Frequency Of Claims Research Articles

Weekly dynamic motor insurance ratemaking with a telematics signals bonus-malus score

Abstract We present a dynamic pay-how-you-drive pricing scheme for motor insurance using telematics signals. More specifically, our approach allows the insurer to apply penalties to a baseline premium on the occurrence of events such as hard acceleration or braking. In addition, we incorporate a bonus-malus system (BMS) adapted for telematics data, providing a credibility component based on past telematics signals to the claim frequency predictions. We purposefully consider a weekly setting for our ratemaking approach to benefit from the signal’s high-frequency rate and to encourage safe driving via dynamic premium corrections. Moreover, we provide a detailed structure that allows our model to benefit from historical records and detailed telematics data collected weekly through an onboard device. We showcase our results numerically in a case study using data from an insurance company.

Modeling of Car Insurance Premiums Using the Bayes Method with Poisson and Exponential Distributions

Car insurance is included in the general insurance category. Determination of general insurance premiums can be done through various approaches, one of which is the Bayes method used in this study. The Poisson distribution is chosen to model the frequency of claims, while the Exponential distribution is used to model the amount of claims. Insurance premiums are calculated by multiplying the expected frequency of claims by the expected amount of claims. Based on the results of the analysis of car insurance data using the Bayes method, it was found that the highest premium rates were for Mercedes brand vehicles, while the lowest rates were for Saab brand vehicles. The results of this calculation can be used by insurance companies as a reference in managing car insurance reserve funds.

Features to Involve and Collect in Future Auto Insurance Pricing

The property insurance industry is a highly data-driven sector. In the traditional model, insurance pricing largely depends on the self-reported risk characteristics of clients, such as age, gender, vehicle type, etc. These characteristics help insurance companies categorize clients into different risk levels and set premiums accordingly. With the development of telematics, insurance companies can now collect and analyze more dynamic data that is more directly related to risk. This study presents a comprehensive analysis of three feature selection models for vehicle insurance pricing. Utilizing regression techniques, it evaluated a multitude of factors believed to influence claim frequency and severity. Through a rigorous comparative study, key factors that significantly impact the vehicle are assessed and fit to different regression models. The articles findings indicate that factors such as policy bonus, mileage base data, and car usage are paramount in determining insurance rates. Moreover, the study revealed that incorporating these factors into the pricing model enhances its accuracy and fairness.

A Systematic Literature Review of Insurance Claims Risk Measurement Using the Hidden Markov Model

In the rapidly evolving field of insurance, accurate risk measurement is crucial for effective claims management and financial stability. Therefore, this research presented a systematic literature review (SLR) on insurance claims risk measurement using the Hidden Markov Model (HMM). Bibliometric analysis was conducted using VOSviewer 1.6.20 and ResearchRabbit software to map research trends and collaboration networks in this topic. This review explored the implementation of the HMM in predicting the frequency and severity of insurance claims, with a focus on the statistical distribution methods used. In addition, the research emphasized the influence of the number of hidden states in the HMM on claims behavior, both in terms of frequency and magnitude, and provided interpretations of these hidden dynamics. Data sources for this review comprised three databases, namely, Scopus, ScienceDirect, and Dimensions, and additional papers from a website. The article selection process followed updated PRISMA 2020 guidelines, resulting in twelve key papers relevant to the topic. The results offered insights into the application of the HMM for forecasting the frequency and severity of insurance claims and opened avenues for further investigation on distribution models and hidden state modeling.

Implementing Loss Prevention by Identifying Trends and Insights to Help Policyholders Mitigate Risks and Reduce Claims

Loss prevention is a critical focus for both insurers and policyholders, as rising claim volumes lead to increased premiums and financial losses. This paper explores the implementation of loss prevention strategies through the identification of trends and insights derived from historical data, predictive analytics, and emerging technologies. By leveraging these insights, insurers can recommend proactive risk mitigation measures to policyholders, reducing the occurrence of claims and overall risks. The study begins by analyzing historical data trends across various sectors such as auto, property, and health insurance, identifying key factors that drive claims. Predictive analytics is then applied to forecast future risks, allowing insurers to develop personalized strategies for mitigating those risks. Emerging technologies like Internet of Things (IoT) devices and artificial intelligence (AI) are highlighted for their role in providing real-time data and early warning systems, which help to prevent incidents before they escalate into claims. This paper also outlines practical loss prevention strategies for policyholders, such as installing smart home devices to monitor potential hazards, encouraging safe driving habits through telematics, and promoting health and wellness programs to lower medical claims. These risk mitigation strategies are shown to provide significant return on investment (ROI) for policyholders by reducing claims frequency and severity. Through case studies and data analysis, the paper demonstrates that proactive loss prevention not only benefits policyholders by lowering their risk exposure but also helps insurers reduce claims costs and enhance profitability. In conclusion, this research highlights the importance of trend identification, predictive analytics, and emerging technologies in the future of loss prevention, offering a path forward for insurers and policyholders to work collaboratively in mitigating risks.

