RBI GRADE B OFFICER (DR)-GENERAL- 2026 Phase I Exam Syllabus
Sections | Topics |
|---|
Data Interpretation & Analysis | Tabular Graph, Line Graph, Pie Chart, Bar Graph, Radar Graph, Caselet, Missing Case DI, Let it Case DI, Data Sufficiency, Probability, Permutation & Combination, Averages, Ratio, Percentages, Mixture & Allegation, Speed, Distance & Time |
Reasoning | Verbal Reasoning, Syllogism, Circular Seating Arrangement, Linear Seating Arrangement, Double Lineup, Scheduling, Input-Output, Blood Relations, Directions & Distances, Ordering & Ranking, Coding & Decoding, Code Inequalities, Course of Action, Critical Reasoning, Analytical and Decision Making |
English Language | Reading Comprehension, Grammar, Vocabulary, Verbal Ability, Sentence Improvement, Para Jumbles, Word Association, Cloze Test, Error Spotting, Fill in the Blanks |
General/Economy/Banking Awareness | Financial Awareness, Current Affairs, General Knowledge, Static Awareness, Government Schemes, Agreements & Deals, Banking Terms, Banking Rates & Processes |
RBI GRADE B OFFICER (DR)-GENERAL- 2026 (Phase-II) Exam Syllabus
Paper-I - Economic and Social Issues (ESI):
Growth and Development – Measurement of growth: National Income and per capita income – Poverty Alleviation and Employment Generation in India – Sustainable Development and Environmental issues.
Indian Economy – Economic History of India - Changes in Industrial and Labour Policy, Monetary and Fiscal Policy since reforms of 1991 Priorities and recommendations of Economic Survey and Union Budget – Indian Money and Financial Markets: Linkages with the economy – Role of Indian banks and Reserve Bank in the development process - Public Finance - Political Economy - Industrial Developments in India- Indian Agriculture - Services sector in India.
Globalization Opening up of the Indian Economy – Balance of Payments, Export-Import Policy – International Economic Institutions IMF and World Bank – WTO – Regional Economic Cooperation; International Economic Issues.
Social Structure in India Multiculturalism – Demographic Trends – Urbanisation and Migration – Gender Issues – Social Justice: Positive Discrimination in favour of the under privileged – Social Movements – Indian Political System – Human Development – Social Sectors in India, Health and Education. Suggested reference material.
Paper-II -English (Writing Skills):
The paper on English shall be framed in a manner to assess the writing skills including expression and understanding of the topic.
Paper-III - General Finance and Management
a) Financial System
1. Structure and Functions of Financial Institutions
2. Functions of Reserve Bank of India
3. Banking System in India – Structure and Developments, Financial Institutions – SIDBI, EXIM Bank, NABARD, NHB, NaBFID etc.
4. Recent Developments in Global Financial System and its impact on Indian Financial System
5. Role of Information Technology in Banking and Finance
6. Non-Banking System
7. Developments in Digital Payments
b) Financial Markets
Primary and Secondary Markets (Forex, Money, Bond, Equity, etc.), functions, instruments, recent developments.
c) General Topics
1. Risk Management in Banking Sector
2. Basics of Derivatives
3. Global financial markets and International Banking – broad trends and latest developments.
4. Financial Inclusion
5. Alternate source of finance, private and social cost-benefit, Public-Private Partnership
6. Corporate Governance in Banking Sector, role of e-governance in addressing issues of corruption and inefficiency in the government sector.
7. The Union Budget – Concepts, approach and broad trends
8. Basics of Accounting and Financial Statements - Balance Sheet, Profit and Loss, Cash Flow Statements, Ratio Analysis (such as Debt to Equity, Debtor Days, Creditor Days, Inventory Turnover, Return on Assets, Return on Equity, etc.)
