Currently, there is a lack of public research into the detection of fraud. One important reason is the lack of transaction data which is often sensitive. To address this problem we present a mobile money Payment Simulator PaySim and Retail Store Simulator RetSim , which allow us to generate synthetic transactional data that contains both: normal customer behaviour and fraudulent behaviour. We developed agents that represent the clients and merchants in PaySim and customers and salesmen in RetSim.
The normal behaviour was based on behaviour observed in data from the field, and is codified in the agents as rules of transactions and interaction between clients and merchants, or customers and salesmen. Some of these agents were intentionally designed to act fraudulently, based on observed patterns of real fraud. We introduced known signatures of fraud in our model and simulations to test and evaluate our fraud detection methods. The resulting behaviour of the agents generate a synthetic log of all transactions as a result of the simulation.
This synthetic data can be used to further advance fraud detection research, without leaking sensitive information about the underlying data or breaking any non-disclose agreements. Using statistics and social network analysis SNA on real data we calibrated the relations between our agents and generate realistic synthetic data sets that were verified against the domain and validated statistically against the original source. We then used the simulation tools to model common fraud scenarios to ascertain exactly how effective are fraud techniques such as the simplest form of statistical threshold detection, which is perhaps the most common in use.
The preliminary results show that threshold detection is effective enough at keeping fraud losses at a set level. This is a quite worrying statement when we know that we will have more than 2 billion computers connected in , that we send billion emails everyday, and that we are more than 1 billion facebook users and 19 million French using smartphones Study of IPSOS-CGI for Elia Consulting Why banks? Depending on the way you look at them, these facts can be considered threats as well as opportunities: if there are threats for the daily customer and his bank, we can imagine that there are also opportunities for consulting firms who are in fact starting to help the banks in facing these news trends faster by making their action and clients secure.
Indeed, if the banks adapt, that means that their tools have to adapt as well, especially when it is about a quite sensitive thematic like fraud. Of course, legislation adapts to all that and a few years ago, the emergence of intelligence structures, the punishment for concrete fraud, the definition of monitoring frameworks and the several evolutions of law texts has shown the importance given to financial crime and fraud.
These steps cannot be taken regardless to the changing environment in which we live and the opportunities presented by internet including the growing volume of data. Then, we used several data basis such as Business Source Complete and Xerfi in order to cross our sources and insure a good quality of information. At the same time we have started to approach professionals in order to plan for interviews after deepening our research and preparing targeted questions. Besides that, we have participated to the 20th national day of Economic Intelligence on December 11th.
We have met Philippe Besseyre des Horts, Executive Vice President Sales of Invoxis, a software editor start up created in , providing security and investigation services with advanced technologies. He gave us a demonstration of their very innovative Know Your Customer solution that leverage structured and unstructured data simultaneously with advanced text mining capabilities. We also posted a document with our research plan and interview questions in several LinkedIn groups about fraud and Big Data as well as on slideshare sharing slides via linkedIn.
This allowed us to be directly contacted by several professionals interested in the topic. Well experienced in the area of Fraud and Big Data in the banking industry, he gave us interesting ideas and plenty of insight about how consultants help banks managing the risk of fraud in a context of Big Data expansion. We then tried to complete our information thanks to other webinars posted on YouTube.
Since we have adopted several methodologies of research, we have encountered two main constraints. We have noticed through our documentary research the limitation of research articles linked to Big Data as a way to manage the risk of fraud. We confirmed this fact with the professionals that we interviewed. According to the discussions we had, this may be strongly linked to the newness of the topic in the field of research.
Also, given the sensitiveness of the subject we encountered real difficulties to know how some banks react with fraud risk. Many professionals we contacted declined for confidentiality reasons, even if it was about the methodologies and not concrete cases, the IT strategy applied to fraud stays a very sensitive subject.
That was also what we noticed with some professionals who accepted our interviews. For these reasons, we could not go into the details of the existing processes and we wish we could know more about the future tools and strategies of the Banks. Even though, we have succeeded on tackling this issue by compensating with interviews in the consulting field and knowing about the tools of the market which are designed for the banking industry.
