Paper uses RL for better credit scoring and underwriting.
problem Traditional underwriting methods are ungeneralizable in complex scenarios.
method Adapts RL principles for credit scoring, incorporating action space renewal and multi-choice actions.
result RL-based algorithms outperform traditional methods in aligned data scenarios.
Method debiases alternative data for fair credit underwriting.
problem Bias in alternative data affecting credit underwriting fairness.
method Causal inference applied to machine learning models.
result Improves model accuracy across racial groups without discrimination.
The paper models insurance market dynamics under uncertainty and financial frictions.
problem Modeling insurer behavior under uncertainty and financial frictions.
method Dynamic equilibrium model of insurance market with competitive insurers maximizing shareholder value.
result Investment can lead to lower insurance prices and negative loadings under certain conditions.
A framework converts spatial data into embeddings for insurance risk modelling.
problem Improving underwriting precision and risk management in insurance with spatial data.
method Multi-view contrastive learning framework for generating spatial embeddings.
result Spatial embeddings consistently improve predictive accuracy across various models.
The paper examines how insurers manage risks and liquidity in a dynamic market.
problem Model uncertainty in insurance pricing and competitive equilibrium.
method Analyzes insurers' robustness preferences and optimization strategies for underwriting and liquidity management.
result Robust insurance pricing leads to higher premiums and equity valuations compared to a benchmark.
We study a continuous-time asset-allocation problem for an insurance firm that backs up liabilities from multiple non-life business lines with underwriting profits and investment income. The insurance risks are captured via a multidimensional jump-diffusion process with a multivariate compound Poisson process with depe…
A new runtime for AI agents calculates risks in real-time.
problem Managing risks and liabilities in autonomous AI actions.
method A time-consistent counterfactual actuarial layer with explicit underwriting boundaries.
result Establishes a well-defined toll and guarantees executed-action budgets.
Study insurance pricing under correlation ambiguity without increasing prices or reducing utility.
problem Understanding the dependence structure between insurance and financial risks.
method Dynamic equilibrium analysis of insurance pricing with worst-case beliefs.
result Correlation ambiguity does not necessarily increase insurance prices or reduce insurers' utility.
The paper examines how decentralized credit curators have taken over risk management from traditional protocols.
problem Risk management in decentralized credit has shifted from centralized protocols to decentralized curators.
method Analysis of ERC 4626 vaults and third-party curators, focusing on capital utilization, concentration, and fee margins.
result Curators have a significant impact on the risk profile of decentralized credit systems, with a small set of curators handling a disproportionate share of system TVL.
The paper examines insurance market dynamics and optimal regulation.
problem Equilibrium outcomes in dynamic insurance markets.
method Analyzes three equilibrium outcomes: positive, zero, and market failure.
result Insurers may accept underwriting losses by investing profits, especially with negative correlations.
Study shows fiduciary duty reduces municipal bond yields by 9% after SEC rule.
problem Effect of fiduciary duty on municipal bond yields and fees.
method Difference-in-differences analysis using hand-collected data.
result Bond yields decrease by 9% after SEC rule, but smaller issuers see increased borrowing costs.
Framework insures AI actions with reserve capital, preventing loss.
problem Ensuring safety and accountability for AI actions with varying side effects.
method Developed Actuarial Action Interface (AAI) and Authority Frontier to price and gate AI actions.
result Found common refusal and release patterns across domains, with varying required reserve capital.
Paper proposes a new reserving model using machine learning techniques.
problem Managing uncertainties in premium sufficiency and reserves for future claims.
method Stacked model combining Gradient Boosting, Random Forest, Artificial Neural Networks, and log-normal approach.
result The proposed model improves traditional reserving techniques, leading to more accurate reserving risk assessment.
ClauseLens uses reinforcement learning to price reinsurance treaties transparently and auditably.
problem Opaque and difficult-to-audit reinsurance treaty pricing practices.
method ClauseLens models treaty pricing as a Risk-Aware Constrained Markov Decision Process (RA-CMDP), incorporating legal clauses and generating interpretable explanations.
result ClauseLens reduces solvency violations and improves tail-risk performance, achieving 88.2% accuracy in clause-grounded explanations.
AI improves MSME credit scoring using bank statement data.
problem Lack of access to financing for MSMEs due to traditional credit scoring methods.
method Developed a cash flow-based pipeline using bank statement data for machine learning credit scoring.
result Bank statement features significantly improve credit scoring models, achieving AUROC of 0.806.
