call download fax letter pdf search x chevron
"jcr:b6dd0cf6-c77d-4061-bb40-2fcf3cfbef34" (String)

Enhancing SME Digital Lending in Egypt through Credit Scoring Masterclass

An online seminar series by GIZ and Frankfurt School of Finance and Management

Successful banks and financial institutions of the 21st century have understood the importance of data for their business operations. GIZ Egypt and Frankfurt School are targeting this by providing a high-profile webinars series, which aims to provide knowledge transfer on advanced Credit Scoring, data analytics, in particular machine-learning algorithms to develop predictive models. Demonstrated on automated customer scoring the concepts will be explained and shown in very detail and could be used for many other analytical tasks.

Besides, the course conveys approaches to obtain relevant Environmental, Social, Governance (ESG) data and consider that in automated scoring models to obtain weighted scores, assess environmental risks and simplify ESG reporting. The series will be delivered online in a combination of lectures and case studies.

GC-GIZ-implemented-by_EN.JPG

Partner Logos

Target Audience

We are targeting professionals from the financial sector in general. The sessions are well structured to make sure its relevance for Banking Sector Professionals and Non-Banking Financial Services Professionals. Regulators are also invited to the online sessions.

Certificate

Participants who attend more than 70% of the sessions will receive a certificate of attendance.

In addition, there will be the option to prove your knowledge by a masterclass at the end of the course. If you pass the exam, you will receive an extra certificate issued by Frankfurt School.

Registration

Register for the online seminar series below. Session links will be sent to you via email after registration.

Course Structure

The length of each webinar is approximately 2 hours. The modules and sessions are built on one another. We therefore encourage you to attend the full programme. However, if you don’t want to follow the full programme, you will also be able to attend parts of it. In this case, we highly recommend to at least follow all sessions of a module, since they will be closely linked to each other.  

All times are displayed in Egypt Standard Time.  

Read more about the module content below:

Module One

The first module provides a general introduction into statistical scoring and machine learning. Moreover, it ensures that the students are/get familiar with the widely applied expected loss metrics, as a standard measure for credit risk. We want to emphasize that scoring is a process, not a secret formula. The success of a scoring system, as any predictive model in banking, relies not only on the technical and analytical capacity of developing staff but also on wide staff ownership, an appropriate customer view and underlying data. The second session highlights the most important success factors on a glance and provides some anecdotal evidence why in some cases models do not work as expected.

For the case studies of this series, general Excel skills are required. In the third session we an optional training on the most required Excel functions, in particular Pivot Tables, LOOKUPs resp. INDEX/MATCH, SUMIFS/COUNTIFS, Filters, Sorting, Conditional formatting, Charts, and the Data Analysis Tool Kit. It is only recommended for participants who feel serious uncertainty in many of those.

Module Three

This module is the core of the course. Participants will learn everything about developing a credit scoring model on their own. The first part will lay the necessary mathematical and statistical foundations for applied popular machine learning algorithms. It will demystify the statistics and provide a very solid under-standing of what these algorithms do and how they should be interpreted without using “scary math”, Greeks and integrals.

The second part is dedicated to a large case study. We will show, explain, and ultimately teach all necessary work steps from the initial data to the final model – all in MS Excel together with a little plugin. Every individual work step will base on the theoretical foundation laid in the session described above. After that you should be able to apply various machine learning algorithms on your own data, hence train and test your own models.

Module Five

This module has a closer view on Egyptian regulatory rules with regards to scoring and rating schemes and also presents and dis-cusses general taxonomies and frameworks (e.g. BASEL, IFRS 9). Moreover, it compares respective guidelines from other jurisdictions and aims to derive recommendations for a revised Egyptian framework. This module is partly planned to be an interactive workshop with the regulator group.

Module Seven

In the light of serious climatic effects it is seen as a duty for many banks to anchor respective commitments in their corporate identity and daily procedures. There are increasing efforts, often supported by the international donor community, to facility and accelerate such developments. This module will discuss and present possible approaches and lessons learnt from that journey – in particular how risk-evaluating scoring schemes can be enriched with ESG data to obtain a rating about a customer’s environmental foot-print.

