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Identifying & mitigating fraud risks in a Remote Working Environment

While the direct financial impact on organizations and the personal financial impact on employees is obvious, a large percentage of companies have had to adjust to a workforce that has migrated to a remote working environment. As the challenges of operating a remote workforce are great, companies must also ensure that assets are protected from fraudsters seeking to take advantage of potential weakened internal control structures caused by a remote workforce.

Forensic accounting

Forensic Accounting is a combination of accounting and investigative techniques that plays an imperative role in performing timely detection, prevention, and regulation of corporate fraud. It involves detailed research and analysis of financial information to detect fraud and embezzlement cases so as to help explain the nature of financial crimes in the courts. The role of a forensic accountant is to assist the courts, clients, and solicitors understand complex financial and accounting issues.


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As technology infiltrates every aspect of our lives, it is no wonder that solving crimes has become almost futuristic in its advances. From retinal scanning to trace evidence chemistry, actual forensic technologies are so advanced at helping to solve crimes that they seem like something from a science fiction thriller.

With all this forensic technology, its no wonder that this field is one of the fastest growing in the U.S. Shows like CSI and NCIS have made most of the forensic science techniques used today common knowledge. You might think that virtually the whole gamut of forensic technology is old hat to today’s savvy viewer. In fact, there are a number of incredibly cool forensic technologies that you probably never knew existed.

7 best computer forensics tools [updated 2021]

Computer forensics deals with the collection of evidence from digital media, such as desktops, mobile devices, cloud computing and IoT devices. This evidence can be used as part of incident remediation activities or to support law enforcement activities.

Digital evidence can exist on a number of different platforms and in many different forms. Forensic investigation often includes analysis of files, emails, network activity and other potential artifacts and sources of clues to the scope, impact and attribution of an incident. Due to the wide variety of potential data sources, digital forensics tools often have different specialties. This list outlines some of the most common and widely used tools for accomplishing different parts of a computer forensics investigation.

Digital Forensics: What Is It in 2021—2022?

Rapid technological development has given rise to cybercrimes. More often than not, criminals use technology in planning and committing other kinds of crime. Computers, smartphones, flash drives, and cloud data storage are among many types of devices that keep digital evidence. Not only do cybercrime specialists have to know how to collect and analyze data, but they also have to comprehend the legal basis of using this data in the judicial process. Read on to find out about digital forensics in 2020—2021

A guide to digital forensics and cybersecurity tools

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Digital devices are ubiquitous and their use in chain-of-evidence investigations is crucial. Today’s smoking gun is more likely to be a laptop or a phone than it is a more literal weapon. Whether such a device belongs to a suspect or victim, the vast swathes of data these systems contain could be all an investigator needs to put together a case.

That said, retrieving that data in a secure, efficient, and lawful manner is not always a simple endeavor. Investigators are increasingly relying upon new digital forensics tools to assist them.

Digital forensics tools are all relatively new. Up until the early 1990s, most digital investigations were conducted through live analysis, which meant examining digital media by using the device-in-question as anyone else would. As devices became more complex and packed with more information, live analysis became cumbersome and inefficient. Eventually, freeware and proprietary specialist technologies began to crop up as both hardware and software to carefully sift, extract, or observe data on a device without damaging or modifying it.

Fraud Detection: In-Depth Guide [2021 update]

With the increase in digital banking and e-commerce transactions, digital fraud has become a larger threat. There are numerous types of fraud such as account take over and new account fraud. Companies are estimated to spend >$20bn annually on fraud detection.

Since fraudsters improve their techniques over time and since number of transactions are too numerous to deal with manual controls, companies need to rely on machine learning to build resilient and efficient fraud detection systems.

Privacy Intrusiveness in Financial-Banking Fraud Detection

Abstract: Specialty literature and solutions in the market have been focusing in the last decade on collecting and aggregating significant amounts of data about transactions (and user behavior) and on refining the algorithms used to identify fraud. At the same time, legislation in the European Union has been adopted in the same direction (e.g., PSD2) in order to impose obligations on stakeholders to identify fraud. However, on the one hand, the legislation provides a high-level description of this legal obligation, and on the other hand, the solutions in the market are diversifying in terms of data collected and, especially, attempts to aggregate data in order to generate more accurate results. This leads to an issue that has not been analyzed yet deeply in specialty literature or by legislators, respectively, the privacy concerns in case of profile building and aggregation of data for fraud identification purposes and responsibility of stakeholders in the identification of frauds in the context of their obligations under data protection legislation.

This article comes as a building block in this direction of research, as it contains (i) an analysis of existing fraud detection methods and approaches, together with their impact from a data protection legislation perspective and (ii) an analysis of respondents’ views toward privacy in case of fraud identification in transactions based on a questionnaire in this respect having 425 respondents. Consequently, this article assists in bridging the gap between data protection legislation and implementation of fraud detection obligations under the law, as it provides recommendations for compliance with the latter legal obligation while also complying with data protection aspects.