Traditional fraud risk management solutions are still the most widely used methodsin the banksbut they tend to become weak in a context of Big Data expansion 2. A strong fraud risk management system Fraud is an intentional act that aims to obtain a material or intangible benefit to the detriment of a person or an organization, committed in contravention of the laws, regulations and internal rules, or infringing the rights of others, or concealing all or part of an operation or set of operations or some of their characteristics.
Generally, we can identify three kinds of fraud: internal, external or mixed fraud. Internal fraud involves the active or passive participation of an employee of the entity bank , either exclusively or in collusion with outside individuals mixed fraud. Globally, it is about limiting the costs of fraud by strengthening prevention and control systems as well as the involvement of all the stakeholders. To reach these objectives, management has a real responsibility to ensure the monitoring of behavior of its employees with increased vigilance for sensitive functions such as the front office and respect the general professional rules compliance of IT authorizations, protection of company data etc.
For these reasons, a strong fraud risk management system is absolutely necessary; it is also a real prevention from reputational damage and heavy sanctions. Fraud can happen in every bank even the biggest and the Bernie Madoff Scandal has shown that. In , Bernard L. Investors were paid returns out of their own money or that of other investors rather than from profits. KYC relies first on the identification and the verification ID, official address, activity etc of the prospect before he becomes a client.
Generally, the bank has to know about the expected use of the account in order to define the risk profile of the client. In France, checking the identity and characteristics of the client is compulsory by law before entering in a relationship article L al.
In order to grant a good KYC, the client data has to be updated on a regular basis, with the necessary and compulsory documents besides that, the bank has to know about the origin and the destination of the funds and ensuring the coherence between the client profile and his financial transactions.
Unlike what is common to think, whistleblowing is often not taken seriously by fraud professionals, unless the content really deserves a real enquiry and contains proof. By contrast, alerts generated by monitoring tools are given much more time and analysis. In fact, banks have tools which help in detecting potentially fraudulent transactions, often by generating alerts showing an unusual transaction considering for instance the habits of the clients or his average transactions.
Norkom Technologies is a leading player in the financial crime and compliance market sector. Norkom enables many banks in France and internationally to fight crime and meet the most stringent demands of the regulator with a portfolio of products that address every aspect of crime and compliance - from money laundering to fraud.
The alerts generated by such a tool will be analyzed and completed by the knowledge the bank has about the client, his transactions history, the relationship manager opinion, extractions by other softwares of the bank, in different databases, on the internet, by getting complementary documents and every other adapted source of information.
The analysis may be completed by softwares such as eFIRST which is often used to capture, sort, process checks and scan them-or by internet platforms like RESOCOM which helps to reveal the extent of identity fraud from private economic actors and public authorities certifying the control of identity supports. To detect fraud, banks also use data analytics: processes and activities designed to obtain and evaluate data to extract useful information and answer strategic questions.
To best use data analytics, banks try to assess risk, define clear objectives, obtain the necessary data, develop and apply procedures, analyze and finally manage results. Limits of the traditional solutions in a context of Big Data expansion The methodologies used in the banks and mentioned before, show the need for banks to cross several sources of information even if they have a tool for detecting suspicious transactions. This can be time and energy consuming. Also, the alerts generated are based on the internal database of the bank which leads to complementary research on the internet in order to get external data.
In reality, the analysis made in banks is based on a procedure to follow by different anti-fraud collaborators; this makes fraud risk management on their hands. Therefore, the analysis can be very subjective and depends on the view or on the experience of the person in charge of the field analysis. It can also depend on the completeness of the information the bank has about its client.
These characteristics are difficult to manage with traditional processes or tools. Indeed, banks can amass petabytes of information in a year and traditional tools struggle to leverage such enormous volumes of data. In addition, traditional methods cannot go as fast as the thousands of credit card transactions occurring every second and loss will occur before anything could be done. Finally, many estimate that 80 percent of data is semi-structured or unstructured.
The latter is near-impossible to analyze with traditional methods. Without being able to leverage Big Data banks are missing significant opportunities to prevent and detect fraud. Traditional methods tend to be inefficient in an increasing complex environment driven by Big Data expansion. They have already invested tens of millions of dollars in technologies and don't even know how to leverage them.
They have so many cases to investigate that it is time consuming and it ends up in huge amount of suspicious transactions backlog. This generates even more fraud cases to analyze. Attackers have become increasingly creative about devising new methods to fraud which makes the detection harder and the analysis more complex for each fraud case. New technologies are evolving in this direction, and many institutions have brought information security professionals into the boardroom.