Motor Insurance Claim Frequency Prediction Using XGBoost

Insurance claim frequency modelling is an important task for non-life insurers, this together with other variables forms an important part of product pricing and risk management. The traditional frequency models such as Poisson, Negative Binomial and Zero Inflated models have several weakness such as scalibility issues, overdispersion and independence assumptions for large datasets and therefore not ideal to use when dealing with complex and unstructured data. Extreme gradient Boosting Algorithm (XGBoost) is an ensemble learning which has the capacity to effectively handle big complex and unstructured insurance data. XGBoost creates tree-based models by iteratively fitting decision trees to the residuals of the previous predictions, effectively reducing the error in each iteration. This research utilized and explored the XGBoost algorithm to process motor insurance claims large dataset in-order to predict the frequencies of insurance claims, that is 0,1,2,and 3. Using this algorithm we aim to enhance the accuracy of predictions that will yield better estimates for improved risk assessment and pricing of insurance products. Cross validation was performed to assess the true performance of our model. Cross validation results showed that XGBoost models for the claim frequency had a RMSE estimate of 0.949, MAE of 0.7741 and RSQ 0.781. This demonstrated a strong predictive performance, with an RMSE of 0.949 and an MAE of 0.7741, indicating a low average error in the predictions.The RSQ of 0.781 suggests further that the model explained a significant proportion of variability of the insurance data. Our model was evaluated with a confusion matrix. The results of the confusion matrix showed that for the frequency 0 99.59% of cases were correctly predicted, frequency 1 94.01% were correctly predicted and frequency 2 84.80% and finally frequency 3 only 40.96% of the observations were correctly predicted.These results highlights the potential of XGBoost as a robust modeling technique for handling big data and accurately predicting insurance claim frequency. The results corroborate with other studies that XGBoost is an invaluable tool for insurance companies.

Modelling of Claim and Pricing of Motor Insurance Based on Bonus-Malus System Considering the Frequency and Severity of Claims

This article proposes new distributions for claim frequency and severity, specifically tailored for a bonus-malus system in automobile insurance. The mixed Poisson with weighted quasi Lindley distribution is recommended for modeling claim frequency, while the mixed exponential with weighted quasi Lindley distribution is suggested for modeling claim severity. To estimate insurance premiums, the Bayesian method is employed, incorporating both frequency and severity distributions. The study validates the proposed models using real data from an Australian insurance company, which includes 67,856 policies. The assessment of model adequacy indicates that the Poisson-weighted quasi Lindley distribution is a suitable fit for modeling claim frequency, while the exponential-weighted quasi Lindley distribution is appropriate for modeling claim severity. Overall, the results suggest that the proposed models offer optimal premium estimations, considering both claim frequency and severity, which can lead to fairer pricing and increased customer appeal during claim occurrences compared to conventional models.

Calculation of Motor Vehicle Insurance Premiums Through Evaluation of Claim Frequency and Amount Data

Insurance, as a risk control strategy by transferring the burden of risk from one party to another, consists of two main forms: life insurance, which covers financial losses from the risk of death of the policyholder, and general insurance, which involves the transfer of risk against property losses. Motor vehicle insurance has become a common product reflecting the high value and benefits of motor vehicles, which has resulted in an increase in vehicle ownership. Although the increase in the number of vehicles contributes to the increase in road accidents, many owners who suffer losses do not receive the compensation they deserve. In this context, the premium becomes a key factor, where the policyholder pays a certain amount of money to get protection. This research aims to apply risk premium calculation based on claim frequency and claim size data, as conducted by Ozgurel in 2005, especially for each vehicle category and region in XYZ insurance company. The main problem is to optimize the premium calculation to reflect the actual risk, providing a more accurate understanding of the influence of vehicle and regional characteristics in determining a fair and appropriate premium.

