Welcome!
I’m Ratanlal Mahanta, a dedicated researcher and lead scientist specializing in Computational Finance and AI-driven Quantitative Research.
Currently, I work on pioneering projects that integrate machine learning, stochastic modeling, and scenario simulations to push the boundaries of finance. My expertise spans various fields, including option pricing models, credit valuation adjustments (CVA), climate risk in finance, and tail risk hedging through scenario simulations. I have an extensive background in Bayesian machine learning, quantitative trading strategies, and dynamic hedging.
My career bridges the depth of quantitative development and the rigor of model validation. I’ve led validations of high-impact models including CVA, IRRBB, FRTB-SA, repo VaR, and various internal capital engines. I work closely with regulatory stakeholders and internal audit teams to establish robust governance frameworks, helping financial institutions meet both compliance and innovation goals.
With over 330 citations and an h-index of 10, my work is recognized in global conferences such as IRMC 2024, IMRC Milan, and the Odisha AI Conference 2024. My contributions span cutting-edge research and enterprise-grade implementation, from Monte Carlo CVA engines and deep hedging via reinforcement learning to fire-sale repo haircut stress testing and wrong-way risk modeling via copulas.
I’ve built full validation stacks, dashboards, and regulatory submission toolkits that integrate Python, Excel, and Streamlit. My strategic focus includes aligning AI-powered quant models with capital planning, scenario design, NMRF assessments, and ESG-aligned stress frameworks. With a strong foundation in startup culture and product innovation, I combine agility and technical depth to solve frontier problems in financial risk and derivatives modeling.
Here, I aim to share insights into my evolving projects—from quantitative alpha strategy development to green finance risk engines—while showcasing how AI, regulation, and quantitative finance are converging to reshape global markets.
Highlights
- Quantitative Expertise: Option pricing, credit valuation, climate risk, and stochastic volatility modeling.
- Risk Management Focus: Scenario simulation for tail risk hedging and dynamic hedging.
- Model Validation Leadership: CVA Greeks, repo VaR, FRTB-SA, IRRBB, and deep audit tooling.
- Innovative Research: AI/ML-driven finance with reinforcement learning and Bayesian inference models.
- Regulatory Readiness: Alignment with ECB, CBUAE, PRA and audit-driven stress scenario platforms.
- Recognized Scholar: Publications, citations, and invited talks at leading finance and AI forums.
- System Development: Streamlit/Excel dashboards, quant API stacks, and live scenario simulators.
- Thought Leadership: Driving the frontier in capital modeling, margin optimization, and AI governance.