Reference health
Machine learning improves accounting estimates: evidence from insurance payments
25/25 checkable references clean · checked 2026-07-25
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
15 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 25 checked references that resolve
resolves10.1111/1475-679X.12292
Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach
resolves10.1023/A:1009676300318
The Characteristics and Valuation of Loss Reserves of Property Casualty Insurers
resolves10.1016/S0165-4101(03)00037-5
Management of the loss reserve accrual and the distribution of earnings in the property-casualty insurance industry
resolves10.1016/j.jacceco.2003.10.010
Do insurers manipulate loss reserves to mask solvency problems?
resolves10.2307/252923
Property-Liability Insurer Reserve Errors: A Theoretical and Empirical Analysis
resolves10.1111/j.1539-6975.2011.01434.x
<scp>Property–Liability Insurer Reserve Error: Motive, Manipulation, or Mistake</scp>
resolves10.2308/accr.2000.75.1.115
Rate Regulation, Competition, and Loss Reserve Discounting by Property-Casualty Insurers
resolves10.2308/ajpt-50009
Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms
resolves10.2308/accr-51562
Finding Needles in a Haystack: Using Data Analytics to Improve Fraud Prediction
resolves10.1016/0165-4101(92)90003-K
Optimistic reporting in the property- casualty insurance industry
resolves10.2307/252512
A Multivariate Analysis of Loss Reserving Estimates in Property-Liability Insurers
The 15 references without a DOI — listed, not checked
no DOI — not checkedA. M. Best Company. (1994). Best’s aggregates and averages: Property-casualty edition. Oldwick: A. M. Best Company.
no DOI — not checkedAnderson, D. R. (1973). Effects of loss reserve evaluation upon policyholders’ surplus. Madison, Wisconsin: Bureau of Business Research and Service, University of Wisconsin, monograph, 6.
no DOI — not checkedBierens, H. J., & Bradford, D. F. (2005). Are property-casualty insurance reserves biased? A Non-Standard Random Effects Panel Data Analysis 1.
no DOI — not checkedBishop, C. M. (2006). Pattern recognition and machine learning: Springer.
no DOI — not checkedBreiman, L. (2002). Using models to infer mechanisms. IMS Wald Lecture, 2, 59–71.
no DOI — not checkedBrowne, M. J., Ma, Y.-L., & Wang, P. (2009). Stock-based executive compensation and reserve errors in the property and casualty insurance industry. Journal of Insurance Regulation, 27(4).
no DOI — not checkedBrownlee, J. (2016). Linear regression for machine learning. Machine learning mastery. https://machinelearningmastery.com/linear-regression-for-machine-learning/, accessed the last time on 22 June 2019.
no DOI — not checkedBrownlee, J. (2018). A gentle introduction to k-fold cross-validation, may 2018. Available in https://machinelearningmastery.com/k-fold-cross-validation/, accessed the last time on 22 June 2019.
no DOI — not checkedHoyt, R. E., & McCullough, K. A. (2010). Managerial discretion and the impact of risk-based capital requirements on property-liability insurer reserving practices. Journal of Insurance Regulation, 29(2).
no DOI — not checkedLambert, R. A. (1984). Income smoothing as rational equilibrium behavior. Accounting Review, 604–618.
no DOI — not checkedMason, L., Baxter, J., Bartlett, P. L., & Frean, M. R. (2000) Boosting algorithms as gradient descent. In Advances in neural information processing systems, (pp. 512–518).
no DOI — not checkedRamon, J. 2013. Responses to question “how to determine the number of trees to be generated in random Forest algorithm?”. ResearchGate. https://www.researchgate.net/post/How_to_determine_the_number_of_trees_to_be_generated_in_Random_Forest_algorithm
no DOI — not checkedSchapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197–227.
no DOI — not checkedSun, T., & Vasarhelyi, M. A. (2017). Deep learning and the future of auditing: How an evolving technology could transform analysis and improve judgment. CPA Journal, 87(6).
no DOI — not checkedZhang, C., & Browne, M. J. (2013) Loss reserve errors, income smoothing and firm risk of property and casualty insurance companies. In Annual Meeting of the American Risk and Insurance Association, Working Pa, (pp. 1–55).
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