Collective risk models with FGM dependence

ABSTRACT We study copula-based collective risk models when the dependence structure is defined by a Farlie-Gumbel-Morgenstern (FGM) copula. By leveraging a one-to-one correspondence between the class of FGM copulas and multivariate symmetric Bernoulli distributions, we find convenient representations for the moments and Laplace-Stieltjes transform for the aggregate random variable defined from collective risk models with FGM dependence. We examine different components of this collective risk model, aiming to better understand the impact of the assumed dependence between a claim's frequency and severity. Relying on stochastic ordering, we analyze the impact of dependence on the aggregate claim amount random variable. Even if the FGM copula may only induce moderate dependence, we illustrate through numerical examples that the cumulative effect of FGM dependence can lead to substantial variations in key risk measures on aggregate random variables defined from collective risk models.

Malpractice rates are lower for women physicians compared to men physicians

Background Previous literature has shown that women physicians are less likely to be involved in medical malpractice litigation than male physicians. Methods This study sought to examine the differences in malpractice experience by provider gender by using closed medical malpractice claims from 2011 through 2020. Results We found that after adjusting for the number of claims and dollar losses per full time equivalents worked, female physicians experienced fewer and less costly claims than male physicians ( p < .001). Specific specialties had a significant relationship between claim frequency and provider gender, which included general surgery ( p = .01), obstetrics ( p = .001), gynecology ( p = .04), internal medicine and family general practice ( p < .001). Conclusion The results from this analysis support the prior findings that male physicians experience a higher rate of malpractice claims. Therefore, there is an opportunity to learn how women providers achieve lower malpractice risk to inform clinical practice recommendations.

Are electric vehicles riskier? A comparative study of driving behaviour and insurance claims for internal combustion engine, hybrid and electric vehicles

Electric vehicles (EVs) differ significantly from their internal combustion engine (ICE) counterparts, with reduced mechanical parts, Lithium-ion batteries and differences in pedal and transmission control. These differences in vehicle operation, coupled with the proliferation of EVs on our roads, warrant an in-depth investigation into the divergent risk profiles and driving behaviour of EVs, Hybrids (HYB) and ICEs. In this unique study, we analyze a novel telematics dataset of 14,642 vehicles in the Netherlands accompanied by accident claims data. We train a Logistic Regression model to predict the occurrence of driver at-fault claims, where an at-fault claim refers to First and Third Party damages where the driver was at fault. Our results reveal that EV drivers are more exposed to incurring at-fault claims than ICE drivers despite their lower average mileage. Additionally, we investigate the financial implications of these increased at-fault claims likelihoods and have found that EVs experience a 6.7% increase in significant first-party damage costs compared to ICE. When analyzing driver behaviour, we found that EVs and HYBs record fewer harsh acceleration, braking, cornering and speeding events than ICE. However, these reduced harsh events do not translate to reducing claims frequency for EVs. This research finds evidence of a higher frequency of accidents caused by Electric Vehicles. This burden should be considered explicitly by regulators, manufacturers, businesses and the general public when evaluating the cost of transitioning to alternative fuel vehicles.

Anesthesia-Related Closed Claims in Free-Standing Ambulatory Surgery Centers.

As higher acuity procedures continue to move from hospital-based operating rooms (HORs) to free-standing ambulatory surgery centers (ASCs), concerns for patient safety remain high. We conducted a contemporary, descriptive analysis of anesthesia-related liability closed claims to understand risks to patient safety in the free-standing ASC setting, compared to HORs. Free-standing ASC and HOR closed claims between 2015 and 2022 from The Doctors Company that involved an anesthesia provider responsible for the claim were included. We compared the coded data of 212 free-standing ASC claims with 268 HOR claims in terms of severity of injury, major injuries, allegations, comorbidities, contributing factors, and financial value of the claim. Free-standing ASC claims accounted for almost half of all anesthesia-related cases (44%, 212 of 480). Claims with high severity of injury were less frequent in free-standing ASCs (22%) compared to HORs (34%; P = .004). The most common types of injuries in both free-standing ASCs and HORs were dental injury (17% vs 17%) and nerve damage (14% vs 11%). No difference in frequency was noted for types of injuries between claims from free-standing ASCs versus HORs--except that burns appeared more frequently in free-standing ASC claims than in HORs (6% vs 2%; P = .015). Claims with alleged improper management of anesthesia occurred less frequently among free-standing ASC claims than HOR claims (17% vs 29%; P = .01), as well as positioning-related injury (3% vs 8%; P = .025). No difference was seen in frequency of claims regarding alleged improper performance of anesthesia procedures between free-standing ASCs and HORs (25% vs 19%; P = .072). Technical performance of procedures (ie, intubation and nerve block) was the most common contributing factor among free-standing ASC (74%) and HOR (74%) claims. Free-standing ASC claims also had a higher frequency of communication issues between provider and patient/family versus HOR claims (20% vs 10%; P = .004). Most claims were not associated with major comorbidities; however, cardiovascular disease was less prevalent in free-standing ASC claims versus HOR claims (3% vs 11%; P = .002). The mean ± standard deviation total of expenses and payments was lower among free-standing ASC claims ($167,000 ± $295,000) than HOR claims ($332,000 ± $775,000; P = .002). This analysis of medical malpractice claims may indicate higher-than-expected patient and procedural complexity in free-standing ASCs, presenting patient safety concerns and opportunities for improvement. Ambulatory anesthesia practices should consider improving safety culture and communication with families while ensuring that providers have up-to-date training and resources to safely perform routine anesthesia procedures.

