From many, comes one (algorithm)

Recent anticorruption research shows that when companies collaborate to share information about third-party payments and high-risk transactions, they have a 25% greater chance of predicting improper payments than when each company’s model is performed in isolation. A new data-sharing consortium led by a nonprofit at MIT is working to make such collaboration possible.

According to the World Economic Forum, international corruption can take many forms, including bribery, embezzlement, cronyism, and fraud. And it’s an expensive problem that costs the global economy trillions of dollars annually. (See “Corruption is costing the global economy $3.6 trillion dollars every year,” by Stephen Johnson, World Economic Forum, Dec. 13, 2018.) Detecting it is challenging but possible using advanced analytics and machine learning technologies on top of legal and subject-matter expertise. And, when organizations work together in collaboration, it’s now proven the results are even better. Here we look at how a consortium led by a nonprofit at MIT is helping organizations share data without comprising privacy in their fight against fraud.