Criminal Demographics - Assignment Example

CRIMINAL DEMOGRAPHICS by The of the The of the School The and where it is located The Date Discussion The results of the study reflect the tendency that fraud crimes happened more often (Doig, Levi, Bambridge & McIvor, 2001; Gill, Woolley & Gill, 1994). They challenge insurers and investigators because they require specific skills and interventions to deal with offenders and investigate insurance fraud cases (Bond & DePaulo, 2006; Vrij, 2008a). The study clearly shows that there is no common agreement between studies connected with criminal demographics of white collar offenders and insurance fraud offenders in particular. The results do not fully meet the established profile of insurance fraud offender because of significant variations in expected age and gender of fraudsters. Despite the common data that men represent the majority of offenders in all classified crimes (Smith & Paternoster, 1987, The Home office, 2004f, Goodkind, Ng & Sarri, 2006; Berman & Dar, 2013; Barnett, 2002), female offenders are evidently numerous in insurance fraud crimes. Household and motor insurance fraud cases display higher prevalence of women which does not correspond to the average rate of female offenders’ participation in crimes. Generally, they represent 6.6-15% of all offenders (Berman & Dar, 2013; Perreault, 2014; Federal Bureau of Prisons, 2014). The Home Office (2004f) reported 27% female offenders in its study, but in the analysed sample, women offenders make up 38% of all cases with the significant prevalence of females (43.6%) in household insurance fraud. These results are crucial for insurance fraud detection and investigation because general claim that females are underrepresented in general offender group reduces attention of insurers who fail to detect possible risks of dealing with their clients. It also reflects the tendency of increasing number of female offenders in all groups of crimes (Schwartz, 2011). As for the median age of offenders, it corresponds to the common data that white-collar offenders are older than other group of criminal. The median age of offenders in the sample in 39 years old; motor insurance offenders are younger that household insurance offenders with the median age of 37 and 41 respectively. Previous studies found that offenders aged 18-25 are overrepresented within insurance fraud offense group (The Home Office, 2009). At the same time, sample analysis does not correspond to this information; insurance fraud offenders tend to be older with the peak of criminal activities between 34-43 years old. This discrepancy in the results shows that each type of insurance fraud requires proper analysis of criminal demographics. In many ways, previous research fails to provide comprehensive data for risk group identification studying insurance fraud as a unity. Data connected with insurance fraud cases heavily depends in the community where they take place. Inconsistencies in data point out the need to avoid generalizations insurance fraud research. Previous findings display controversial results concerning previous criminal record in cases of white collar offenders. Some of researchers state that white collar offenders usually do not have any records (Barnett, 2002; Croall, 2001) while others state that fraudsters usually engage in different types of crimes like theft (The Home Office, 2003). Based on results of sample analysis as well as previous studies of insurance fraud (The Home Office, 2003), it is possible to notice that insurance fraudsters are very likely to repeat their crimes. In most of the cases, they are represented by fraudulent insurance claims submitted to their insurers. The type of fraud depends on the abilities and imagination of fraudsters who are reported to be very creative (Šubelj, Furlan & Bajec, 2011). As well as other groups of crimes, people with prior criminal record are more likely to resort to unlawful activities again (Berman & Dar, 2013). Moreover, people’s attitudes to fraud crimes show that they are perceived as a way to make money easily (ABI, 2013). Insurance fraud is rarely related to high scale victimization crimes as it consists of individual claims of the insured. According to ABI (2013), almost 38% of people perceive it as a crime which does not harm other people. Based on the sample, almost 70% of household claimants had previous claims. Fraudsters tend to conceal the actual number of previous claims they have made. Their choices vary from reporting reduced number of claim or reporting nothing at all. Currently investigation involves retrieval of data concerning previous claim in past 6 years. Extended period of database investigation might add clarity to the individual cases as well as general profiling of insurance fraud offenders. Two types of insurance fraud offenses under the analysis reveal that fraudsters tend to ask for something minor and not too expensive to avoid increased attention and examination conducted by the insurers. Technical devices top the list of claimed items. The most common reasons are those which have fewer evidences after them. Knock/drop (35.5%), spillage (25%), theft (18%) and accidental damage (10.5%) are the most common as they do not leave any obvious evidences in the area where the action took place. Cases with obvious forensic evidences are rare because they require proper investigation which can reveal the fraud effectively. Fire, power failure or weather circumstances are easy to check, so fraudsters choose actions which can hardly have any witnesses or other organization involved. In cases of motor insurance fraud, local cars are more likely to be claimed, as it is easier to receive compensation from the manufacturer. One of the most common features shared by all insurance fraud offenders is the lack of evidences to support their claim. Only a few cases contained any technical reports (5.2%) about the damage. Most fraudsters represented only photos of the circumstances where the damage took place. Receipts and inventory are also rarely represented to support the claim of the policyholder. Even though any evidences are required for effective investigation, insurers