Analysis of Claim Frequency and Severity in Auto Insurance Using Generalized Linear Models in Philippines

Abstract Purpose: The aim of the study was to analyze the analysis of claim frequency and severity in auto insurance using generalized linear models in Philippines. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The analysis of claim frequency and severity in auto insurance using Generalized Linear Models (GLMs) in the Philippines revealed that various factors, including driver demographics, vehicle characteristics, and geographic location, significantly impact the likelihood and severity of claims. The GLM approach effectively modeled these relationships, providing insights into risk factors that contribute to higher claim rates and costs. The findings suggest that younger drivers and those in urban areas tend to have higher claim frequencies, while larger vehicles and those with higher engine capacities are associated with more severe claims. Unique Contribution to Theory, Practice and Policy: Utility Theory, Actuarial Fairness Theory & Risk Theory may be used to anchor future studies on analyze the analysis of claim frequency and severity in auto insurance using generalized linear models in Philippines. From a practical perspective, insurers should increasingly adopt telematics and real-time driving data within their GLM frameworks. Policymakers should encourage and potentially mandate the integration of telematics data in auto insurance modeling.

Real-World Evidence Application in Translational Medicine: Making Use of Prescription Claims to Inform Drug-Drug Interactions of a New Psoriasis Treatment.

Patients with psoriasis often take multiple medications due to comorbidities, raising concerns about drug-drug interactions (DDIs) during the development of new medicines. DDI risk assessments of a new small molecule showed risks of CYP3A4 autoinduction and being a sensitive CYP3A4 substrate. We conducted a real-world evidence (RWE) claims analysis to assess the frequency of prescription claims for up to 12 months from the date of the initial psoriasis diagnosis for drugs that may interact with CYP3A4 substrates. We used 2013 to 2018 patient data from the US Merative MarketScan Research Database. Among patients diagnosed with psoriasis, less than 1% had a claim for a moderate/strong inducer, but up to 15% had a claim for moderate/strong inhibitor. Most prescriptions for CYP3A4 inhibitors or inducers included antibiotics and anticonvulsants. While CYP3A4 inducers were rarely used, those treated received more than >90 days treatment. Then, these RWE data were used to inform the early translational medicine strategy for the new investigational drug by strategically integrating DDI evaluations into a first-in-human healthy volunteer trial prior to studies in patients with psoriasis. The resulting DDI substudy showed that the investigational small molecule did not induce midazolam clearance but was sensitive to CYP3A inhibition, leading to the decision to exclude concomitant use of strong CYP3A4 inducers or inhibitors from clinical trials.

Claim Prediction and Premium Pricing for Telematics Auto Insurance Data Using Poisson Regression with Lasso Regularisation

We leverage telematics data on driving behavior variables to assess driver risk and predict future insurance claims in a case study utilising a representative telematics sample. In the study, we aim to categorise drivers according to their driving habits and establish premiums that accurately reflect their driving risk. To accomplish our goal, we employ the two-stage Poisson model, the Poisson mixture model, and the Zero-Inflated Poisson model to analyse the telematics data. These models are further enhanced by incorporating regularisation techniques such as lasso, adaptive lasso, elastic net, and adaptive elastic net. Our empirical findings demonstrate that the Poisson mixture model with the adaptive lasso regularisation outperforms other models. Based on predicted claim frequencies and drivers’ risk groups, we introduce a novel usage-based experience rating premium pricing method. This method enables more frequent premium updates based on recent driving behaviour, providing instant rewards and incentivising responsible driving practices. Consequently, it helps to alleviate cross-subsidization among risky drivers and improves the accuracy of loss reserving for auto insurance companies.

