Fraudulent Claims Research Articles

A Secure AI-Driven Architecture for Automated Insurance Systems: Fraud Detection and Risk Measurement

The private insurance sector is recognized as one of the fastest-growing industries. This rapid growth has fueled incredible transformations over the past decade. Nowadays, there exist insurance products for most high-value assets such as vehicles, jewellery, health/life, and homes. Insurance companies are at the forefront in adopting cutting-edge operations, processes, and mathematical models to maximize profit whilst servicing their customers claims. Traditional methods that are exclusively based on human-in-the-loop models are very time-consuming and inaccurate. In this paper, we develop a secure and automated insurance system framework that reduces human interaction, secures the insurance activities, alerts and informs about risky customers, detects fraudulent claims, and reduces monetary loss for the insurance sector. After presenting the blockchain-based framework to enable secure transactions and data sharing among different interacting agents within the insurance network, we propose to employ the extreme gradient boosting (XGBoost) machine learning algorithm for the aforementioned insurance services and compare its performances with those of other state-of-the-art algorithms. The obtained results reveal that, when applied to an auto insurance dataset, the XGboost achieves high performance gains compared to other existing learning algorithms. For instance, it reaches 7% higher accuracy compared to decision tree models when detecting fraudulent claims. The obtained results reveal that, when applied to an auto insurance dataset, the XGboost achieves high performance gains compared to other existing learning algorithms. For instance, it reaches 7% higher accuracy compared to decision tree models when detecting fraudulent claims. Furthermore, we propose an online learning solution to automatically deal with real-time updates of the insurance network and we show that it outperforms another online state-of-the-art algorithm. Finally, we combine the developed machine learning modules with the hyperledger fabric composer to implement and emulate the artificial intelligence and blockchain-based framework.

How Surgery Saves Money: A Paradox Explained

“A new analysis from U.S. federal government actuaries say that Americans spent $3.65 trillion on healthcare in 2018, according to a report from Axios. “1 This impacts on companies, where health insurance benefits are the second largest cost in a service business, and 3rd largest in manufacturing business2. Back pain alone accounted for $86 billion a year in 2008 3. The top 5 expenses for a commercial line of insurance were for back pain, osteoarthritis, childbirth, injuries, and non-hip/non- spine fractures4. Work related injuries cost an estimated $1.2 trillion annually, which includes lost wage payment, medical care, and short and long term disability5. Of all health care expenses paid by a company, workers’ compensation becomes the most expensive, since the company pays not only for medical care, but also the lost wage of the employee. This is usually 66% of the employee’s salary, which come to the employee tax free. This provides the employee with essentially the same income received when at work. These generous benefits lead insurance adjusters to suspect potential for abuse. Estimates of fraudulent claims range from 1% to 80% depending on unsubstantiated reports in the insurance literature6,7,8,9. However, methods to identify fraudulent cases are not always productive. The State Auditor of the State of California reported that the $30,000,000 a year spent by the State of California to detect workers’ compensation fraud was not cost effective10. Detection methods used include Functional Capacity Evaluations (FCEs). However, Feeler and Schapmire in a review of 180,000 patients found that FCEs were subjective and had no predictive nor diagnostic value11. Attempts to blame prolonged recovery on psychological issues, using the Minnesota Multiphasic Personality Inventory MMPI are consistently thrown out of court often 12. The most reliable method of fraud detection is the Pain Validity Test, which can predict with 95% accuracy who will have abnormal medical tests, and predicts with 85%-100% accuracy who will not have medical test abnormalities, i.e. who is faking or malingering 13,14,15. In a head to head comparison with the MMPI, in the same group of patients, the MMPI had no predictive ability 13,14,15. The Pain Validity Test has always been admitted as evidence in 30 cases in 9 states, which are listed on SlideShare.net 16. Once an injured worker has been identified has having a valid complaint of pain, the next step is to o identify the cause of the pain, and properly treat the worker. Research from several sources at Johns Hopkins Hospital report that 40%-80% of chronic pain patients are misdiagnosed 17,18,19. When accurately diagnosed, 50%-63% of patients required surgery to improve 18,19. While the “common wisdom” is that surgery is expensive, a more granular case by case examination documents that this is fallacious thinking. The following case examples show the benefit of accurate diagnosis and the application of the correct surgery resulting in significant cost savings.

