activity
20162026
most citedModelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework

22 citations · 144 across the 21 of their papers we have counts for

collaborators

21 papers

q-fin.MF2026

Balancing Shareholder Value and Financial Stability under a Reduced-Form Liquidation Model

Benjamin Avanzi, Bernard Wong, Jinxia Zhu

Modern resolution and prudential regimes increasingly wind up a distressed firm not at a single hard threshold but through a graduated, state-dependent process. We study how the de…

q-fin.RM2026

Reinforcement Learning for Micro-Level Claims Reserving

Benjamin Avanzi, Ronald Richman, Bernard Wong +2

Outstanding claim liabilities are revised repeatedly as claims develop, yet most modern reserving models are trained as one-shot predictors and typically learn only from settled cl…

q-fin.ST2025

On the use of case estimate and transactional payment data in neural networks for individual loss reserving

Benjamin Avanzi, Matthew Lambrianidis, Greg Taylor +1

The use of neural networks trained on individual claims data has become increasingly popular in the actuarial reserving literature. We consider how to best input historical payment…

stat.AP2025

Dynamic Financial Analysis (DFA) of General Insurers under Climate Change

Benjamin Avanzi, Yanfeng Li, Greg Taylor +1

Climate change is expected to significantly affect the physical, financial, and economic environments over the long term, posing risks to the financial health of general insurers.…

stat.ML2024

Distributional Refinement Network: Distributional Forecasting via Deep Learning

Benjamin Avanzi, Eric Dong, Patrick J. Laub +1

A key task in actuarial modelling involves modelling the distributional properties of losses. Classic (distributional) regression approaches like Generalized Linear Models (GLMs; N…

stat.ML2023★ 19 cited

Machine Learning with High-Cardinality Categorical Features in Actuarial Applications

Benjamin Avanzi, Greg Taylor, Melantha Wang +1

High-cardinality categorical features are pervasive in actuarial data (e.g. occupation in commercial property insurance). Standard categorical encoding methods like one-hot encodin…

stat.ME2022

Ensemble distributional forecasting for insurance loss reserving

Benjamin Avanzi, Yanfeng Li, Bernard Wong +1

Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing d…

math.OC2022

On the surplus management of funds with assets and liabilities in presence of solvency requirements

Benjamin Avanzi, Ping Chen, Lars Frederik Brandt Henriksen +1

In this paper we consider a company whose assets and liabilities evolve according to a correlated bivariate geometric Brownian motion, such as in Gerber and Shiu (2003). We determi…

stat.ME2022★ 4 cited

Detection and treatment of outliers for multivariate robust loss reserving

Benjamin Avanzi, Mark Lavender, Greg Taylor +1

Traditional techniques for calculating outstanding claim liabilities such as the chain ladder are notoriously at risk of being distorted by outliers in past claims data. Unfortunat…

stat.ME2022

On the impact of outliers in loss reserving

Benjamin Avanzi, Mark Lavender, Greg Taylor +1

The sensitivity of loss reserving techniques to outliers in the data or deviations from model assumptions is a well known challenge. It has been shown that the popular chain-ladder…

stat.ME2021★ 14 cited

Stochastic loss reserving with mixture density neural networks

Muhammed Taher Al-Mudafer, Benjamin Avanzi, Greg Taylor +1

Neural networks offer a versatile, flexible and accurate approach to loss reserving. However, such applications have focused primarily on the (important) problem of fitting accurat…

q-fin.RM2020★ 14 cited

SynthETIC: an individual insurance claim simulator with feature control

Benjamin Avanzi, Gregory Clive Taylor, Melantha Wang +1

Recent years have seen rapid increase in the application of machine learning to insurance loss reserving. They yield most value when applied to large data sets, such as individual…

q-fin.RM2020★ 13 cited

On the optimality of joint periodic and extraordinary dividend strategies

Benjamin Avanzi, Hayden Lau, Bernard Wong

In this paper, we model the cash surplus (or equity) of a risky business with a Brownian motion. Owners can take cash out of the surplus in the form of "dividends", subject to tran…

q-fin.RM2020★ 3 cited

On unbalanced data and common shock models in stochastic loss reserving

Benjamin Avanzi, Gregory Clive Taylor, Phuong Anh Vu +1

Introducing common shocks is a popular dependence modelling approach, with some recent applications in loss reserving. The main advantage of this approach is the ability to capture…

q-fin.RM2020★ 9 cited

A multivariate evolutionary generalised linear model framework with adaptive estimation for claims reserving

Benjamin Avanzi, Gregory Clive Taylor, Phuong Anh Vu +1

In this paper, we develop a multivariate evolutionary generalised linear model (GLM) framework for claims reserving, which allows for dynamic features of claims activity in conjunc…

q-fin.RM2020★ 5 cited

On the modelling of multivariate counts with Cox processes and dependent shot noise intensities

Benjamin Avanzi, Gregory Clive Taylor, Bernard Wong +1

In this paper, we develop a method to model and estimate several, _dependent_ count processes, using granular data. Specifically, we develop a multivariate Cox process with shot no…

math.OC2020★ 12 cited

Optimal periodic dividend strategies for spectrally negative Lévy processes with fixed transaction costs

Benjamin Avanzi, Hayden Lau, Bernard Wong

Maximising dividends is one classical stability criterion in actuarial risk theory. Motivated by the fact that dividends are paid periodically in real life, div…

math.PR2020★ 8 cited

A counterexample to the central limit theorem for pairwise independent random variables having a common arbitrary margin

Benjamin Avanzi, Guillaume Boglioni Beaulieu, Pierre Lafaye de Micheaux +2

The Central Limit Theorem (CLT) is one of the most fundamental results in statistics. It states that the standardized sample mean of a sequence of mutually independent and iden…

q-fin.RM2020★ 22 cited

Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework

Benjamin Avanzi, Greg Taylor, Bernard Wong +1

The Markov-modulated Poisson process is utilised for count modelling in a variety of areas such as queueing, reliability, network and insurance claims analysis. In this paper, we e…

math.OC2020★ 6 cited

Optimal periodic dividend strategies for spectrally positive Lévy risk processes with fixed transaction costs

Benjamin Avanzi, Hayden Lau, Bernard Wong

We consider the general class of spectrally positive Lévy risk processes, which are appropriate for businesses with continuous expenses and lump sum gains whose timing and sizes ar…

math.OC2016★ 15 cited

On optimal joint reflective and refractive dividend strategies in spectrally positive Lévy models

Benjamin Avanzi, José-Luis Pérez, Bernard Wong +1

The expected present value of dividends is one of the classical stability criteria in actuarial risk theory. In this context, numerous papers considered threshold (refractive) and…