Study classifies liability insurance policies using machine learning.
problem Classifying liability insurance policies with or without claims.
method Used machine learning models like nearest neighbour and logistic regression on Actuarial Challenge dataset.
result Models accurately classified policies into claims and non-claims groups.
In the framework of Embedded Value new standards, namely the MCEV norms, the latest principles published in June 2008 address the issue of market and underwriting risks measurement by using stochastic models of projection and valorization. Knowing that stochastic models particularly data-consuming, the question which c…
The aim of this paper is to introduce a method for computing the allocated Solvency II Capital Requirement (SCR) of each Risk which the company is exposed to, taking in account for the diversification effect among different risks. The method suggested is based on the Euler principle. We show that it has very suitable p…
DeFi TrustBoost uses blockchain and AI to assess small business loans.
problem Assessing small business loans from low-wealth households.
method Combines blockchain and Explainable AI to ensure confidentiality, compliance, and security.
result Tamper-proof auditing and on-chain/off-chain data storage for financial organizations.
In this theoretical paper, I propose creation of a venture bank, able to multiply the capital of a venture capital firm by at least 47 times, without requiring access to the Federal Reserve or other central bank apart from settlement. This concept rests on obtaining default swap instruments on loans in order to create …
Automated scoring prioritizes risky driving behavior in telematic auto insurance policies.
problem Identifying risky driving behavior in telematic auto insurance policies using machine learning.
method Bayesian approach using MCMC to model propensity of policyholders to undertake trips resulting in positive classification.
result The approach improves efficiency of human resource allocation in identifying risky driving behavior.
Paper models cloud outages for cyber insurance stress-testing.
problem Cyber insurance portfolios' vulnerability to simultaneous cloud outages.
method Modeling and calibrating cloud-outage scenarios, measuring diversification.
result Cloud-outage diversification can protect against accumulation risk.
Lapse-supported life insurance exacerbates adverse selection risks.
problem Lapse-supported life insurance increases adverse selection costs.
method Modeling 'Term to 100' contracts and analyzing three methods of managing lapse surplus.
result Adverse selection losses can be almost unlimited under certain conditions.
CATNet predicts CAT bond spreads using graph-based deep learning.
problem Complex, relational data in CAT bonds not well captured by traditional models.
method CATNet applies R-GCN to CAT bond primary market as a graph.
result CATNet outperforms Random Forest and XGBoost benchmarks.
AI helps simplify complex ship finance processes.
problem Complexity in ship finance due to data and regulatory requirements.
method Integrates large language models for document comprehension, information extraction, and workflow automation.
result AI-assisted systems can support maritime finance professionals in managing complex information and reporting requirements.
The paper compares ML models for credit scoring and investment decisions using explainable AI.
problem The opacity of machine learning models in financial services.
method Comparison of various machine learning models (single classifiers, ensembles, neural networks) and explainability techniques (LIME, SHAP).
result Ensemble classifiers and neural networks outperform in credit scoring models.
GAICF proposes a framework for governing generative AI in banking.
problem Generative AI's impact on financial decision-making and governance.
method SR 26-2-compatible governance framework for generative AI applications.
result GAICF aligns generative AI practices with SR 26-2 supervisory expectations.
GAICF proposes a framework for managing generative AI risks in banking.
problem Generative AI's impact on financial decision-making and governance.
method SR 26-2-compatible governance framework for generative AI.
result GAICF aligns generative AI practices with SR 26-2 supervisory expectations.
Study improves flood loss risk models using historical data and rainfall data.
problem Predicting financial losses from flooding events.
method Used neural networks, decision trees, and kernel-based regressors on NFIP dataset, incorporating rainfall data.
result Extreme Gradient Boosting provided the best results, and bias correction improved model performance.
When an insurance note is also a derivative a serious problem arises because a derivative must be fulfilled immediately. This feature of derivatives prevents claims processing procedures that screen out ineligible claims. This, in turn, creates a perverse incentive for insured holders of notes to commit acts that resul…
Research simulates Lloyd's of London's specialty insurance market dynamics.
problem Quantitative study of complex market phenomena in Lloyd's of London.
method Discrete Event Simulation (DES) framework for Lloyd's of London specialty insurance market.
result Model shows sophisticated exposure management reduces syndicate insolvency, and syndication enhances actuarial price accuracy.