Credit Card Fraud Detection: Top ML Solutions

4.9 / 5 ( 32 votes )

From the moment the payment systems came to existence, there have always been people who will find new ways to access someone’s finances illegally. This has become a major problem in the modern era, as all transactions can easily be completed online by only entering your credit card information. Even in the 2010s, many American retail website users were the victims of online transaction fraud right before two-step verification was used for shopping online. Organizations, consumers, banks, and merchants are put at risk when a data breach leads to monetary theft and ultimately the loss of customers’ loyalty along with the company’s reputation.

How to prevent financial fraud with machine learning

Financial fraud is among the leading causes of profit loss to businesses that rely on money transactions. Whether it’s a banking app or a retail company, compromised security poses an enormous threat to a business’s operations. In addition to financial damages, fraud also contributes to decreased customer loyalty and potential legal issues, particularly when it infiltrates fintech apps that are expected to always be at peak reliability.

To guard against fraud, banks and financial institutions in 2021 employ machine learning. ML-based fraud detection is an effective and cost-saving solution that can help you recognize and deflect hacking attempts before they can do any damage.

Besides, machine learning is a rapidly growing tool—a part of the global fraud detection and prevention market that’s expected to hit $40.8 billion by 2026—and it can be applied across all your services and platforms

The In-depth Guide to E-commerce Fraud Detection – Updated 2021

4.9 / 5 ( 38 votes )

It is hard to underestimate the role of Marketplaces in a world where most communications happen on the web and our virtual environment is full of advertisements with attractive products and services to buy. Meanwhile, it is obvious that many criminals are trying to take advantage of it, using scams and malware to compromise users’ data.

Fraud Detection in E-commerce and Statistics

The level of E-commerce fraud is high, according to the statistics. With E-commerce sales estimated to reach $630 billion (or more) in 2020, an estimated $16 billion will be lost because of fraud. Amazon accounts for almost a third of all E-commerce deals in the United States; Amazon’s sales numbers increase by about 15% to 20% each year. From 2018 to 2019, E-commerce spending increased by 57% — the third time in U.S. history that the money spent shopping online exceeded the amount of money spent in brick-and-mortar stores.

Fraud Detection: How Machine Learning Systems Help Reveal Scams in Fintech, Healthcare, and eCommerce

The financial services industry and the industries that involve financial transactions are suffering from fraud-related losses and damages. 2016 was a banner year for financial scammers. In the US alone, the number of customers who experienced fraud hit a record 15.4 million people, which is 16 percent higher than 2015. Fraudsters stole about $6 billion from banks in 2016. A shift to the digital space opens new channels for financial services distribution. It also created a rich environment for fraudsters.

If earlier criminals had to counterfeit client IDs, now getting a person’s account password may be all that’s needed to steal money. Customer loyalty and conversions are affected in both environments, the digital and the physical. According to Javelin Strategy & Research, it takes 40+ days to detect fraud for brick-and-mortar financial institutions. Fraud also impacts banks that provide online payments service. For instance, 20 percent of customers change their banks after experiencing scams.

How Artificial Intelligence Can Improve Your Ability To Detect Fraud

Online merchants can no longer rely on simple rule-based engines to stop fraud. Utilizing artificial intelligence is essential in protecting your business from modern fraud attempts.

The tactics used to commit card-not-present (CNP) fraud have changed dramatically in the last decade. The solutions used by merchants in the past to detect fraud attacks aren’t as effective because the patterns and methods of committing fraud are ever evolving.

Here’s the bottom line: Traditional approaches to stopping fraud that involve rule-based decisioning engines don’t offer the level of protection that’s necessary to effectively prevent fraud attacks. The tactics used to commit fraud today are more nuanced and require artificial intelligence (AI) and machine learning algorithms to identify and stop.

In this article, we’ll look at the key benefits that come with using artificial intelligence as part of your fraud prevention strategy.

Fraud and Emerging Tech: Artificial Intelligence and Machine Learning

Artificial intelligence (AI) has become one of the fastest growing priorities for many companies in the digital era and, consequently, will have a wide-ranging impact on businesses across the globe. This post, part of an emerging technology series from the Anti-Fraud Collaboration, examines the implications of AI and one of its most commonly known applications—machine learning—when it comes to mitigating fraud risk.


In most companies, fraud is generally detected only after it happens. After that, the measures are implemented for its future prevention and protection. With the upsurge of technology and rising digitization, fraud detection, and prevention, before it happens, has become quintessentially necessary. Several companies believe that fraud detection is the best way to eradicate it from the environment and prevent a recurrence.

Below are the 20 significant fraud detection and prevention software companies that offer a safe environment for businesses to work and operate.

Additional resources:

  1. Financial Crime and FRAUD Report 2021 –

  1. The State of Financial Crime in 2021 –

  1. Report: FBI Says Online Cookies Hold Keys To Preventing Financial Crimes –
  2. FINRA Orders Record Financial Penalties Against Robinhood Financial LLC


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