and investigators are limited in their actions by public opinion about their reputation. According to Bourgeon & Picard (2012), any doubt in the truthfulness of the claim filed by the policyholder leads to the accusation in “nitpicking” and no desire to pay back the compensation. In this way, there is a paradox in the attitude towards insurance fraud; people treat insurers worse than fraudsters. Fraud crimes investigation in criminal justice system requires updates in the system. It has been already stated that IT improvements for criminal data bases as well as development and training programs for law enforcement officers are required (Morley, Ball & Ormerod, 2006; Derrig, 2002; Doig et al, 2001).The ability to detect lie experiences no advances for a long time. The mean result of lie detection is 54% which is not enough to detect fraud successfully (Bond & DePaulo, 2006; Vrij, 2008a). Even though behaviour of fraudsters is extensively researched and described in academic literature, professional lie detectors fail to behave the way they are expected in the process of investigation. Interestingly, neither experience nor emotional intelligence of professional lie detectors contributes to their ability to detect fraud (Baker et al., 2013). In case of emotional lies which are common in fraud offenses, focus on emotions leads to overconfidence and results in failure to detect the actual case of fraud (Baker et al., 2013). People who seek for the solution in technologies also do not get high accuracy of results due to imperfect work of technical lie detectors (Vrij et al., 1996; Mann et al., 2002). Professional lie detectors require extensive training to develop a skill of recognizing verbal and non-verbal signs of lie better (Bond & DePaulo, 2006; Vrij, 2008a). Their skills can be best applied in interviews with offenders where they have to answer questions on the spot. In stressful situations lying requires great concentration and cognitive efforts which lead in delays in the answers, specific mimics and gestures, and inconsistencies in the reports (Vrij et al., 2001; Mann et al., 2002). The sample analysis shows that direct questions concerning information related to the offence are stressful for fraudsters. As a result, they report information about their previous claims or any other information they usually prefer to conceal from investigators. Based on these results, effective interview plan which include stressful questions can change the quality of information insurers and investigators get in the process of dealing with potential cases of fraud. The availability of database makes it possible to retrieve information about any person, and insurers should pay specific attention to this opportunity as a means of insurance fraud crimes prevention. Having access to the latest technological advances, they can include the record about prior claims in the profile of the insured initially to mark the group of risk. The study has identified that computer system errors still have place in the system. In the sample, 2 out of 78 cases (2.6%) had no reference in the data base and were reported as missing information. Despite the fact that the percentage of the lost information is relatively small, the system has to be updated to make it more reliable. Advancements in IT technologies contribute to the ability of fraud investigators to retrieve more information about the claimant using unconventional sources of information. According to Šubelj, Furlan & Bajec (2011), disclose much private information which can be added to the case investigation with the purpose to draw a clearer picture of psychological profile of the claimant. Researchers argue that investigators do not utilize technological data bases effectively. In most cases investigators rely on their ability to detect lie that is hardly accurate. It is especially common in motor fraud cases because they are less systematized than household insurance cases. As far as insurance fraud cases are always tied to certain location, they are more likely to happen in urban areas where insurance agencies are full of various cases, and they tend to be more inattentive to potential fraudsters. Insurances agencies are the first instances to collect information about policyholders and identify risk groups based on the features of offenders discussed above. Networking is a real way out to protect insurance industry from fraud. Local insurers, who focus on competition in terms of market economy, need to cooperate to create shared local databases to inform their partners about fraudsters and keep their profiles updated. Effective utilization of data collection resources and cooperation with other insurers can result in reduced number of insurance fraud cases in the whole country. This research paves a way to several more advanced studies of insurance fraud offenders. First of all, it is helpful to apply the same methodological framework to conduct studies focus on a single area. Local qualitative research can expand the findings on this work adding psychological portraits of offenders and circumstances of their offences. Strategies for information collection and analysis need to be developed to implement appropriate training for insurers and investigators who deal with insurance fraud. The results of this research can be successfully utilized in the UK as a resource of criminal demographics data and a guide to risk group identification based on most common characteristics shared by fraudsters. At the same time, a similar study needs to be repeated in several years to observe the tendencies and changes in insurance fraud offenders group and make insurers and investigators aware of them. References Baker, A., ten Brinke, L., & Porter, S. (2013). Will get fooled again: Emotionally intelligent people are easily duped by high‐stakes deceivers. Legal and Criminological Psychology, 18(2), 300-313. Bourgeon, J. M., & Picard, P. (2012). Fraudulent claims and nitpicky insurers. Šubelj, L., Furlan, Š., & Bajec, M. (2011). An expert system for detecting automobile insurance fraud using social network analysis. Expert Systems with Applications, 38(1), 1039-1052. Read More