Module Two

Adequate, accurate and complete data is the most decisive single resource to train predictive models. This module will focus on data importance, common accuracy and consistency issues and typical quick wins by improving a data architecture. Besides, it will link to legal requirements in the Egyptian jurisdiction.

Module Four

Model development is one thing, implementation the other. This module focuses on next steps to ensure a successful implementation, fully utilizing efficiency gains and at the same time not creating discomfort or fear among staff and clients. It further touches on best-practice process and policy adjustments and provides guidelines for successful system implementation.

Customers expect high service quality, convenience and quick response. Digital financial services are commonly understood as a solution to achieve all of these goals simultaneously. In lending, a fully digitalized workflow from loan application until disbursement can be highly efficient, cut costs and become a strong argument for customers. This session will elaborate on the latest digital lending approaches and how digital scorecards play a role in that. It will also create a link to the previous sessions on effective data management to make the most use of your data.

Module Six

Many financial services providers target small and medium agricultural businesses, which typically have different risk profiles and extraordinary cash cycles. The industry has made significant attempts and progress in embedding agricultural indicators into credit scoring schemes. This module will give an overview about such techniques and present necessary work steps to develop internal agri scorecards.

Module Eight

Who is interested into more of the mathematical foundations for algorithms presented in the previous module may wish to attend the first two sessions of this fully optional module. Here, unfortunately, some “scary math” cannot always be avoided, but will be used at the bare minimum.

This course is provided in MS Excel to be open for practitioners, who are not familiar with statistical programs and programming languages. However, depending on the amount of data used to train a model, the calculations, though mathematically simple, can become computationally very expensive. In such cases, Excel would not be the most appropriate modelling environment. It is beyond the scope of this course to provide comprehensive guidance for many available ML programs/languages.. Though this cannot be shown in depth in this course, the sessions introduce approaches for Logistic Regression and Random Forest Classifiers in R.

Session Topic
Date & Time
Module One - Credit Scoring Model Principles

Session 1: Introduction to Digital Credit Scoring Models and Machine Learning

18.10.2021, 10:00 - 12:00       

Session 2: Prerequisites, Limitations, and Success Factors in Credit Scoring

20.10.2021, 10:00 - 12:00

Session 3: Basic Excel Training 

Download training file: Excel_Demo.xlsx

Information: The Excel training is tailored to the needs of this course. It is not a general training for first-time Excel users. If you are already a proficient Excel user, it might not be necessary for you to take this session. Please have a look at the tasks which will be assigned during the session and decide for yourself if this session is valuable for you. The tasks can be found in the Excel file above

21.10.2021, 10:00 - 12:00
Module Two - Strategic Data Development

Session 4: Strategic Data Development (1/2)

25.10.2021, 10:00 - 12:00

Session 5: Strategic Data Development (2/2)

Download training file: DataProblems.xlsx

Information: In session 5, we will discuss common mistakes in the data capturing process and how to mitigate them. Find some examples and tasks in the excel attached. Please have a look before the session.

27.10.2021, 10:00 - 12:00

Module Three - Model Development

Session 6: Underlying Statistical Concepts and General  Understanding of Selected Machine Learning Algorithms (1/3)

01.11.2021, 10:00 - 12:00

Session 7: Underlying Statistical Concepts and General Understanding of Selected Machine Learning Algorithms (2/3)

03.11.2021, 10:00 - 12:00

Session 8: Underlying Statistical Concepts and General Understanding of Selected Machine Learning Algorithms (3/3)

08.11.2021, 10:00 - 12:00

Session 9: Case Study: From Raw Data to a Predictive Model - Work Steps and Testing (1/3)

10.11.2021, 10:00 - 12:00

Session 10: Case Study: From Raw Data to a Predictive Model - Work Steps and Testing (2/3)

15.11.2021, 10:00 - 12:00

Session 11: Case Study: From Raw Data to a Predictive Model - Work Steps and Testing (3/3)

16.11.2021, 10:00 - 12:00
Module Four - