Krishan Kumar Sharma
Citigroup Inc.
Krishan Kumar Sharma is a senior model risk management and data analytics leader with nearly two decades of experience developing, validating, and governing regulatory-compliant finance, credit, and market risk models for global financial institutions. His work spans AI/ML-driven finance & risk modeling, stress testing, capital planning, credit loss forecasting, and financial risk management across banking and trading books, with direct applicability to systemically important financial institutions.
Mr. Sharma has led and mentored high-performing, geographically distributed teams of quantitative and risk professionals, advancing robust model risk governance frameworks and ensuring alignment with evolving regulatory expectations. He has played a pivotal role in the design, validation, and oversight of models meeting CCAR, CECL, IFRS 9, and ICAAP standards for U.S. and international regulators. His contributions extend to the development of innovative governance approaches for emerging technologies, including generative AI, at the intersection of advanced analytics and regulatory compliance.
Currently, Mr. Sharma serves as Senior Vice President at Citigroup in New York, where he leads cross-regional initiatives translating complex regulatory and business requirements into scalable analytical frameworks. He oversees the end-to-end development and independent validation of finance risk PPNR models—encompassing balance sheet, P&L, and capital—as well as credit risk loss-forecasting models for global banking and trading portfolios. He also plays a key role in addressing MRA/MRIA and consent order findings through direct engagement with U.S. and international regulators, including the Federal Reserve Board, OCC, PRA, and ECB.
Mr. Sharma holds an MBA in Finance and a Bachelor of Engineering in Information Technology.
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Articles by Krishan Kumar Sharma
A global governance framework for generative artificial intelligence in financial risk management: empirical insights on mitigating hallucination and opacity in the augmented intelligence era
The author proposes a six-pillar governance framework for generative-AI applications in financial risk management.
A dual backtesting framework for quantifying nested model error and unlocking capital efficiency
The author puts forward a framework for dual backtesting, in which single-blind backtesting assesses core models and double-blind backtesting evaluates the whole system.
Validating bank risk models under trade war stress: a framework for adaptive stress testing with AI-driven calibration and cross-industry applications
Focusing on validating and enhancing risk models, the author proposes a comprehensive framework through which to stress test under trade war conditions.
Rethinking model validation for GenAI governance
A US model risk leader outlines how banks can recalibrate existing supervisory standards