India Inc, individuals could soon get to settle fund diversion cases
India's Securities and Exchange Board of India (Sebi) is considering a novel approach to address fund siphoning cases. Offenders would return misappropriated funds along with interest, while also disclosing relevant details. This initiative aims to create a more realistic enforcement structure. The proposals, including a new mutual fund-specific category for portfolio management services, will be…
Mumbai: India's securities regulator, Sebi, aims to allow accused individuals and companies accused of diverting funds from listed companies to settle their cases. This avenue is currently unavailable to them. Sebi's board is set to meet on September 24 to discuss both proposals. The settlement process would require the accused party to return the misappropriated funds along with interest and disclose the matter to investors.
Sebi believes that an offender who returns the funds with interest changes the dynamics of the case, making it eligible for settlement. Pratap Venugopal, a senior advocate at the Supreme Court, stated that settlement is not an endorsement of leniency. However, cases with widespread investor harm or concerns about market integrity could be resolved more effectively through settlement, backed by restitution, disgorgement, and corrective actions.
In FY2026, Sebi received 439 settlement applications, approved 170, and rejected or withdrew 199. A study conducted by Sebi showed that the proposed settlement amounts were, on average, eight times higher than the penalties paid in such cases in the past two years. Sebi's revenue from settlement and compounding charges rose eightfold to ₹815 crore in FY25 from ₹104 crore in FY24.
Sandeep Parekh, a managing partner at Finsec Law Advisors, believes that the new formula of settling before a show-cause notice and during the appellate stage should significantly alter the calculation for accused parties, encouraging them to settle.
Written by urgent.news from The Economic Times - Top News's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
