By MS Sahoo & Sumit Agrawal, the authors are legal practitioners. They earlier worked for Sebi
In 2024-25, the Securities and Exchange Board of India (Sebi) received a record 703 consent applications, marginally higher than the 600-plus applications filed annually between 2007 and 2010. This stands out against the manifold rise in investors and intermediaries, sharp growth in transaction volumes and values, addition of new markets (commodities) to Sebi’s fold, and the resulting surge in enforcement actions. The flat trend signals that the market may not be viewing the consent process as a sufficiently predictable enforcement tool. During the year, Sebi disposed of 556 applications, accepting 284 and rejecting 272, a near-even split with an acceptance rate of 51% and a rejection rate of 49%. Cumulatively, since inception, Sebi has accepted 2,713 applications and rejected 2,808, almost evenly balanced at 49% versus 51%. This 50:50 symmetry suggests that negotiations between Sebi and applicants are finely balanced. Neither side enjoys overwhelming bargaining power: if Sebi presses too hard, applicants may opt for litigation; if applicants resist too much, Sebi may reject the application.
A deeper look, however, reveals a different story. Year-to-year acceptance rates have swung from as low as 25% in 2012-13 to as high as 82% in 2016-17, without a clear trend. Such swings undermine predictability, which likely explains why consent applications have not kept pace with the market’s growth. Since applicants are numerous and diverse and act independently, predictability seems to depend less on their conduct and more on Sebi’s approach in a year.
Two factors largely determine predictability: proportionality and transparency. Proportionality requires that settlement terms reflect the nature and gravity of the contravention, considering intent, scale of impact, and the benefits derived. A proportionate approach ensures relatively minor contraventions are resolved on lighter terms, while serious breaches invite onerous settlements. This not only aligns settlement outcomes with culpability but also promotes fairness, consistency, and deterrence. Where proportionality is applied unevenly, say, if minor infractions are rejected outright while significant ones are settled on lenient terms, fairness is compromised, and acceptance rates swing sharply.
Equally important is transparency. When settlement norms, evaluation criteria, and guiding principles are clearly articulated, publicly explained, and consistently applied, applicants and market participants can anticipate outcomes with greater confidence. Conversely, where the basis for decisions is not sufficiently visible or well-understood, participants may perceive outcomes as unpredictable, even if each decision is individually reasoned.
Sebi pioneered the settlement mechanism in India in 2007, even before statutes formally backed it in 2014, to create an efficient and expeditious, non-adversarial means of resolving enforcement proceedings. The objective was simple: provide a structured path to closure that lightens the burden on the regulator, markets, and courts, while fully preserving the deterrence. A well-functioning settlement mechanism achieves in weeks or months what a trial might take decades to accomplish, with the added risk of the delinquent walking free on technical grounds after expensive and prolonged litigation. Importantly, settlement closes only after full compliance with agreed terms, whereas enforcement actions decided on merits may languish at the stage of implementation.
The mechanism derives settlement terms from a formula anchored in a “base amount” (BA), defined as the higher of (i) illegal profits made plus loss caused to investors, or (ii) a value specified in regulatory tables. While conceptually sound, this formula falters in practice.
Profit and loss data are rarely available, forcing reliance on tabular values that fail to reflect the gravity of defaults. This produces anomalies: a contravention yielding an unlawful gain of Rs 1 crore attracts the same BA as one yielding only Rs 1. The tables prescribe uniform amounts irrespective of scale or impact of the violation. A failure to disclose a change in shareholding, for example, draws the same BA whether the company in question has a lakh shareholders or merely a hundred.
Worse still, most contraventions are pushed into a “residuary” table. This table lists a few contraventions before sweeping the rest into a catch-all “residuary” category. Consequently, most contraventions are settled using the table values assigned to the residuary contraventions under the residuary table, which barely accounts for the seriousness of misconduct. Such distortions undermine fairness, diminish deterrence, and risk eroding confidence in settlements.
Opacity compounds these distortions. For instance, the regulations empower Sebi to refuse settlement where defaults affect “market integrity” or have a “market-wide impact”. Yet, neither expression is articulated, leaving applicants, intermediaries, and professionals uncertain about what may be settled and what cannot. Similarly, the regulations offer no clarity on which contraventions may be resolved through monetary terms alone, which require non-monetary commitments, and which warrant a blend of both.
Settlement orders often lack essential details. Consider the contrast: following the Satyam scandal of 2009, the US regulators concluded proceedings by 2011 (Indian proceedings yet to conclude), imposing $7.5 million in penalties, censures, ongoing monitoring obligations, and far-reaching audit reforms on PwC, Satyam’s auditors. The Securities and Exchange Commission’s settlement order runs to over 16,000 words, rich in reasoning, factual context, and explanation. The level of detail not only justified the outcome but also created a road map for the market and future cases. In India, many consent orders are terse, leaving applicants and practitioners with limited insight into how terms were derived or why certain choices were made.
The regulations do prescribe a formula for determining settlement amounts. But an excessive reliance on formulae may obscure crucial considerations. The formula, for instance, does not factor in the strength of evidence, impacting the probability of conviction. If the formula indicates a settlement of `1 crore but the likelihood of conviction is only 10%, no rational applicant would settle at that amount; they might instead contest proceedings. This misalignment skews outcomes: cases backed by strong evidence are more likely to settle, while weaker cases drag through prolonged adjudication.
For the consent mechanism to remain a credible enforcement tool, it must embed and visibly uphold both proportionality and transparency. Together, these principles enhance predictability, strengthen trust, and reinforce regulatory legitimacy. Over time, their consistent application will build a body of jurisprudence that guides applicants, practitioners, and the regulator alike, toward fairer, faster, and more predictable enforcement outcomes.