PERKEMBANGAN PEMBUKTIAN PADA PUTUSAN VERSTEK: STUDI KASUS DI PENGADILAN NEGERI SLEMAN DAN KOTA YOGYAKARTA

Article 25 of HIR provides that in case the defendant does not appear at court process (verstek), the claim will be accepted unless it is unreasonable and against the rights. In the beginning, that article was interpreted that in case the defendant doesn’t appear at court process (verstek), the plaintiff shall not obeyed to proof his claim. How is the development of that previous interpretation? Normative legal research has been conducted to redress that issue. The data were collected from secondary data resources from Sleman and Yogyakarta District Court decisions concerning the burden of proof in verstek recently, and being analized qualitatively. The research result shows that there is a development in interpretating Article 125 HIR, where the judges burdened the plaintiff to proof his claim. That interpretation development supports the strive to get the truth beyond reasonable doubt, to meet a fair decision, as good as to avoid fraud claim, and haphazard decision.

Automated Crash Event Data Recording System in Vehicles for Auto-Insurance Industry using IoT

The Automobile sector is the leading edge of global economic growth which intensifies the market share rapidly. The Automobile Insurance Industry ensures financial protection against property damage or corporal injury resulting from traffic collisions and against liability that could also arise there from. One of the strenuous processes explored in this industry is claiming the fund. In current scenarios, the most of auto insurance industry works on various tested traditional methods leading to time-consuming claim settlement and also fraudulent claims. The low level of accuracy and the manual processes are the raising encumbrances for an insurer which leads to dissatisfied customers. This paper proposes, an Event Data Recorder (EDR) which is fixed in the vehicles that continuously monitors and acquires the behavioural information of the automobile and the motorist during the crash. Once the crash event occurs, the EDR sends the data before and after the incidents automatically to the cloud with the help of IoT. Analyses of these recorded variables helps identify the genuine reason behind the cause of accident with better accuracy. Thus, this automated Event Data Recording System helps the Auto Insurance Industry to make facile claims and in the reduction of fraudulent claims.

Assessment of Citations of the Retracted Article by Wakefield et al With Fraudulent Claims of an Association Between Vaccination and Autism

The number of citations can be used to show the influence of an article or to measure the validity of a research study. The article by Wakefield et al that fraudulently reported an association between vaccination and autism continues to accumulate citations even after it was retracted. To examine the characteristics of citations from scholarly literature that reference the 1998 article by Wakefield et al and to investigate whether authors are accurately citing retracted references. In this cross-sectional bibliographic analysis of the scholarly publications that cited a 1998 article by Wakefield et al, cited references were collected from a Web of Science Core Collection search performed on March 11, 2019. A total of 1211 articles were identified, with 58 citing works excluded because they were non-English-language publications or the citation to the study by Wakefield et al could not be located by reviewers. Citing works consisted of books, research articles, letters, editorials, news items, and other scholarly literature. Citations to the article by Wakefield et al were identified and analyzed by 2 reviewers in a blinded screening. Reviewers assigned a characteristic to each citation and indicated whether the retraction was documented. The characteristics of citations to the article by Wakefield et al, were categorized as negative, affirmative, or contrastive; if not, persuasive; and if not, assumptive, perfunctory, methodologic, or conceptual. Whether the partial retraction or notice of retraction was included in the citing work was also documented. Among the 1153 citing works included in this analysis, the most common citation characteristics were negative (838 [72.7%]) followed by perfunctory (106 [9.2%]) and affirmative (94 [8.2%]). A total of 123 of 322 citing works (38.2%) published between 2005 and 2010 documented the partial retraction. After the notice of retraction was published in 2010, the percentage of citing works that documented the partial retraction and/or notice of retraction between 2011 and 2018 increased to 360 of 502 (71.7%). Since the article by Wakefield et al was initially published, authors have mostly negated the findings of the study. A significant number of authors did not document retractions of the article by Wakefield et al. The findings suggest that improvements are needed from publishers, bibliographic databases, and citation management software to ensure that retracted articles are accurately documented.

