Harm to a companies reputation can have a significant overall financial impact. While other forms of risk are easier to identify and mitigate against, reputational risk can appear random and impossible to prevent. Recent developments in AI-powered risk intelligence are changing that and making preventing reputation increasingly possible.

Getting caught by a company engaged in law-breaking or dishonest business practices has always been a risk in any investment opportunity. With high-profile cases such as Theranos still hitting the headlines, we ask what constitutes corporate fraud, how it is different from other forms of white-collar crime, and how you can reduce the risk of being duped.

Using personal judgment to conduct due diligence may not be the optimal way to make business decisions. Many psychological studies show how our decision-making capabilities are limited due to the biases that are inherent in human nature. While the phrase ‘“go with your gut” may have positive implications, in reality, it could be fueled by “confirmation bias” — with disastrous repercussions for investors and decision-makers.

AI and machine learning have the capability to refine business practices, enabling firms to achieve their overarching goals. Due diligence is a prime example of an industry in which the application of artificial intelligence can help firms actualize their objectives of absolute transparency. By reducing natural human errors, eliminating manual inaccuracies, and automating internal operations to increase productivity, AI is transforming industry norms.

49% of respondent companies around the world disclosed that they’ve been a victim of fraud and economic crime. Considering the myriad of white-collar crimes constantly being reported in the news, the survey results are hardly shocking.

The proliferation of social media into our daily lives comes as no surprise, and yet people who love to share what they’ve eaten for breakfast with the world are apprehensive about sharing what they post on social media with their prospective employers.

While not much has principally changed in terms of employers being accountable for their employees’ actions, the future of due diligence seems to be taking some interesting twists and turns.

Globalization leads to a competitive talent search worldwide due to the borderless nature of employment, and companies have demonstrated an increase in demand to engage in the pre screening process. Protecting company reputation, making optimal business decisions, and ensuring employee and customer safety are among the top priorities for global brands looking to maintain their long term positions in the market. In 2018, for the 11th consecutive year, HireRight reported that the most popular types of background checks include employment, identity, criminal searches, and education verification.

According to StatisticBrain research institute, employee theft is a crime that costs U.S. businesses $50 billion annually. On a global scale, the Association of Certified Fraud Examiners reported that the median loss to businesses due to employee and executive fraud is $145,000- or an aggregated $3.7 trillion annually. More specifically, a study by global specialist insurer Hiscox found that the financial services sector had the highest level of losses due to employment fraud across all industries. In 2016 losses were over $120 million. How can these alarming figures be avoided to prevent future devastating losses to businesses?

Although the due diligence and background check sectors seem to be particularly straightforward in operations (find out all you can about a candidate, use that information to make a go/no go hire or investment decision), there are many more intricacies and complexities than meet the eye. With the continuous dynamic changes in political, technological, legal, cultural, and essentially all realms of society, information overload is inescapable.

Selecting a new employee to join a company is no easy feat for a hiring manager. In this overly pressurized, highly competitive marketplace, managers are under intense scrutiny to ensure that they hire top talent individuals from the marketplace. Moreover, managers in the U.S. face a network of intricate and overwhelming complexities when it comes to hiring laws and regulations. While it may be called the United States (emphasis on the United), most states are quite divided about their policies toward conducting background checks and pre screening hiring procedures. Let’s examine some of those differences and see how they affect the due diligence process.

Artificial Intelligence is unarguably changing your life whether you’re aware of it or not. Health care, cyber security, energy, finance, and tech are only a few of the infinite numbers of industries and ways in which AI is transforming society as we know it.

Although the Gig Economy is not a new concept in the U.S., it has seen some significant growth in the last few years, not surprisingly due to technological developments. According to a 2015 study by the American Action Forum, the number of workers in the gig economy grew from 8.8 percent in 2002 to 14.4 percent in 2014.

Furthermore, independent contract workers grew by 2.1 million people from 2010 to 2014. The growth of the gig economy is sure to have first hand effects on the employment sector in more than one way.

Facebook, Amazon, Wells Fargo. These are companies that you would expect hire only the best of the best. But while their brand names may carry elite associations, their compliance practices are anything but superior. In the wake of corporate scandals, the list of fraudulent activities never seems to end—and some of the top Fortune 500 companies are no exception to the rule. Over the past ten years, employers and screening firms have had to pay more than $325 million to settle background check (or lack thereof) lawsuits. In this new social era of transparency, there seems to be conflicting interests between corporate trust and corporate unjust.

Artificial intelligence has been used across a diverse set of industries for a plethora of purposes. One example is the use of AI and machine learning to perform background check services on employees, investors, and companies.

Recent trends show that it’s easier than ever to create fictional companies and returns, among other collaborating materials. In fact, a few months ago the SEC produced a fraudulent site to highlight just how easy it is to present the illusion of an up-and-coming, promising investment.
It is for that reason, among others, that we argue the uptick in fraud is correlated with the proliferation of data. And it’s directly affecting your due diligence.

Big Data, artificial intelligence, machine learning and the list of buzzwords continues. It almost seems like today, investors have a fiduciary duty to prescreen new investments, hires, and incumbent employees- and in some cases they actually do. But how does this concept translate in practice? How is technology really used to help investors navigate the world of business decision making? Here, we take a closer look into how investors and financial professionals use these tools for optimal practices.

According to a study conducted by CareerBuilder, 58% of resumes consist of misleading or false information such as incorrect education details and inaccurate job titles and seniority levels. More shockingly, a survey by HireRight found that 85% of resumes contained false statements. If these numbers seem to be so high why have executive resume scandals been so prevalent, and even seem to be increasing, over the past decades?