Financial Risk Manager (FRM®) Exam Part I Preparation Course in Saudi Arabia

Risk types and the risk management process

Enterprise risk management and ERM frameworks

Corporate governance and board-level risk oversight

Risk appetite, risk culture, and risk strategy

GARP Code of Conduct and professional standards

Risk-adjusted performance measurement basics

Barings Bank collapse and rogue trader risk

LTCM failure and model risk lessons

Orange County and interest rate risk

2007 to 2009 Global Financial Crisis anatomy

Risk management failures and internal controls

Applying disaster lessons to current governance

Probability rules, conditional probability, Bayes theorem

Discrete and continuous probability distributions

Normal, lognormal, Student t, Poisson, binomial distributions

Descriptive statistics: mean, variance, skewness, kurtosis

Hypothesis testing and confidence intervals

Correlation, covariance, and portfolio statistics

Linear regression: construction, interpretation, diagnostics

Multivariate regression and model assumptions

Time-series models: AR, MA, ARMA frameworks

Monte Carlo simulation for risk scenario generation

Volatility estimation: EWMA and GARCH models

Machine learning basics in risk modelling context

Forwards and futures: pricing and cost-of-carry models

Interest rate, currency, and equity swaps mechanics

Options: put-call parity, payoffs, and trading strategies

Exotic options and structured product features

Hedging strategies using derivatives in institutions

Central clearing and counterparty credit risk basics

Bond pricing, yield measures, and yield curve analysis

Macaulay duration, modified duration, DV01, convexity

Mortgage-backed securities and securitisation mechanics

Foreign exchange markets and FX forward pricing

Commodity markets: futures pricing and storage costs

Interest rate risk and fixed income hedging strategies

VaR: historical simulation, parametric, and Monte Carlo methods

Expected shortfall as a coherent risk measure

Black-Scholes-Merton option pricing model

Binomial tree option valuation for American options

The Greeks: Delta, Gamma, Vega, Theta, Rho

Using Greeks to manage options risk in portfolios

Credit risk: PD, LGD, EAD, expected and unexpected

Credit risk models and counterparty exposure

Operational risk definitions and measurement approaches

Stress testing: scenario analysis and reverse stress testing

Term structure models: spot rates, forward rates, bootstrapping

Country and sovereign risk assessment frameworks

Timed 100-question mock exam under CBT conditions

Topic-by-topic performance analytics and gap analysis

Elimination strategies and calculation shortcuts

Calculator fluency: BA II Plus and HP 12C

Personalised revision planning from mock results

Time management across four-hour exam sessions

Participants take on the role of junior risk analysts at a mid-sized commercial bank preparing for a regulatory review.

The bank holds a mixed portfolio of corporate loans, interest rate swaps, equity options, and sovereign bonds across three geographies.

Participants must assess market risk using VaR and expected shortfall, calculate credit risk expected loss across the loan book, evaluate the duration and convexity exposure of the fixed income portfolio, and flag operational risk events that require capital treatment.

The scenario is structured to mirror the four FRM Part I topic areas simultaneously, forcing candidates to switch between quantitative, markets, and risk modelling frameworks as they would on exam day.

Working in small online breakout groups, participants divide responsibilities across risk type, then reconvene to present a consolidated risk summary to a simulated risk committee.

Each group must justify their VaR methodology choice, defend their duration-based hedging recommendation, and explain the governance implications of two operational risk events.

The simulation concludes with a 20-question timed MCQ set drawn from the scenario, replicating the exam's requirement to apply knowledge under time pressure.

Facilitators debrief each group's decisions against GARP learning objectives, reinforcing the link between simulation performance and exam-day accuracy.

Participants work in a simulated AI-assisted study environment designed specifically for FRM Part I preparation.

Using generative AI tools integrated into the learning platform, candidates input their target exam date, available weekly study hours, and mock exam performance scores by topic.

The AI generates a dynamic, week-by-week study plan that allocates remaining self-study hours proportionally to GARP's 20-20-30-30 topic weights, adjusting emphasis toward each candidate's identified weak areas.

Participants also use AI-powered scenario generators to produce novel FRM-style MCQs on demand across probability, derivatives pricing, VaR, and credit risk, preventing over-reliance on memorised question sets.

Candidates then use the sandbox to automate their performance tracking workflow.

After each timed quiz or mock exam, they upload their topic-by-topic score breakdown and the AI produces a ranked revision priority list, flags chapters where error rates exceed a threshold, and suggests targeted readings from the GARP curriculum.

Governance stays in place through a trainer-review layer: all AI-generated questions are validated against current GARP Learning Objectives before use, and candidates are trained to treat AI output as a draft requiring expert verification.

The sandbox closes with each participant exporting a finalised, AI-assisted self-study plan covering the weeks between the training program and their live exam sitting.