Current trends in insurance fraud in Russia: evidence from a survey of industry experts

This study aims to explore the current trends in fraud prevention in the insurance industry in Russia. Survey responses from 20 experts and professionals of the leading insurance companies in Moscow were collected. More than a half of them are former police officers who work at security or investigation departments. Survey data analysis was employed. According to the experts’ opinion, existing gaps in the legislation and difficulties in cooperation with the police are the main sources of inefficiency of fraud prevention strategies utilised by the Russian insurance companies. The respondents agreed that both insurers and fraudsters actively use new technologies. Fraudulent claims in compulsory third party liability motor insurance remain the most common activity among Russian criminals, although they quickly expand to health and property insurance. Typically, an insurance fraudster is a 34-year-old male with a college/university degree who cooperates with an insurance broker in 42% of cases. Based on this, a set of recommendations aimed at increasing the efficiency of insurance fraud prevention was produced.

Current Trends in Insurance Fraud in Russia: Evidence from a Survey of Industry Experts

This study aims to explore the current trends in fraud prevention in the insurance industry in Russia. Survey responses from 20 experts and professionals of the leading insurance companies in Moscow were collected. More than a half of them are former police officers who work at security or investigation departments. Survey data analysis was employed. According to the experts’ opinion, existing gaps in the legislation and difficulties in cooperation with the police are the main sources of inefficiency of fraud prevention strategies utilised by the Russian insurance companies. The respondents agreed that both insurers and fraudsters actively use new technologies. Fraudulent claims in compulsory third party liability motor insurance remain the most common activity among Russian criminals, although they quickly expand to health and property insurance. Typically, an insurance fraudster is a 34-year old male with a college/university degree who cooperates with an insurance broker in 42 percent of cases. Based on this, a set of recommendations aimed at increasing the efficiency of insurance fraud prevention was produced.

Unintended Consequences, Loopholes, and Gibberish

This Article examines Congress’s decades-long attempt to ensure that securities class action lawsuits of national importance are litigated in federal courts. The intent is limiting strike suits. Congress attempted to curtail strike suits through the enactment of the Private Securities Litigation Reform Act (“PSLRA”). The PSLRA required heightened pleading requirements to ensure the validity of federal securities class actions. Instead of solving the dilemma, plaintiffs circumvented the PSLRA by bringing fraud cases as state law claims. To combat the circumvention of the PSLRA, Congress enacted the Securities Litigation Uniform Standards Act (“SLUSA”). SLUSA federally preempted state law claims based on alleged misrepresentations, untrue statements, or omissions of material facts, requiring them to be brought in federal court. However, SLUSA did not address the concurrent jurisdiction provision of the Securities Act of 1933. This created an anomaly whereby many federal claims under the 1933 Act were brought in state courts, while state fraud claims were required to be brought in federal court. Congress could have addressed this enigma when it enacted the Class Action Fairness Act (“CAFA”). Instead, CAFA, which reformed class actions generally, exempted most securities class actions from its rules. In 2018, the Supreme Court decided Cyan v. Beaver County and allowed 1933 Act claims covered by SLUSA to continue to be brought in state courts. The Court was silent on non-covered securities. This Article recommends how Congress can accomplish its goal of forcing important securities class actions into federal courts.

Effective Fraud Detection in Healthcare Domain using Popular Classification Modeling Techniques

Fraud is any activity with malicious intentions resulting in personal gain. In the Present Day scenario, every sector is polluted by such fraudulent activities to fetch unauthorized benefits. In HealthCare, an increase in fraudulent insurance claims has been observed over the years which may constitute around 3-5% of the total cost. Increasing healthcare costs along with the hike in fraud cases have made it difficult for people to approach these services when required. To avoid such situations, we must understand and identify such illegal acts and prepare our systems to combat such cases. Thus, there is a need to have a powerful mechanism to detect and avoid fraudulent activities. Many Data mining approaches are applied to identify, analyze and categorized fraud claims from the genuine ones. In this paper, various frauds existing in the Health Care sector have been discussed along with analyzing the effect of frauds in the health care domain with existing data mining models. Furthermost, a comparative analysis is performed on two existing approaches to extract relevant patterns related to fraudulent claims.

