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- Founder and chairman of Satyam Computer Services Ltd., B. Ramalinga Raju, admitted to a large accounting scam on January 7, 2009, causing great disruption in India’s business sector. The Satyam Scam, also known as ‘India’s Enron,’ included the fraudulent alteration of the company’s accounts by $1.47 billion.
lawfullegal.in/the-satyam-scam-unravelling-indias-largest-corporate-fraud/The Satyam Scam: Unravelling India’s Largest Corporate Fraud
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Raju was embroiled in one of the biggest corporate fraud scams involving Satyam Computer Services Limited, the company that he left in 2000. The Securities and Exchange Board of India (SEBI), India’s market regulator held Raju guilty of insider trading and making “unlawful gains”.
2 days ago · Impact: Satyam's scandal severely damaged investor confidence in India's IT sector and led to reforms in corporate governance and auditing standards in the country. 4. Lehman Brothers: Background: Lehman Brothers, a global financial services firm, filed for bankruptcy in 2008 during the financial crisis.
4 days ago · the case was strikingly similar to Maranello Rosso Ltd v Lohomij BV and Ors [2022] EWCA Civ 1667, which made it clear that the decision in Satyam Computer Services v Upaid Systems [2008] EWCA Civ 487 “should not be read as support (even obiter) for the proposition that express words are always, or even generally, required to release a claim in fraud” (see [74]);
4 days ago · He is blaming coaching institutes, but he himself is the biggest fraud by claiming 750 interview calls from his SFG group. He has completely copied all the things from forum and is showing himself to be a saviour to innocent students who instead of studying are misguided by him.
On Friday, cybersecurity company CrowdStrike pushed a faulty software update that bricked thousands of Microsoft Windows computers across the world and brought many services to a screeching halt.
1 day ago · The model predicted Fraud, and this prediction supported by an even positive and greater SHAP value. All those features that have positive SHAP values have led to a positive contribution to the model’s prediction of a non-fraudulent transaction, while features having negative SHAP values try to decrease the model prediction of non-fraudulent transactions.