Spatial copula-based modeling of claim frequency and claim size in third-party car insurance: A Poisson-mixed approach for predictive analysis

The number and amount of claims, referred to as the sum of claims or the total claim/loss amounts in insurance literature, are crucial pieces of information for insurance companies. The analysis of these numerical values can provide essential insights for targeted planning. This study explores a spatial approach for jointly modeling claim frequency and claim size. We assume that the number of accidents follows a Poisson distribution with a variable mean, and this mean, in turn, has a distribution commonly known as a mixed distribution. The spatial dependence structure within the observations is then modeled using an appropriate copula. By estimating the parameters of the proposed model, we draw prediction maps for both claim frequencies and total claim size. These maps will contribute to the prediction of future claim dynamics, offering insurers the opportunity to refine their market strategies and enhance their overall risk management approach based on evolving spatial patterns.

Utilization and affordability of health insurance coverage for rare disease drugs in a first-tier city in Northeast China from 2018 to 2021: a study based on the health insurance claims database

ObjectiveThe accessibility issue of orphan drugs in China is prominent. Based on real-world data from a tier-one city in Northeast China, this study aims to analyze the current usage and affordability of orphan drugs for rare diseases.MethodsThe data was sourced from the health insurance claims data of a certain city from 2018 to 2021, including a total of 16 orphan drugs. The utilization of orphan drugs is assessed using four indicators: frequency of medical insurance claims, medication cost, defined daily doses (DDDs), and defined daily drug cost (DDDc). Affordability is measured using the concept of catastrophic health expenditure (CHE).ResultsBetween January 2018 and December 2021, there were a total of 2,851 medical insurance claims in the city, with a total medication costs of $3.08 million. Overall, during the study, there was a year-on-year increase in the utilization frequency of individual rare disease drugs in the city, with DDDs rising from 140.22 in 2018 to 3983.63 in 2021. Additionally, the annual medication costs of individual drugs showed a consistent upward trend, increasing from $10,953.53 in 2018 to $120,491.36 in 2021. However, the DDDc of individual drugs decreased from $398.12 in 2018 to $96.65 in 2021.The number of sales and the amount of sales for orphan drugs in community pharmacies have significantly increased. Prior to medical insurance coverage, out of the 16 orphan drugs, 9 drugs had annual treatment costs exceeding CHE for urban residents, and 15 drugs had annual treatment costs exceeding CHE for rural residents. After medical insurance coverage, there were no drugs with out-of-pocket costs exceeding CHE for urban residents, while 8 drugs had out-of-pocket costs exceeding CHE for rural residents. Furthermore, both before and after medical insurance coverage, the four treatment drugs for idiopathic pulmonary arterial hypertension were more affordable compared to the four treatment drugs for multiple sclerosis.ConclusionThe usage frequency of orphan drugs in a certain city increased gradually, but the disease burden remained heavy. More policy support should be provided to the priority rare disease populations, and the rare disease medical security and diagnosis and treatment systems should be improved.

On bayesian bonus-malus premium under linex loss function with applications

The majority of bonus-malus systems (BMS) determine the premium for each policyholder solely based on their claim frequency. Adopting this method would be unjust. A driver who files a claim for small damage cannot be subjected to the same penalties as a driver responsible for a claim involving significant damage. To achieve a balance in the portfolio between good and bad drivers, a new portion of the premium is computed as a compromise. To do this, the claim severity is created and assessed to provide a fair premium. The consideration was determined by taking into account both the quantity and magnitude of the claims. This work assumes that the frequency of claims follows a Poisson-Akash distribution, but the severity of claims follows a newly proposed distribution known as the Inverse-Gamma Lindley distribution. The bonus-malus premiums were computed using the Bayesian approach for both the frequency and severity of claims. The premium is computed using the asymmetric Linex loss function for both the frequency and severity of claims in the bonus-malus system. Illustrative instances utilizing an actual dataset for the application of BMS are presented, showcasing the use of claim frequency alone and the combination of claim frequency and claim severity. The R software is employed for these demonstrations. We concluded this paper with a discussion comparing the premiums obtained in each case.

