In the era of globalized markets, burgeoning international trade, complex financial systems, ever-evolving compliance and regulatory landscapes, and rapid technology advancement, white-collar crime has unfortunately seen a significant uptick in scale, variety, and sophistication.

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Whereas white-collar crime used to conjure images of high-flying executives stealing from company coffers, the modern landscape is much more complex, encompassing misconduct of all shapes and sizes, such as international bribery and corruption, sophisticated money laundering, health care fraud, complex accounting and financial reporting fraud, securities trading schemes, and cybercrime, to name but a few.

Today’s white-collar criminals are smarter and more technology-savvy, often exploiting complex and siloed systems and circumventing often archaic fraud- and compliance-monitoring solutions used by corporations and government entities. And while bad actors are effectively leveraging the massive swathes of data to their advantage in obfuscating investigators and avoiding detection, organizations are struggling to store, manage, and utilize data effectively to investigate and prevent compliance issues, fraud, waste, and abuse.

As an added challenge, regulators have raised the bar and expect corporations to employ data-driven methods to tackle white-collar crime.

In Dataversity, Paresh Chiney outlines how data science and big data analytics offer a plethora of solutions and techniques to prevent, detect, investigate, and remediate white-collar crime.

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Paresh Chiney

Paresh Chiney, a Partner with StoneTurn, brings more than 17 years of experience in the application of technology and data analytics to deliver business value. He works with clients—to solve […]

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