9. Inflation: Definition, trends, estimates, consequences and remedies (control): WPI- CPI - components and trends; striking a balance between inflation and growth through monetary and fiscal policies
d) Management:
1. Fundamentals of Management & Organizational Behaviour:
Introduction to management; Evolution of management thought: Scientific, Administrative, Human Relations and Systems approach to management; Management functions and Managerial roles; Nudge theory
Meaning & concept of organizational behaviour; Personality:
Meaning, factors affecting personality, Big five model of personality; concept of reinforcement; Perception: concept, perceptual errors.
Motivation: Concept, importance, Content theories (Maslow’s need theory, Alderfers’ ERG theory, McCllelands’ theory of needs,
Herzberg’s two factor theory) & Process theories (Adams equity theory, Vrooms expectancy theory).
Leadership:
Concept, Theories (Trait, Behavioural, Contingency, Charismatic, Transactional and Transformational Leadership; Emotional Intelligence: Concept, Importance, Dimensions. Analysis of Interpersonal Relationship: Transactional Analysis, Johari Window; Conflict: Concept, Sources, Types, Management of Conflict; Organizational Change: Concept, Kurt Lewin Theory of Change; Organizational Development (OD): Organisational Change, Strategies for Change, Theories of Planned Change (Lewin’s change model, Action research model, Positive model).
2. Ethics at the Workplace and Corporate Governance Meaning of ethics, why ethical problems occur in business. Theories of ethics: Utilitarianism: weighing social cost and benefits, Rights and duties, Justice and fairness, ethics of care, integrating utility, rights, justice and caring, An alternative to moral principles: virtue ethics, teleological theories, egoism theory, relativism theory, Moral issues in business: Ethics in Compliance, Finance, Human Resources, Marketing, etc. Ethical Principles in Business: introduction, Organization Structure and Ethics, Role of Board of Directors, Best Practices in Ethics Programme, Code of Ethics, Code of Conduct, etc.
Corporate Governance: Factors affecting Corporate Governance; Mechanisms of Corporate Governance.
Communication: Steps in the Communication Process; Communication Channels; Oral versus Written Communication; Verbal versus non-verbal Communication; upward, downward and lateral communication; Barriers to Communication, Role of Information Technology.
# The suggested reference materials are "indicative" only.
OFFICERS IN GR. ‘B’ (DR) – DEPR and DSIM Cadres
Officers in Grade 'B' (DR) – DEPR- PY 2026 - Syllabus
Phase-I: Paper-I - Objective Type (on Economics)
(1) Microeconomics (Theories of consumer’s demand; Production; Market Structures and Pricing; Distribution; and Welfare Economics)
(2) Macroeconomics (Theories of Employment, Output and Inflation; Monetary Economics; ISLM Model; Schools of Economic Thought)
(3) International Economics (Theories of International Trade; Balance of Payments; Exchange Rate Models)
(4) Theories of Economic Growth and Development (Classical neo-classical approaches to economic growth and major theories of economic development)
(5) Public Finance (Theories of taxation and public expenditure and Public Debt Management)
(6) Environmental Economics (Green GDP, Environmental Valuation, Environmental policy instruments)
(7) Quantitative Methods in Economics (Mathematical and Statistical Methods for Economics, Ordinary Least Square Regression)
(8) Current developments in Indian Economy (Growth, inflation, poverty, unemployment, financial sector developments, external sector developments, fiscal developments, agriculture, industry, infrastructure, and services)
Phase- I: Paper-II - Descriptive Type (on English)
The paper on English shall be framed in a manner to assess the writing skills including expression and understanding of the topic.