Nowadays, managers realize that Big Data and its real-time intelligence abilities are strong assets if the insights they enable are quickly available to be applied through new processes, risk rules, and defense mechanisms. Big Data technologies as a big opportunity for fraud risk manage- ment in the banking industry 3. The first challenge for traditional tools is to deal with enormous volumes of data as banks can generate terabytes of new data every hour.
They often use data sampling which is ineffective when it comes to finding fraud. Most frauds are not noticeable in sampling. To be effective in fraud detection, banks have to leverage the complete data set. This is possible with Big Data tools that enable banks to analyze all the data set available to the bank.
Hadoop, which is a framework that processes large data sets across clusters of computers, is one of the main tools able to leverage such a massive amount of data streams. Banks have to analyze transactions as quickly as they occur to take immediate corrective actions in case of threat. To do that, they have to process data in near real-time. When this is not possible with traditional methods, sophisticated tools can integrate advanced analytics such as predictive modeling that will flag or stop fraudulent transactions much sooner and before any damage is done.
As the speed of information generated is increasing, fraudsters are able to evolve and adapt their tactics quickly. To fight these rapidly changing fraud schemes, banks need tools that enable them to be flexible and adjust to these schemes. The solution can detect and block a suspicious transaction in near real-time. An alert is then sent to an investigator for analysis.
SAP solution also allows banks to customize and adapt the solution to evolving threats by changing the rules to the new requirements. The third challenge is to be able to analyze structured and unstructured data simultaneously when most of data is semi-structured or unstructured. Indeed, this type of data is very difficult to analyze and time-consuming with traditional methods that are limited to the analysis of structured data.
The ability to analyze text and other unstructured data can give lots of insight to the bank's fraud management. It can spot criminals who can hide behind the structured data: forwarding sensitive information to a personal email account for instance. A good example of challenging unstructured data analytics for fighting fraud is text analytics. It is the process of structuring text using different techniques and algorithms for detecting patterns and connections in the text.
As text is open-ended and can be interpreted in numerous ways, analysts need to start with hypothesis and know what they are looking for in the text, which is not easy to shape accurately. But what banks are seeking now with Big Data solutions is being able to harness both structured and unstructured data that exist in different locations simultaneously.
It allows the bank to get a full and accurate view of the enterprise and detect more fraud schemes such as collusive relationships. Importance of reliable BigDatatools foranefficient fraudrisk management Big Data tools enable banks to be more efficient in their fraud risk management.
Traditional tools and methods can be time consuming and have limitations in doing analytics with Big Data. On the contrary, Big Data tools harness data at speeds once inconceivable. It accelerates the processes giving the company access to data in real- time. What would have taken weeks or months for investigators can now be done in Manually analyzed by an investigator, it can be very inefficient and time becomes a major inhibitor.
Thanks to Big Data tools such as text analytics and mining, this becomes possible: it generates efficiency, uncovers fraudulent tactics quickly and ensures that legitimate transactions are processed without delay. Besides that, Big Data solutions tend to be more accurate. Managing Big Data with traditional methods doesn't allow for the level of scrutiny and analytics that is needed to detect the most hidden fraudulent activities. Key connections can be missed. On the contrary, Big Data tools are more accurate as advanced algorithms can be developed to be more precise and reduce false positives and negatives.
This accuracy leads to less cases to analyze but better qualified, which also means time-savings. Banks can more accurately define what risk areas they have to monitor and invest time and money on. Big Data tools tend to have limitless capabilities and integrate an increasing number of useful applications such as visualization and social network analysis.
As we have seen, they allow banks to leverage and analyze tremendous volume of diverse data in near real-time. This helps banks to minimize the zone of ignorance and detect frauds that wouldn't be uncovered previously. To derive even more insight from Big Data, some technologies offer the capability to visually analyze data. Visual representations illustrate the story behind the data and demonstrate connections that are not obvious between people, places and things.
This gives the bank a holistic view of interconnections between accounts and transactions across channels and products and for a network of individuals. Visualization is also a powerful and insightful tool for communicating fraud cases to management, fraud investigators or law enforcement. Once the links are constructed, they need to be refined with analytical techniques to produce meaningful networks that have a high likelihood of actual or potential fraud.
