritter applies reinforcement learning to dynamic trading strategies with market impact