APRIDIT Unsupervised Classification with Asymmetric Valuation of Variable Discriminatory Worth

Sometimes one needs to classify individuals into groups, but there is no available grouping information due to social desirability bias in reporting behavior like unethical or dishonest intentions or unlawful actions. Assessing hard-to-detect behaviors is useful; however it is methodologically difficult because people are unlikely to self-disclose bad actions. This paper presents an unsupervised classification methodology utilizing ordinal categorical predictor variables. It allows for classification, individual respondent ranking, and grouping without access to a dependent group indicator variable. The methodology also measures predictor variable worth (for determining target behavior group membership) at a predictor variable category-by-category level, so different variable response categories can contain different amounts of information about classification. It is asymmetric in that a “0” on a binary predictor does not have a similar impact toward signaling “membership in the target group” as a “1” has for signaling “membership in the non-target group.” The methodology is illustrated by identifying Spanish consumers filing fraudulent insurance claims. A second illustration classifies Portuguese high school student’s propensity to alcohol abuse. Results show the methodology is useful when it is difficult to get dependent variable information, and is useful for deciding which predictor variables and categorical response options are most important.

Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs)

Fraud in health insurance claims has become a significant problem whose rampant growth has deeply affected the global delivery of health services. In addition to financial losses incurred, patients who genuinely need medical care suffer because service providers are not paid on time as a result of delays in the manual vetting of their claims and are therefore unwilling to continue offering their services. Health insurance claims fraud is committed through service providers, insurance subscribers, and insurance companies. The need for the development of a decision support system (DSS) for accurate, automated claim processing to offset the attendant challenges faced by the National Health Insurance Scheme cannot be overstated. This paper utilized the National Health Insurance Scheme claims dataset obtained from hospitals in Ghana for detecting health insurance fraud and other anomalies. Genetic support vector machines (GSVMs), a novel hybridized data mining and statistical machine learning tool, which provide a set of sophisticated algorithms for the automatic detection of fraudulent claims in these health insurance databases are used. The experimental results have proven that the GSVM possessed better detection and classification performance when applied using SVM kernel classifiers. Three GSVM classifiers were evaluated and their results compared. Experimental results show a significant reduction in computational time on claims processing while increasing classification accuracy via the various SVM classifiers (linear (80.67%), polynomial (81.22%), and radial basis function (RBF) kernel (87.91%).

Sample statistics as convincing evidence: a tax fraud case†

Abstract This work deals with the analysis of data used by tax officials to support their claim of tax fraud at a pizzeria. The possibilities of embezzlement under study are overreporting of takeaway sales and underreporting of cash payments. Standard methods based on normal assumptions and models based on Gamma assumptions are contrasted. Criteria for the choice of method in practice are discussed, among them, how easy the method is to understand, justify and communicate to the parties. Some dilemmas present itself: the choice of statistical method, its role in building the evidence, the choice of risk factor, the application of legal principles like ‘clear and convincing evidence’ and ‘beyond reasonable doubt’. The insights gained may be useful for both tax officials and expert witnesses, as well as defenders of the taxpayer. A detailed exposition is given, both from the frequentist and Bayesian position, in order to prepare for the various statistical arguments that may be used by the parties. The presented Gamma approach may be applied to any business dealing with ratios of aggregated sales.

Regulatory and policy tools to address unproven stem cell interventions in Canada: the need for action

BackgroundThe marketing of unproven direct-to-consumer stem cell interventions is becoming widespread in Canada. There is little evidence supporting their use and they have been associated with a range of harms. Canada has been slower to act against clinics offering these interventions than other jurisdictions, including the United States. Here, we outline the regulatory and policy tools available in Canada to address this growing problem.Main bodyHealth Canada’s regulations governing cell therapies are complex, but recent statements make it clear that Health Canada believes it has jurisdiction over many of the currently marketed stem cell interventions. Still, further regulatory clarity is needed from Health Canada, as are increased directed enforcement efforts on interventions that fall within their scope. The Competition Bureau, via the Competition Act, prohibits advertisers from making materially false or misleading promotional representations. The Competition Bureau could collaborate with the scientific community to analyze the claims of existing clinics in Canada, and impose sanctions upon those who breach the established standard. Professional regulators, including provincial colleges of physicians and surgeons, have considerable power over what products and services their members can offer. Every college of physicians in Canada requires, via policy and codes of ethics, that doctors maintain evidence-based practices. This requirement is incompatible with offering many unproven stem cell interventions. Litigation may be another tool, including the use of fraud, misrepresentation and/or negligence claims for failing to meet the required standard of care. Finally, political pressure on federal and provincial lawmakers could encourage changes to marketing, cell therapy and professional regulations that would allow a more comprehensive response.ConclusionsIn sum, there are many existing tools that can be used to protect the public from unproven stem cell interventions. Increased bureaucratic will and grassroots efforts are needed in order to effect a positive policy response.

