Dr. Mohd Raagib Shakeel serves as an
Assistant Professor in Business Analytics, backed by a rigorous academic
foundation and over seven years of corporate experience across internal
auditing, credit risk assessment, compliance auditing (mortgage),
strategic planning, and software testing. He holds a Ph.D. in Finance
from Jamia Millia Islamia, an MBA in Finance, and a B.Tech in Computers,
and is also UGC-NET qualified. At Lloyd Business School, he teaches
courses across BBA (Business Analytics), MBA, PGDM (Business Analytics)
programs. His teaching responsibilities include Business Analytics &
Data Science, Econometrics and Time Series Forecasting, Data Exploration
& Visualization with Python, Database Management Systems, and advanced
courses in Quantitative Finance and Credit Risk Modelling. Earlier, he
taught at DSEU (BPIBS) as Guest Faculty, where he engaged with students
from B.Tech (Data Science), B. Sc (Data Analytics), and MBA (Data
Analytics).
Dr. Shakeel’s research focuses on quantitative finance, risk analytics,
and machine-learning-driven financial forecasting. His publication
record spans SCI, SSCI, and Scopus-indexed journals and edited volumes,
covering themes such as NAV prediction using deep residual models,
ML-based corporate bankruptcy feature selection, MENA Sukuk price
prediction, a corporate governance case on the PMC Bank failure, and
volatility modelling using explainable AI. His latest paper, “Leveraging
Explainable AI, GRU and VAR Based Framework to Uncover Relationships
Between Endogenous and Exogenous Volatility Indices for Effective
Trading Strategies,” was published in Computational Economics, adding to
his contributions at the intersection of econometrics, AI, and financial
market dynamics.
His analytical expertise extends across Python, R, MySQL, Tableau,
machine learning, deep learning, econometric modelling, financial time
series, Monte Carlo simulation, VaR modelling, derivatives pricing, and
quantitative risk forecasting. He has hands-on experience with
frameworks such as Black–Scholes, CAPM, Brownian motion–based models,
and simulation-driven risk engines. He has delivered institutional
workshops such as the “AI Empowering Pharma Research & Analytics”
pre-conference session and the BBA Business Analytics Workshop Series on
Python-based project development and Agentic-AI automation—helping
learners connect theoretical concepts with practical, industry-ready
analytics workflows.
Prior to academia, Dr. Shakeel worked across diverse corporate
domains—internal auditing, mortgage compliance, supply chain
coordination, strategic planning, and IT testing—at organizations such
as Ethical Circle Advisory Pvt Ltd (CA Firm), Quattro Mortgage Solutions
(Onsite client - Bank of America), Havells India, Esquare Technologies,
and Trisoft Systems. This multifaceted industry experience equips him to
blend analytical rigor with real-world business insights in the
classroom.
Research Interests: Quantitative Finance, Financial Econometrics,
Volatility Forecasting, Risk Analytics, Credit Risk Modelling,
Algorithmic Trading, Deep Learning for Financial Markets, Explainable
AI, Time Series Analysis, and Data-Driven Decision Support Systems.