Phase– II: Paper - I - Descriptive Type (on Economics) (Question paper displayed on computer, answers to be written on paper
Microeconomics Module
• Consumer Theory: Cardinal and Marginal Utility Analysis, Consumer Surplus, Indifference Curve Analysis, Price, Income and Substitution Effects, Game Theory
• Production Theory: Forms of Production function; Laws of Returns to Scale; Partial Equilibrium Vs General Equilibrium Analysis
• Market Theory: Pricing under different market structures
• Distribution Theories: Ricardo, Marx, Kalecki and Kaldor
• Welfare Economics: Pareto Optimality, Schools of Welfare Thought including Arrow, Coase and Sen
Macroeconomics Module
• National Income Accounting: Various methods for measurement of National Income
• Theory of employment and Output: Classical and Neo-classical approaches, Keynesian theory of Employment and output, Post-Keynesian developments, Business Cycles
• Inflation: Types of Inflation, Philip's curve, Taylor’s Rule, Lucas Critique
• Money and Banking: Quantity theory of Money, Neutrality of money, IS - LM Model and AD-AS Models, Money Multiplier, Monetary Policy – Scope, Objectives and instruments, Inflation targeting
• Theories of Economic Growth and Development: Theories of growth, Classical and neoclassical approaches, Theories of Economic Development
• International Trade and Balance of payments: Theories of international trade, Determination of exchange rates, Impossible Trinity
• Public Finance: Theories of taxation, Theories of public expenditure, Theories of public debt management (Equal weightage will be given to Microeconomic and Macroeconomic modules)
Phase– II: Paper-II - Descriptive Type (on Economics) (Question paper displayed on computer, answers to be written on paper)
Module on Quantitative Methods in Economics
• Mathematical Methods in Economics: Differentiation and Integration, Optimisation, Sets, Matrices, Linear algebra and Linear programming
• Statistical Methods in Economics: Measures of central tendency and dispersions, Probability, Time series, Index numbers.
• Econometrics and advanced Applications: Regression analysis, Panel data econometrics, Time Series econometrics, Basics of Bayesian Econometrics, Basic application of Artificial Intelligence/ Machine Learning
Module on Indian Economy – Policy and Trends
• Fiscal policy in India: Evolution, scope and limitations, current trends
• Monetary Policy in India: Evolution, Functions of the Reserve Bank of India, MonetaryFiscal coordination, Inflation targeting, Operating framework of Monetary Policy, Current trends
• Banking and financial sector development in India: Banks and other constituents of Indian financial markets and related developments, Current trends
• Inflation in India: Trends and drivers
• External sector developments in India: Exchange rate management, external debt, Balance of payments, Current trends
• Sectoral and other developments in India: Agriculture, industry, services and social sector-related developments
(Equal weightage will be given to Quantitative Economics and Indian Economy-related modules)
Officers in Grade 'B' (DR) – DSIM- PY 2026 - Syllabus
Paper - I and Paper – II
(i) Theory of Probability, Probability Distributions and Sampling Theory,
(ii) Linear Models and Economic Statistics,
(iii) Statistical Inference: Estimation, Testing of Hypothesis and Non-Parametric Test,
(iv) Stochastic Processes,
(v) Multivariate Analysis,
(vi) Econometrics and Time Series,
(vii) Optimization and Statistical Computing;
(viii) Data Science, Artificial Intelligence and Machine Learning Techniques,
(ix) Database and Data Warehouse Management
Detailed Syllabus
1. Theory of Probability, Probability Distributions and Sampling Theory
• Classical and axiomatic approach of probability and its properties, Bayes Theorem and its application, strong and weak laws of large numbers, characteristic functions, central limit theorem, probability inequalities.
• Standard probability distributions - Binomial, Poison, Geometric, Negative binomial, Uniform, Normal, exponential, Logistic, Log-normal, Beta, Gamma, Weibull, Bivariate normal etc.
• Exact Sampling distributions - Chi-square, student’s t, F and Z distributions and their applications. Asymptotic sampling distributions and large sample tests, association, and analysis of contingency tables.
• Standard sampling methods such as simple random sampling, Stratified random sampling, Systematic sampling, Cluster sampling, Two stage sampling, Probability proportional to size etc. Ratio estimation, Regression estimation, non-sampling errors and problem of non-response.