However, SNA is not new but what changes is the emergence of capabilities to construct networks automatically and leverage Big Data. Big Data solutions tend to be more beneficial and least costly Traditional methods provide reactive analysis of suspicious transactions, which doesn't protect from loss. Indeed, the earlier the bank uncovers the fraud the greater its chances are to stop the fraudster before any incident and minimize the associated loss.
That's why, Big Data tools tend to be proactive, predictive and preventive thanks to their advanced and automated analytics capabilities that allow continuous monitoring. This helps banks to take more informed and anticipatory decisions.
These abilities tend to discourage fraudsters to attempt any crime and seek an easier target. He also highlighted that if there is fraud it is often because of a weak control environment. Continuous monitoring helps detect weak controls and give key insights to internal auditors for improving existing controls or creating new policies. Effective controls can be powerful in preventing frauds. Beyond the detection of fraud, banks have to remain compliant in an increasing regulatory environment to prevent engaging in fraudulent activities themselves.
The bank has to pay a fine of 9 billions of dollars and this event damaged its reputation. As these regulations change from country to country, it is difficult for banks to have an overall view of a customer across geographic regions. However, to avoid large fines and reputation damages, banks have to improve their KYC processes to meet regulatory requirements. Banks should be able to detect potential criminals among individuals they do business with or want to do business with and regularly update their profiles as events occur.
In fact, the latter struggle to watch consumer behaviors across product lines, channels, systems and geographic areas. Indeed, this approach works channel by channel or region by region, which is too slow and inefficient for an early detection of threats. Fraudsters already know this weakness and take advantage of it.
Indeed, instead of setting a large one-time attack that could be uncovered, they do lots of small activities in multi-areas. Fraudsters are evolving from individual attacks to very organized and decentralized tactics. They also go to new banking channels where controls are not yet strong enough such as electronic banking online, mobile and other e-channels. The cost of implementing Big Data tools is still high Implementing a Big Data solution is a major decision, as it is time and cost spending to get the right information.
Banks are aware of the Big Data phenomenon and its potential benefits for fraud management but they lack specific information to decide about implementing any of the Big Data solutions. As we have seen previously, banks already struggle to get full advantage of their current systems and lack of information about Big Data tools, which are even more difficult to apprehend, they are not feeling ready for implementing more advanced solutions.
Moreover, today we are still at an early stage of Big Data solutions and banks are waiting for more proof of concept. IT investment is most of the time a strategic decision taken by a Chief Information Officer who builds a several years plan and it takes time to make such a decision and invest in Big Data. Prior to any Big Data technology implementation, a bank needs to centralize its organization. Chip Kohlweiler explained that Banks often operate in silos.
They can have commercial and consumer banking fraud departments completely separated and using completely different technologies. It doesn't make any sense but it is often the result of their historical growth through mergers and acquisitions and their focus on customer satisfaction.
Indeed, in many large banks, each line of channels credit cards, online banking With this siloed approach to fraud management, the bank's different groups cannot communicate well to each other and will miss relationships between channels. To successfully implement a Big Data solution, it has to be integrated in the overall system. For that, banks have to first coordinate their fraud management departments.
Reorganizing is not a quick and easy task. As most of Information technology investments and implementation, Big Data solutions are costly. First, they are based on sophisticated technologies and banks have to pay the price to get such advanced technologies and replace some of their existing technology and infrastructure.
This expense often doesn't fit in the budget. For large banks it is often a multiyear project with an important change management. Since it is often too costly to implement a Big Data solution, IT managers first think of what they could improve and get full advantage of their existing fraud management tools such as integrating additional data or predictive capabilities.
Another equally important key is to recruit a team of experts such as data scientists who know how to get advantage of the Big Data technologies. These professionals know what data is available, how to leverage it and what to look for in the data to detect any fraud. A regular business analyst won't necessarily know what fraudulent activities look like in the data. Chip Kohlweiler explained that they recommend to their clients to have a team of data scientists to support the fraud management system.
Banks need the right skilled people to keep the fraud management system going on in the long term without having to rely on external consultants. Data scientists can continuously review the models and adapt them regularly to the new requirements. However data scientists can be scarce and are expensive to attract and retain.