Low precision weights, activations, and gradients have been proposed as a way to improve the computational efficiency and memory footprint of deep neural networks. Recently, low precision networks have even shown to be more robust to adversarial attacks. However, typical implementations of low precision DNNs use unifor…
Mixed-precision CA-SGD for generalized linear models on GPUs
problem SGD communication bottleneck
method Mixed-precision CA-SGD
result Matches FP32 SGD loss within 0.5% on various problems
Paper explores low-precision SGLD for neural networks, reducing costs without sacrificing performance.
problem Infeasibility of low-precision sampling in large-scale scenarios.
method Developed low-precision SGLD with quantization function and full-precision gradient accumulators.
result Low-precision SGLD achieves comparable performance to full-precision SGLD with only 8 bits.
Machine learning models outperform traditional actuarial methods in predicting health insurance costs.
problem Improving accuracy in health insurance pricing to identify concession opportunities.
method Developed and evaluated two machine learning models at the patient and employer-group levels.
result Machine learning models outperformed traditional actuarial models by 20% in predicting costs.
Enhances cyber risk assessment with entity-specific features.
problem Lack of high-quality public cyber incident data.
method Develops an InsurTech framework to enrich cyber incident data with entity-specific attributes and implements machine learning models.
result InsurTech features improve prediction robustness and provide customized risk profiles.
Low precision operations can provide scalability, memory savings, portability, and energy efficiency. This paper proposes SWALP, an approach to low precision training that averages low-precision SGD iterates with a modified learning rate schedule. SWALP is easy to implement and can match the performance of full-precisi…
This paper improves low-precision sampling using SGHMC for deep learning models.
problem Enhancing training efficiency of deep neural networks with low-precision training.
method Investigates low-precision sampling via Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) for both log-concave and non-log-concave distributions.
result Low-precision SGHMC achieves quadratic improvement in error compared to SGLD for non-log-concave distributions.
Low-precision computation is often used to lower the time and energy cost of machine learning, and recently hardware accelerators have been developed to support it. Still, it has been used primarily for inference - not training. Previous low-precision training algorithms suffered from a fundamental tradeoff: as the num…
Efficient Bitwidth Search optimizes neural network quantization for better performance.
problem Finding optimal bitwidth for weights and activations of each layer efficiently.
method EBS algorithm reusing meta weights and binary decomposition for efficient mixed precision convolution.
result Mixed precision QNN outperforms uniform bitwidth and other techniques on CIFAR10 and ImageNet.
Deep neural networks have enabled progress in a wide variety of applications. Growing the size of the neural network typically results in improved accuracy. As model sizes grow, the memory and compute requirements for training these models also increases. We introduce a technique to train deep neural networks using hal…
The study examines how class imbalance affects precision-recall curves.
problem Understanding how precision changes with class imbalance ratios.
method Analyzes the relationship between precision, class imbalance ratio, and true/false positive rates.
result Predicts changes in precision-recall curves and other measures with class imbalance ratios.
It is well-known that the precision of data, hyperparameters, and internal representations employed in learning systems directly impacts its energy, throughput, and latency. The precision requirements for the training algorithm are also important for systems that learn on-the-fly. Prior work has shown that the data and…
This paper introduces a new method to train normalizing flows using precision-recall divergences.
problem Training generative models with mode dropping and low-quality samples.
method Introduces PR-divergences and proposes a novel generative model to minimize precision-recall trade-offs.
result Normalizing flows can be trained to achieve specific precision-recall trade-offs using PR-divergences.
A framework estimates multiple precision matrices with shared structures.
problem Estimating multiple precision matrices with shared structures.
method Penalized likelihood framework with iterative algorithm alternating between convex and clustering problems.
result The method outperforms competitors and performs similarly to methods using prior information.
Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence. The absence of any framework to an…
Paper explores reducing precision in SVM for faster text classification.
problem Efficiency in multi-class text classification training.
method Comparison of SVM trained with reduced precision (16-bit, half) vs original.
result Reduced precision training maintains text classification accuracy.
We consider the post-training quantization problem, which discretizes the weights of pre-trained deep neural networks without re-training the model. We propose multipoint quantization, a quantization method that approximates a full-precision weight vector using a linear combination of multiple vectors of low-bit number…