2. Linear Models and Economic Statistics
• Linear algebra - Vector, matrices, spanning of vector space, matrix algebra, inverse of partitioned matrices, g-inverse, orthogonal matrices, properties of idempotent matrices, characteristic roots and vectors, Cayley-Hamilton theorem, quadratic forms, definite, semidefinite and indefinite forms, simultaneous reduction of two quadratic forms, properties of similar matrices.
• Simple linear regression - assumptions, estimation, and inference diagnostic checks;polynomial regression, transformations on Y or X (Box-Cox, square root, log etc.), method of weighted least squares, inverse regression. Multiple regression - Standard Gauss Markov setup, least squares estimation and related properties, regression analysis with correlated observations. Simultaneous estimation of linear parametric functions, Testing of hypotheses; Confidence intervals and regions; Multicollinearity and shrinkage models (ridge regression, LASSO, Elastic Net) model selection criteria, residual diagnostics, categorical data analysis using dummy variables; Outlier detection and treatment.
• Definition and construction of index numbers, Standard index numbers; Conversion of chain base index to fixed base and vice-versa; base shifting, splicing and deflating of index numbers; Measurement of economic inequality: Gini's coefficient, Lorenz curves etc. Basics of macroeconomics and national accounts.polynomial regression, transformations on Y or X (Box-Cox, square root, log etc.), method of weighted least squares, inverse regression. Multiple regression - Standard Gauss Markov setup, least squares estimation and related properties, regression analysis with correlated observations. Simultaneous estimation of linear parametric functions, Testing of hypotheses; Confidence intervals and regions; Multicollinearity and shrinkage models (ridge regression, LASSO, Elastic Net) model selection criteria, residual diagnostics, categorical data analysis using dummy variables; Outlier detection and treatment.
• Definition and construction of index numbers, Standard index numbers; Conversion of chain base index to fixed base and vice-versa; base shifting, splicing and deflating of index numbers; Measurement of economic inequality: Gini's coefficient, Lorenz curves etc. Basics of macroeconomics and national accounts.
3. Statistical Inference: Estimation, Testing of Hypothesis and Non-Parametric Test
Estimation
• Concepts of estimation, unbiasedness, sufficiency, consistency, and efficiency. Factorization theorem. Complete statistic, Minimum variance unbiased estimator (MVUE), Rao-Blackwell and Lehmann-Scheffe theorems and their applications. Cramer-Rao inequality.
Methods of Estimation
• Method of moments, method of maximum likelihood estimation, method of least square, method of minimum Chi-square, basic idea of Bayes estimators.
Principles of Test of Significance
• Type-I and Type-II errors (False-positive and False-negative errors), critical region, level of significance, size, p value & its interpretation and power, best critical region, most powerful test, uniformly most powerful test, Neyman Pearson theory of testing of hypothesis. Likelihood ratio tests, Tests of goodness of fit. Bartlett's test for homogeneity of variances.
Non-Parametric Test
• The Kolmogorov-Smirnov test, Sign test, Wilcoxon Signed-rank test, Wilcoxon Rank-Sum test, Mann Whitney U-test, Kruskal-Walls one way ANOVA test, Friedman’s test, Kendall’s Tau coefficient, Spearman’s coefficient of rank correlation.
• Distribution of order statistics, distribution fitting, kernel density estimation.
4. Stochastic Processes
Poisson process
• Arrival, interarrival and conditional arrival distributions. Non-homogeneous Processes. Law of Rare Events and Poisson Process. Compound Poisson Processes.
Markov Chains
• Transition probability matrix, Chapman- Kolmogorov equations, Regular chains and Stationary distributions, Periodicity, Limit theorems. Patterns for recurrent events. Brownian Motion - Limit of Random Walk, its defining characteristics, and peculiarities; Martingales.
5. Multivariate Analysis
• Multivariate normal distribution and its properties and characterization; Logit-Probit models Mahalanobis’ D2 statistics; linear discriminant analysis (LDA); Canonical correlation analysis, Principal components analysis, Factor analysis, Cluster analysis.