Sometimes, it is even better for banks to hire more staff to work on more alerts as new fraud activities are detected and prevent losses. In addition, banks have to make sure they have someone senior on board such as a Chief Information Officer or a Chief Data Scientist who are expert in new technologies and can make the right decisions.
Banks are always in a dilemma about whether or not putting painful controls for customers to prevent fraud. Leveraging Big Data leads to data privacy issues because being able to collect larger set of data regardless of where they are hosted and use them with near-limitless capabilities make violation of privacy easier. In addition, privacy and information security laws differ from one country to another and are even evolving more rapidly these last years in result of the Big Data phenomenon.
That's why it is important to implement a security and privacy strategy to prevent any abuse or data breach and preserve the customer's trust in the bank. He should ask himself the questions: How can we protect the customer's privacy?
What data do we collect? Are these data sources protected? Who can have access? How to use these data without abuse? Big Data solutions cannot replace traditional methods andfinding fraudstill relies on human intelligence Big Data tools are powerful tools indeed, but can we say that they can replace human intelligence?
If that was the case, how to analyze unique fraud cases? How to ask the right questions at the right moment? Managing the fraud risk is about all of governance, people, methods, practices and environmental factors. One should also remember that fraud detection and prevention starts at the client level, the education of the client for an adequate use of the payment tools and his sense of discernment are the first qualities which cannot be replaced by a tool, even a Big Data tool.
Also, the bank advisor the relationship manager plays a crucial role in appreciating the profile of the clients, his needs, his habits, his reactions and the general feeling established. Fraud risk management is a concern for each employee of a bank from the This shows that the tool that transforms raw data into useful information is a wonderful asset but it can lose its value if it is not used as it should and by the right persons.
What Consulting firms can use from our study 5. How cana consultingfirm help its client to manage fraudrisk in a context of Big Data expansion? When banks ask for consulting services for their fraud risk management, it is because they need help and advice from informed professionals. That's why, consulting firms must keep up to date. They have to be aware of changing environment such as bank regulatory requirements, evolving fraud schemes in the banking industry and latest fraud management solutions in a context of Big Data expansion.
For instance, if the concept of Big Data is new for the bank, they have to be able to define it and explain how to leverage it. It is derived from the main solution but it contains predefined rules and other content relevant for the banks. SAP co-developed this solution with large banks and specialized consulting firms. Banks also expect from consultants an effective assessment of their current fraud management.
Consulting firms have to be able to bring out any dysfunctions, how to get full advantage of the existing system or its limits. The latter could lead the bank to update or change its current fraud management. Are they effective and is there any weakness?
The current study offers detailed guidance to external auditors in this area. The findings of the current study also revealed that management integrity is a significant factor in assessing the risk of financial reporting fraud and that rationalisation of fraud should be assessed as part of management integrity rather than a separate fraud risk factor. The current study found that fraud perpetrators capabilities are equally significant to the opportunity to commit fraud factor yet it is currently ignored by the audit standards and thus should be assessed as part of opportunity to commit fraud.
The current study was the first to explore financial reporting fraud and the extent by which external auditors comply with ISA in the Egyptian context. The current study offered recommendations to external auditors, audit firms, audit regulators, and the Egyptian government on how to combat financial reporting fraud. Potential areas for future research were also identified by the current study. School Business and Economics. Department Business. Notes A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.
Usage metrics. Categories Business and Management not elsewhere classified. The resulting behaviour of the agents generate a synthetic log of all transactions as a result of the simulation. This synthetic data can be used to further advance fraud detection research, without leaking sensitive information about the underlying data or breaking any non-disclose agreements. Using statistics and social network analysis SNA on real data we calibrated the relations between our agents and generate realistic synthetic data sets that were verified against the domain and validated statistically against the original source.
We then used the simulation tools to model common fraud scenarios to ascertain exactly how effective are fraud techniques such as the simplest form of statistical threshold detection, which is perhaps the most common in use. The preliminary results show that threshold detection is effective enough at keeping fraud losses at a set level.
This means that there seems to be little economic room for improved fraud detection techniques. We also implemented other applications for the simulator tools such as the set up of a triage model and the measure of cost of fraud. This showed to be an important help for managers that aim to prioritise the fraud detection and want to know how much they should invest in fraud to keep the loses below a desired limit according to different experimented and expected scenarios of fraud.
