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Financial Modelling, Actuarial & Credit Risk Analytics - AI
Rating: 4.4 out of 5(170 ratings)
1,399 students

Financial Modelling, Actuarial & Credit Risk Analytics - AI

Master financial modelling, actuarial analytics, credit risk and AI & LLMs for real-world financial decision-making
Created byPius Dave
Last updated 10/2026
English
English [Auto],

What you'll learn

  • Build integrated financial models covering financial statements, forecasting, assumptions, and financial performance analysis.
  • Create DCF, valuation, M&A, LBO, project finance, and real estate models using practical financial modelling techniques.
  • Apply actuarial analytics to survival, mortality, insurance, annuities, loss models, pricing, reserving, and longevity.
  • Develop credit risk models using credit data, WOE, Information Value, scorecards, PD, LGD, EAD, and IFRS 9.
  • Validate credit models and analyze portfolio concentration, stress testing, expected credit loss, and credit decisioning.
  • Apply scenario analysis, sensitivity analysis, risk measurement, and solvency concepts to financial and risk models.
  • Use AI and LLMs for financial analysis, financial documents, data analysis, research, reporting, and workflow automation.
  • Build practical RAG, financial research agent, and analyst copilot workflows for AI-powered finance and risk analytics.

Course content

6 sections • 152 lectures • 22h 8m total length
  • Course Introduction2:30
  • Foundations & Model Design4:31
  • Excel Toolkit for Financial Modelling3:17
  • Lookup, Logic & Date Functions2:48
  • Financial Mathematics in Excel3:25
  • Three-Statement Modelling3:25
  • Income Statement Modelling2:51
  • Balance Sheet Modelling2:32
  • Cash Flow Linking3:19
  • Working Capital2:51
  • Depreciation & PPE3:03
  • Debt & Interest Modelling2:46
  • Equity & Shares2:36
  • Revenue Forecasting3:05
  • Financial Statement Analysis6:17
  • Scenario & Sensitivity Analysis2:55
  • Model Auditing2:57
  • DCF & Free Cash Flow3:03
  • DCF with WACC & Terminal Value3:50
  • Trading Comparables3:36
  • Precedent Transactions2:43
  • LBO Modelling3:16
  • M&A / Merger Modelling3:24
  • Project Finance3:06
  • Real Estate Modelling3:08
  • Dashboards & Capstone2:57
  • Guided Financial Modelling Practice3:26

Requirements

  • No prior financial modelling, actuarial, or credit risk experience is required. Concepts are introduced progressively.
  • Basic understanding of finance, accounting, mathematics, or statistics is helpful but not mandatory.
  • No advanced programming experience is required. The course focuses primarily on financial and risk analytics concepts.
  • A computer with Excel and internet access is recommended for following the practical modelling exercises.
  • An interest in financial modelling, risk analytics, banking, insurance, actuarial analysis, and AI applications in finance.

Description

Master Financial Modelling, Actuarial Analytics, Credit Risk and AI & LLMs in one comprehensive finance-focused course.

This course brings together four highly practical areas of modern financial analytics: Financial Modelling, Actuarial Analytics, Credit Risk Analytics, and AI & LLM applications for Finance.

You will begin with the fundamentals of financial modelling and progressively build skills in financial statements, forecasting, valuation, scenario analysis, DCF, M&A, LBO, project finance, and real estate modelling.

You will then explore Actuarial Analytics, covering survival and mortality analysis, insurance and annuities, loss models, pricing, reserving, ruin theory, risk measures, mortality and longevity, and solvency and capital.

The course also covers Credit Risk Analytics, including credit data analysis, WOE and Information Value, logistic scorecards, model validation, PD estimation and calibration, LGD, EAD, IFRS 9/ECL, portfolio concentration, stress testing, machine learning, and credit decisioning.

Finally, you will learn how AI and Large Language Models (LLMs) can be applied to financial and risk analytics. Topics include prompt engineering, structured outputs, embeddings, RAG, financial document intelligence, financial data analysis, sentiment analytics, automated reporting, research agents, workflow automation, analyst copilots, LLM evaluation, security, governance, and model risk.

What You Will Learn

  • Build integrated financial models from financial statements

  • Perform financial forecasting and scenario analysis

  • Build DCF, valuation, M&A, LBO and project finance models

  • Apply actuarial concepts to insurance and financial risk

  • Analyze mortality, longevity, loss, pricing and reserving models

  • Build and validate credit risk scorecards

  • Understand PD, LGD, EAD and Expected Credit Loss

  • Perform credit portfolio and stress-testing analysis

  • Apply AI and LLMs to financial analysis and reporting

  • Build RAG systems for financial documents

  • Develop financial research and analyst AI workflows

  • Understand AI governance, security, evaluation and model risk

Who Is This Course For?

This course is designed for finance professionals, financial analysts, risk analysts, credit professionals, actuarial and insurance professionals, students, business analysts, and aspiring finance data professionals who want to develop practical modelling and AI skills.

Whether your goal is financial modelling, risk analytics, credit analysis, actuarial modelling, or AI-powered finance, this course provides a broad practical foundation across these interconnected areas.

Learn to build financial models, measure risk, analyze credit and actuarial problems, and use modern AI & LLM technologies to enhance financial decision-making.

Who this course is for:

  • Finance professionals who want to strengthen their financial modelling, valuation, and risk analytics skills.
  • Credit risk, banking, lending, and financial risk professionals who want practical credit modelling knowledge.
  • Actuarial and insurance professionals or students interested in actuarial analytics, risk modelling, and financial applications.
  • Financial analysts, business analysts, MBA students, commerce graduates, and aspiring finance professionals.
  • Professionals interested in applying AI and LLMs to financial modelling, credit risk, actuarial analytics, and financial research.