Pricing risk contribution of general Takaful by spatial generalized linear mixed models at the level of tariff cells

PurposeThis study aims to price the risk contribution of general Takaful at the level of tariff cells, considering a spatial dependency framework.Design/methodology/approachThree different models, including a generalized linear model, a generalized linear mixed model (GLMM) and a spatial generalized linear mixed model (SGLMM), according to the actuarial modeling of general Takaful, are used to price pure risk contribution (PRC).FindingsThe results reveal that the SGLMM yields more accurate predictions of the PRC compared to the other models, emphasizing the significance of spatial modeling in this context. Following the estimation of the PRC, the gross contribution according to the mechanism of Takaful models is calculated considering the spatial model.Practical implicationsConsidering the similarities between Takaful and insurance, this study addresses the pricing of general Takaful within different Takaful models through a spatial dependency framework, such that the practical implications of the study are applicable for running Takaful's business in both Islamic and non-Islamic countries.Originality/valueMost studies consider only the social or practical view of Takaful. This study contributes to the broader knowledge and understanding of Takaful by presenting a conceptual understanding of Takaful and then investigates the practical application of pricing risk contribution using innovative modeling of claim frequency and severity at the level of tariff cells.

P-717 Add-ons availability and quality of online information provided by Brazilian IVF clinics

Abstract Study question How do Brazilian fertility clinic websites advertise four IVF add-ons: assistedhatching (AH), preimplantation genetic testing for aneuploidies (PGT-A), ERA and sperm DNA fragmentation test. Summary answer Available information, about ivf add-ons in Brazil, on researched websites is heterogeneous and is not evidence-based according to the last published recommendations. What is known already Over 1 billion people worldwide use online resources to obtain health-related information. People struggling with infertility are increasingly turning to online resources for infertility-related content and social support. “Add-on” procedures are actively promoted on some fertility clinic websites as means to improve IVF success rates. However, the actual contribution of these interventions to IVF success rates remains debatable. ESHRE has recently issued 42 recommendations were formulated on the use of add-ons in IVF. At present, no study has evaluated the type and quality of the information provided on the Brazilian IVF clinics’ websites regarding the merits of “add-ons. Study design, size, duration Prospective evaluation of the availability and quality of information on four common add-ons provided by IVF clinics in the southeast of Brazil between October-November 2023. Clinics were identified using the national registry (SisEmbrio) as well as through manual online search. As only public online available information was used no ethical approval was necessary. Statistical analyses were restricted to descriptive of the frequency of each add-on and frequency of claims made in relation to their use. Participants/materials, setting, methods One reviewer took screenshots of the included clinic pages and claims and a second reviewer double-checked everything. The folowing information was colletced: availability of of the four add-ons; information on the effectiveness on: implanation rate(IR), pregnancy rate (PR) or live-birth rate (LBR); information on the scientifc uncertainty of the use of add-on and possible negative impact; for PGT-A if the clinic informed that the data obtained should be confirmed by prenatal diagnostic testing. Main results and the role of chance The websites of 122 IVF clinics were included. Nine clinics had no website or were offline. PGT-A was the most frequent available add-on (n = 77; 68%) but only two of them (2,5%) informed on PR. There was no information on IR nor LBR. Thirteen clinics (16,8%) acknowledged it could have adeverse effects but only one mentioned the scientifc uncertainty related to its use. Forty (35%) websites displayed AH with 7 reporting on possible negative effects but none disclosed success rates (PR, IR or LBR). Eight clinics admitted AH had uncertain benefits and seven noted possible negative impact. The sperm DNA fragmentation test was presented in 37 (33%) websites but none reported on potential adverse effects nor success rates in terms of PR, IR nor LBR. No website provided information on the uncertainty of the benefit of the test. The ERA-test was advertised in 28 (25%) websites with no information on its benefits on sucess rates. Three clinics advised it could have a negative impact and six agreed its effectiveness was unclear. The cost was never pesented for any of the add-ons researched in accordance to national regulations. Of note, 36 websites advertised other add-ond such as MSOME, ZYMOT and endometrial scrathcing. Limitations, reasons for caution The study was restricted to 122 clinics websites in the southeast of Brazil. Evalaution was restricted to four commons add-ons and the quality of the information provided could not be assessed as the clinics follow no specific regulation on how to advertise scientifc information to their patiens and general public. Wider implications of the findings The information provided by Brazilian IVF clinics could be misleading. The use of add-ons is surrounded by scientific uncertainly related to their benefits and cost-effectiveness therfeore it is of utmost importance that clinics reviewt heir websites and offer sound scientific data to patients and the general public as well. Trial registration number Not applicable