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Part 3: Applications pdf ppt Applications of network representation learning for recommender systems and computational biology. Jure Leskovec at Stanford University. The group is one of the leading centers of research on new network analytics methods. In recent years, the SNAP group has performed extensive research in the area of network representation learning NRL by publishing new methods, releasing open source code and datasets, and writing a review paper on the topic.
William L. His research focuses on NRL as well as large-scale computational social science applications. His research focuses on deep learning algorithms for network-structured data, and applying these methods in domains including recommender systems, knowledge graph reasoning, social networks, and biology.
He is the co-lead developer of the GraphSAGE framework, and he has undertaken industry collaborations to apply this framework to real-world web-scale recommender systems. His research focuses on the analysis and modeling of large real-world social and information networks as the study of phenomena across the social, technological, and natural worlds. Problems he investigates are motivated by large scale data, the Web and Social Media.
Rok Sosic is a senior researcher in Prof. Why didn't he just keep his mouth shut? Follow him to baggage and out the door. You can escort me to carousel after I point him out. The incident drew national attention, prompting federal investigations to examine Clarke's conduct. Attorney's Office declined to prosecute Clarke for federal civil-rights offenses, writing: "Our decision is not meant to affirm the wisdom or propriety of what occurred.
It reflects only our belief that it would be difficult or impossible to prove a violation of the only federal statute available to us Milwaukee County auditors launched an investigation into whether Clarke abused taxpayer resources during the airport incident. Clarke is a strong supporter of Republican Donald Trump , saying during Trump's presidential campaign that he would "do everything I can" to help Trump win the presidency.
Pitchforks and torches time. In May , Clarke said in a radio interview that he would take the post of Assistant Secretary of Homeland Security for Partnership and Engagement in the Trump administration. The White House declined to comment, and the Department of Homeland Security stated that no appointment had been officially made. The position does not require Senate confirmation.
The DHS did not say whether the appointment was actually offered to Clarke. Harris wrote that "Clarke's unconscionable record makes him unfit to serve" and that the "appointment is a disgrace. Kelly , who had been the Secretary of Homeland Security at the time, told Clarke that he would not be given a position at the DHS in part due to scandal surrounding the treatment of inmates in Clarke's jail and the ensuing negative media attention.
Clarke "has become a fixture of conservative media" and in began hosting a podcast talk show, David Clarke: The People's Sheriff , on Glenn Beck 's TheBlaze Radio Network ,   where he has expressed support for the occupation of the Malheur National Wildlife Refuge.
Clarke's higher profile coincided with an increase in his speaking fees and time spent outside Milwaukee County on outside activities. On August 31, , Clarke resigned his position. A few days after his resignation as sheriff, it was announced that Clarke had joined pro- Donald Trump super PAC America First Action as a spokesman and senior advisor,  where his role was to make regular appearances in the media, particularly on Fox News.
In August , a report in Urban Milwaukee itemized Clarke's role since in Steve Bannon and Brian Kolfage 's We Build the Wall scam, as an active member and a central figure in many of its fundraising efforts. Clarke frequently appears at public events on horseback wearing a cowboy hat.
In January , Clarke announced he was considering a run for mayor of Milwaukee in ,  but ultimately decided not to run,  instead endorsing Republican Alderman Bob Donovan's unsuccessful bid to unseat Mayor Tom Barrett. Clarke married his wife Julie in ; she was a court clerk and later a real estate agent. They lived on the northwest side of Milwaukee. In , Clarke filed for divorce from his wife. He is said to be a "deeply religious" Catholic. From Wikipedia, the free encyclopedia.
For other people named David Clarke, see David Clarke disambiguation. American former sheriff. Main article: Electoral history of David Clarke. Milwaukee Magazine. Retrieved May 26, San Francisco, CA. Media Matters. Archived from the original on May 21, September ABC News. Retrieved May 21, Guy is a sleaze bag," Clarke wrote in a post that linked to a story in which Sen.
Rand Paul of Kentucky pushed back against a Kaczynski-authored story for Buzzfeed News, in which he was accused of using disputed quotes. The Atlantic. Retrieved May 17, Retrieved November 17, You heard it first here. August 18, The Economist.