6. Econometrics and Time Series
• General linear model and its extensions, ordinary least squares and generalized least squares estimation and prediction, heteroscedastic disturbances, pure and mixed estimation. Auto correlation, its consequences, and related tests; Theil BLUS procedure, estimation, and prediction; issue of multi-collinearity, its implications, and tools for handling it; Ridge regression.
• Linear regression and stochastic regression, instrumental variable regression, panel regression, autoregressive linear regression, distributed lag models, estimation of lags by OLS method. Simultaneous linear equations model and its generalization, identification problem, restrictions on structural parameters, rank and order conditions; different estimation methods for simultaneous equations model, prediction and simultaneous confidence intervals.
• Exploratory analysis of time series; Concepts of weak and strong stationarity; AR, MA and ARMA processes and their properties; model identification based on ACF and PACF; model estimation and diagnostic tests; Box- Jenkins models; ARCH/GARCH models.
Inference with Non-Stationary Models
• ARIMA/SARIMA model, determination of the order of integration, trend stationarity and difference stationary processes, tests of non-stationarity.
7. Optimization and Statistical Computing
• Unconstrained optimization using calculus (Taylor‘s theorem, convex functions, coercive functions). Unconstrained optimization via iterative methods (Newton‘s method, Gradient/ conjugate gradient-based methods, Quasi Newton methods). Constrained optimization (Penalty methods, Lagrange multipliers). Convex sets, Convex hull, Formulation of a Linear Programming Problem, Theorems dealing with vertices of feasible regions and optimality, Graphical solution, Simplex method.
• Simulation techniques for various probability models, and resampling methods jack-knife, bootstrap and cross- validation; techniques for robust linear regression, nonlinear and generalized linear regression problem, tree- structured regression and classification; Analysis of incomplete data - EM algorithm, single and multiple imputation; Bayesian modelling and estimation; Markov Chain Monte Carlo and annealing techniques, Gibbs sampling, Metropolis-Hastings algorithm; Neural Networks, Association Rules and learning algorithms
8. Data Science, Artificial Intelligence and Machine Learning Techniques
• Introduction to supervised and unsupervised pattern classification; unsupervised and reinforcement learning, basics of optimization, model accuracy measures. Linear Regression, Logistic Regression, Penalized Regression, Naïve Bayes, Nearest Neighbor, Decision Tree, Support Vector Machine, Kernel density estimation and kernel discriminant analysis; Classification under a regression framework, neural network, kernel regression and tree and random forests. Hierarchical and non-hierarchical methods: k-means, kmedoids and linkage methods, Cluster validation indices: Dunn index, Gap statistics. Bagging (Random Forest) and Boosting (Adaptive Boosting, Gradient Boosting) techniques; Recurrent Neural Network (RNN); Convolutional Neural Network; Natural Language Processing. Recursive Feature Elimination (RFE), Variance Inflation Factor (VIF), ensembling and stacking methods, Elastic Net regularization, hyperparameter tuning via Grid Search, feature importance interpretation, and cross-validation strategies
9. Database and Data Warehouse Management
• Data structures; Fundamentals of Relational Database Management Systems (RDBMS) and non-traditional (NoSQL) databases. Principles of database normalization, data redundancy elimination, and consistency maintenance. Structured Query Language (SQL) – querying, updating, aggregating, and managing relational data. Database joins – inner join, left join, right join, outer join – with applications to data merging and integration. Overview of NoSQL databases – document-based, key-value, wide-column stores, and graph databases. Data warehousing concepts, star and snowflake schemas, ETL (Extract, Transform, Load) processes, and OLAP vs OLTP. Database indexing and optimization. Basics of big data frameworks and storage systems for large-scale data handling.
Paper-III English:
The paper on English shall be framed in a manner to assess the writing skills including expression and understanding of the topic.