Egocentric Analysis of Dash-Cam Videos for Vehicle Forensics

Video acquisition using dashboard-mounted cameras has recently achieved massive popularity around the world. One of the major developments following the dash-cam's popularity is that videos captured by them can be used as testimony during scenarios, like traffic violations and accidents. The widespread deployment of dash-cams brings new problems ranging from the compromise of privacy by uploading these videos on public websites using videos captured from other cars for making fraudulent claims. Therefore, there is a compelling need to address the problems associated with the usage of dash-cam videos. In this paper, we discuss and highlight the importance of the emerging area of multimedia vehicle forensics. We propose an algorithm for linking a dash-cam video to a specific car. The proposed algorithm is useful for various applications, for example, insurance companies can authenticate the origin of video before processing the claim. In a different scenario of illegitimate video upload on the Web, the video can be traced back to the car it originated from. To this end, we make use of motion blur extracted from dash-cam videos for generating a discriminative feature. We observe that the subtle motion pattern of every vehicle can serve as its unique signature. We extract motion blur from dash-cam videos and use random forest trees for classifying the vehicle correctly. The experimental results on thousands of frames obtained from dash-cam videos of several cars show the effectiveness of our approach. We further investigate the process of forging the signature of a car and propose a counter forensics method to detect such forgery. Also, we discuss the application of our technique to other potential platforms where the camera can be mounted, for example, on the chest of a person. We believe that ours is the first work that describes this new area of research.

Fraud and misrepresentation in retail forest products exceeds U.S. forensic wood science capacity.

Fraud and misrepresentation in forest products supply chains is often associated with illegal logging, but the extent of fraud in the U.S. forest products market, and the availability of forensic expertise to detect it, is unknown. We used forensic wood anatomy to test 183 specimens from 73 consumer products acquired from major U.S. retailers, surveyed U.S. experts regarding their forensic wood anatomy capacity, and conducted a proficiency-testing program of those experts. 62% of tested products (45 of 73) had one or more type of fraudulent or misrepresented claim. Survey respondents reported a total capacity of 830 wood specimens per year, and participants’ identification accuracy ranged from 6% to 92%. Given the extent of fraud and misrepresentation, U.S. wood forensic wood anatomy capacity does not scale with the need for such expertise. We call for increased training in forensic wood anatomy and its broader application in forest products supply chains to eliminate fraud and combat illegal logging.

Abnormal Pattern Prediction: Detecting Fraudulent Insurance Property Claims with Semi-Supervised Machine-Learning

Abnormal pattern prediction has received a great deal of attention from both academia and industry, with various applications (e.g., fraud, terrorism, intrusion detection, etc.). In practice, many abnormal pattern prediction problems are characterized by the simultaneous presence of skewed data, a large number of unlabeled data and a dynamic and changing pattern. In this paper, we propose a methodology based on semi-supervised techniques and we introduce a new metric – the Cluster-Score – for fraud detection which can deal with these practical challenges. Specifically, the methodology involves transmuting unsupervised models into supervised models using the Cluster-Score metric, which defines an objective boundary between lusters and evaluates the homogeneity of the abnormalities in the cluster construction. The objectives are to increase the number of fraudulent claims detected and to reduce the proportion of claims investigated that are, in fact, non-fraudulent. The results from applying our methodology considerably improved these objectives. The experiments were performed on a real world data-set and are the results of building a fraud detection system.

Beyond the Card Reader: Anti-election Rigging Technology and National Security in Nigeria

With the increasing spread of information technology in Africa, digitalisation of elections is gradually becoming a popular phenomenon in the continent. However, there has not been sufficient awareness on the potential dangers in the process of digitalising elections. In this regard, this article provides analysis of the connection between the use of technological model in elections and security using the Nigerian example. The article acknowledges the potency of the technological model to solve the problem of electoral malpractices and ultimately address issues concerning electoral violence over claims of fraud. Notwithstanding its usefulness, the article analyses the challenges associated with the technological model and how this represents a major security threat. In this regard, the key question is: does Nigeria have the technical capacity to manage effectively electronic devices that have the potential of being used to unleash massive electoral fraud that could completely undermine peaceful coexistence within its fragile and sensitive electoral context? Following this, the article advances a number of policy recommendations on the measures to put the technological model into an effective use in order to promote peaceful elections in Nigeria.