Retrieved August 20, Lib policies created it". Bizpac Review. Retrieved July 18, Milwaukee Journal Sentinel. Retrieved March 30, Milwaukee Journal-Sentinel. The Daily Beast. Retrieved May 18, The Hill. Retrieved March 21, Shepherd Express. Apparently, one , Milwaukee Journal Sentinel July 27, National political donors spend hundreds of thousands on local Milwaukee sheriff's race , Fox News August 12, Clarke Jr".
Friends of Sheriff Clarke. Archived from the original on July 18, Journal Sentinel. Clarke's sheriff of the year honor isn't your typical award: Constitutional Sheriffs and Peace Officers Association known for anti-government views , Milwaukee Journal Sentinel May 15, Retrieved August 4, Johnson September 15, The Huffington Post. September 19, Archived from the original PDF on December 19, Retrieved January 22, Associated Press.
March 10, Retrieved March 1, New York Times. May 28, Retrieved May 29, Washington Post. Retrieved June 11, Americans United for Separation of Church and State. Clarke , F. February 3, Retrieved February 14, Black filed a complaint with Milwaukee County a few weeks. Clarke responded by threatening Black on Facebook, saying, 'Next time he or anyone else pulls this stunt on a plane, they may get knocked out. The sheriff said he does not have to wait for some goof to assault him.
He reserves the reasonable right to pre-empt a possible assault. Public Policy Polling. Retrieved April 23, Retrieved July 13, Clarke [ SheriffClarke] October 15, Pitchforks and torches time" Tweet — via Twitter. October 15, Retrieved October 15, The Washington Post. Archived from the original on November 26, Retrieved September 7,
The findings of the current study also revealed that management integrity is a significant factor in assessing the risk of financial reporting fraud and that rationalisation of fraud should be assessed as part of management integrity rather than a separate fraud risk factor. The current study found that fraud perpetrators capabilities are equally significant to the opportunity to commit fraud factor yet it is currently ignored by the audit standards and thus should be assessed as part of opportunity to commit fraud.
The current study was the first to explore financial reporting fraud and the extent by which external auditors comply with ISA in the Egyptian context. The current study offered recommendations to external auditors, audit firms, audit regulators, and the Egyptian government on how to combat financial reporting fraud. Potential areas for future research were also identified by the current study.
School Business and Economics. Department Business. Notes A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University. Usage metrics. Categories Business and Management not elsewhere classified. Hide footer. You do not have access to any existing collections. You may create a new collection. Hines, Christine G. Thesis or Dissertation.
In Copyright. Computer Science. Youssef, Abdou.
Although there is no prescribed style for the completed thesis, there are several style manuals available which may prove helpful. The student should contact the thesis advisor to discuss the style manual to be used. Above all, it is important to be consistent throughout the entire thesis. Decide how you wish to structure your manuscript and be consistent throughout it.
All College of Technology theses submitted in an electronic format may be hosted on the College webpage. You must submit an electronic copy of the thesis in pdf format that accurately represents the printed version of the final document. Questions to ask when evaluating your master project topic: Is there current interest in this topic in the field? Is there is a gap in knowledge that work on this topic could help to fill?
Is it possible to focus on a manageable segment of this topic? Identify a preliminary method of data collection that is acceptable to your advisor. Is there a body of literature is available that is relevant to your topic? Do you need financial assistance to carry out your research? Is the data necessary to complete your work is easily accessible?
Define the project purpose, scope, objectives, and procedures. What are the potential limitations of the study? Are there any skills called on by the study that you have yet to acquire? Write your thesis per College of Technology thesis guidelines. Successfully defend your thesis. Make corrections per the thesis committee.
Committee signs the approval page. Submit a copy of the final thesis version to the Associate Dean for Research for Graduate Studies or your graduate advisor for formatting review a minimum of two weeks prior to the end of the semester. Wait for formatting approval before beginning electronic submission process. Select a master project advisor. Select a project topic. Finish the literature review and finalize the project topic. You can stay in touch with your paper writer every step of the way, ask them questions and request drafts.
This will allow you to stay on top of the process and see how a thesis statement becomes a full paper. How much more detail can you add? Has any new research been made on that topic? Do a quick computer search on the topics on your list to see which one is widely researched.
This means finding a topic that is discussed not just on websites or blogs, but more so in books, articles, and even encyclopedia references. Find a topic that is both interesting and has plenty of published material. Check magazine articles, because these are usually shorter and more updated than those found in books.
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