Deloitte Touche Tohmatsu Limited, commonly referred to as Deloitte, is one of the Big Four professional services firms along with PricewaterhouseCoopers , Ernst & Young, and KPMG. Deloitte is the second largest professional services network in the world by revenue and has 182,000 employees in more than 150 countries providing audit, tax, consulting, enterprise risk and financial advisory services. Wikipedia.
De Vos C.,Deloitte
European journal of preventive cardiology | Year: 2013
International research indicates that attendance of patients to a proposed cardiac rehabilitation (CR) programme varies between 21% and 75%. Addressing the reasons why cardiac patients are not participating will improve accessibility to CR. The objective of this study was to investigate patient compliance with cardiac rehabilitation and the reasons of refusing or abandoning the programme. Twenty hospital centres were recruited to participate. Each centre was asked to recruit patients from three patient groups, namely: percutaneous coronary intervention patients, patients that underwent major cardiac surgery, and patients being admitted because of an acute myocardial infarction and not belonging to the other two groups. Patients were asked to fill out a questionnaire during a follow-up outpatient consultation after the cardiac intervention. In total, 226 patients participated in the survey. Most patients were proposed (86%) and accepted (81% out of proposed) to attend a CR programme. Of those who accepted, 77% completed the programme. The main reasons that led to patients' refusal to participate in a CR programme were distance to the CR centre, patients' belief they could handle their own problems, and lack of time. The main three reasons for not completing an initiated CR programme were other physical problems, patients' belief they could handle their own problems, and the cost of rehabilitation. Our findings demonstrate the importance of raising patients' awareness of the benefits of CR. Addressing potential barriers to attend a CR programme should be investigated with patients individually in order to ensure compliance. Source
Deloitte | Date: 2012-05-03
A method and system for determining the importance of each of the variables that contribute to the overall score of a model for predicting the profitability of an insurance policy. For each variable in the model, an importance is calculated based on the calculated slope and deviance of the predictive variable. Since the score is developed using complex mathematical calculations combining large numbers of parameters with predictive variables, it is often difficult to interpret from the mathematical formula for example, why some policyholders receive low scores while other receive high scores. Such clear communication and interpretation of insurance profitability scores is critical if they are used by the various interested insurance parties including policyholders, agents, underwriters, and regulators.
Deloitte | Date: 2015-03-26
A mobile computing device is adapted to transmit to a scoring server URLs of websites browsed using the device. The scoring server can compare these URLs against a merchant URL obtained within a preselected time period from transaction data resulting from a transaction involving a payment product of the device user. A score can be calculated based on the similarity between each URL obtained from the device and the URL from the transaction data. The score represents the likelihood that a website browsed using the device and, as a result, the transaction, is fraudulent. The browsed URLs can also be scored against a database of known fraudulent websites. A notification concerning the legitimacy of the transaction based on the score can be generated and sent to the mobile device in real-time. On receiving the notification, the device can be used to either accept or decline the transaction in real-time.
Deloitte | Date: 2013-07-24
An unsupervised statistical analytics approach to detecting fraud utilizes cluster analysis to identify specific clusters of claims or transactions for additional investigation, or utilizes association rules as tripwires to identify outliers. The clusters or sets of rules define a normal profile for the claims or transactions used to filter out normal claims, leaving not normal claims for potential investigation. To generate clusters or association rules, data relating to a sample set of claims or transactions may be obtained, and a set of variables used to discover patterns in the data that indicate a normal profile. New claims may be filtered, and not normal claims analyzed further. Alternatively, patterns for both a normal profile and an anomalous profile may be discovered, and a new claim filtered by the normal filter. If the claim is not normal it may be further filtered to detect potential fraud.
Deloitte | Date: 2012-02-15
A quantitative system and method that employs data sources external to an insurance company to generate a statistical model that may be used to more accurately and consistently predict commercial insurance profitability (the predictive statistical model). The system and method are able to predict individual commercial insurance policyholder profitability on a prospective basis regardless of the internal data and business practices of a particular insurance company