Since Russia’s invasion of Ukraine, there’s been a global surge of generosity toward Ukrainians. How can you get involved in humanitarian relief efforts? According to IU Professor Beth Gazley, there are some steps you can take to ensure your money is going to reputable organizations that do the most good. First, she says to do your research and verify that crowdfunding and social media fundraising campaigns are legitimate. You can even check the names of organizations with the Internal Revenue Service’s list of registered charities. Gazley also recommends staying informed about the situation on the ground to determine where the most aid is needed. Right now, with refugees headed mostly west, she says that’s where the need may be greatest. Giving to well-established charities in eastern Europe will also help your donation go further, she says, because they will have already built local relationships, trust and infrastructure. As always, cash is king, so giving cash over goods is always best, she says. Be mindful of charity rating sites, as many still rely on outdated assumptions and may not be as accurate as they seem. You also might consider supporting advocacy and independent media organizations that are working to address Russian disinformation campaigns. And finally, Gazley says to ask the experts you know. If you know people from Ukraine, ask them which charities they would recommend you support.
In other news, cases of corporate fraud continue to make the headlines. A model known as the M-Score, developed in the late 1990s by Kelley School of Business Professor M. Daniel Beneish, remains the most viable means of predicting corporate financial statement fraud, despite the proliferation of such models in the years since. Beneish recently co-authored a paper comparing seven fraud prediction models. The study weighed the benefits of correctly anticipating fraud against the costs incurred by identifying firms as fraudulent – when in fact, they were not. Even though newer fraud models nearly doubled the success rate of M-Score, they did so at the cost of a much larger number of false positives. Each positive result must be carefully investigated, including the false positives, and this is costly. Beneish’s comparison found that the other models are not used in practice by auditors because of the false positive problem. This study is important, he said, because measures commonly used in recent research to justify new models are misleading. The proportion of fraudulent firms in the population being studied is often very small, and researchers typically assume that the cost of a false positive and a missed detection are equal, he says, when they are not.