ENESST

Certificate in Credit Risk Assessment, Modelling & Management

Stay ahead: Master Lo Code ML and AI in Credit Risk Modelling to become a next-gen credit professional


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Course Overview

Credit risk modelling is crucial to the credit management process. Rising competition and regulatory demands have increased its importance, while machine learning and AI add complexity. The challenge is making these technologies accessible to non-IT and non-statistics professionals.

Why You Should Attend the Course?

  • Edge Your Competition

    Credit risk is a significant concern whether you are seeking or extending credit. Gone are the days when credit decisions were the sole concern of financial institutions; now, increased competition has compelled all entities to use credit as an incentive to attract buyers, thereby exposing themselves to credit risks.

  • Become a next-gen credit professional

    The growing use of machine learning and artificial intelligence has further complicated credit risk measurement. However, most training on credit risk modelling remains heavily focused on mathematical modelling or coding, which limits understanding to a select few.

Who should attend.

This course is highly beneficial for any enterprise involved in credit activities. It is particularly valuable for:

  • Credit Risk Managers
  • Analysts
  • Risk Managers
  • Modellers,Portfolio Risk Managers, Financial Analysts, Credit Department Executives, Internal Auditors, Balance Sheet and Asset/Liability Managers, Account Receivables and Collections Managers, Fund Managers, and back and middle Office Managers engaged in credit risk management

Competencies

These are the main capabilities/proficiencies that the course will equip the delegates with:

  • 1. An overview of credit risk
  • 2.The key components and events
  • 3.TTerminologies: probability of default, exposure at default, loss given default
  • 4.Credit loss structure: expected, unexpected, extreme loss
  • 5.Sovereign debt, ratings, and default swaps
  • 6.Accounting for credit quality deterioration
  • 7.Lo Code ML and AI-based credit risk modelling
  • 8.Credit risk modelling approaches: fundamental, statistical, structured, and hybrid

Pre-Course Requirements

Basic knowledge of credit instruments, balance sheets, and Excel spreadsheets

Ability to install Anaconda software and the Excel add-in on your laptop

No prior coding experience is needed; the workshop will use AI tools on your laptop

The workshop is designed for various industries, including banking, but is not exclusively focused on banking credit risk

Delegates must bring their own laptops with Excel spreadsheet installed

Education Partner

Course Requirements and Certificates

You must meet two criteria to be eligible for eNESST AcademyCertificate of Completion:

  • Satisfactory attendance – You must attend all sessions of the course. If you miss more than two hours of the course you will not be eligible to receive the certificate
  • Successful completion of the course assessment – Assessment will be ongoing based on your in-class participation

If you do not meet these criteria, you will receive an eNESST Academy Certificate of Attendance. If you have not attended all of the course, the certificate will clearly state the number of hours you attended.

ABOUT PRICING AND DOCUMENTATION

Cost per person: $1200

Pricing excludes VAT, charged where applicable. Course fees include documentation, luncheon and refreshments for in- person learning Delegates who attend all sessions and successfully complete the assessment, will receive eNNESST Certificate and any applicable partner certificates.

For Special Offers And Discounts:

Call: +250 785731690
Email: jackton@enesst.com
Visit Website: www.enesst.com

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