Audit Policies Under the Sentinel Effect: Deterrence-Driven Algorithms

Auditing has been commonly used in major industries including healthcare to curb fraudulent activity. In addition to the direct benefits of auditing, data collected in this study through interviews, a focus group, and surveys of medical practitioners and audit experts indicate that auditing has a second-order effect through which audit information diffuses in the practitioner network triggering deterrence. This second-order effect, referred to as the “sentinel effect,” has been overlooked by auditing algorithms that have traditionally focused on detection of fraudulent claims or practitioners only. The authors present an algorithm that supports a deterrence-driven audit approach in the presence of audit and sentinel effects. Simulation results, augmented with real practitioner data from two counties in Florida, indicate the potential of such algorithms at reducing excess costs, especially when waste and abuse are prevalent. Management insight: Savings from audit arise as a result of both the direct audit effect and the sentinel effect, which causes deterrence among people who might hear about audits. Insurance providers could therefore consider connectedness, conditional on high enough risk scores, when selecting practitioners for audit. Through the sentinel effect, large numbers of practitioners receiving the audit information are likely to improve billing behavior, ultimately maximizing deterrence from waste and abuse and reducing overall healthcare costs.

INSURANCE CLAIMS FRAUD IN HOMEOWNER’S INSURANCE: EMPIRICAL EVIDENCE FROM THE NIGERIAN INSURANCE INDUSTRY

This study examines insurance claims fraud in homeowner’s insurance with its empirical findings from the Nigerian insurance industry. In this study, a descriptive research design was employed while purposive sampling method was adopted for information selection. A structured questionnaire was used for data collection. 221 participants were drawn from 31 insurance companies, which were basically general insurance companies that represent 61% capacity of the industry in terms of market structure. Major statistical techniques employed in the study were simple frequency percentage and T-test statistics. While five relevant research questions were stated and to which verbal interpretation were provided, added with supporting evidence, two hypothetical statements were made. The study recommends that effective fraud deterrent should be in place so as to promote stable, confidence-based, result-oriented and trustworthy market environment, and government on its own part must not fail to exhibit the will-power to drive the anti-fraud strategy designed, built and modeled for the operational efficiency of insurance companies and effective service delivery in the heart of the insuring public. This research work contributes to existing knowledge in that it helps broaden the scope of the regulatory bodies on the need to continually engage academia, insurance practitioners, IT experts and other stakeholders in designing and building a more sustainable anti-fraud strategy in improving insurance market penetration and density.

An Assessment of the Evidence for Psychic Functioning

Research on psychic functioning, conducted over a two decade period, is examined to determine whether or not the phenomenon has been scientifically established. A secondary question is whether or not it is useful for government purposes. The primary work examined in this report was gov- ernment sponsored research conducted at Stanford Research Institute, later known as SRI International, and at Science Applications International Cor- poration, known as SAIC. Using the standards applied to any other area of science, it is concluded that psychic functioning has been well established. The statistical results of the studies examined are far beyond what is expected by chance. Arguments that these results could be due to methodological flaws in the experiments are soundly refuted. Effects of similar magnitude to those found in government- sponsored research at SRI and SAIC have been replicated at a number of lab- oratories across the world. Such consistency cannot be readily explained by claims of flaws or fraud. The magnitude of psychic functioning exhibited appears to be in the range between what social scientists call a small and medium effect. That means that it is reliable enough to be replicated in properly conducted experiments, with sufficient trials to achieve the long-run statistical results needed for replicability. A number of other patterns have been found, suggestive of how to conduct more productive experiments and applied psychic functioning. For instance, it doesn't appear that a sender is needed. Precognition, in which the answer is known to no one until a future time, appears to work quite well. Recent ex- periments suggest that if there is a psychic sense then it works much like our other five senses, by detecting change. Given that physicists are currently grappling with an understanding of time, it may be that a psychic sense exists that scans the future for major change, much as our eyes scan the environ- ment for visual change or our ears allow us to respond to sudden changes in sound.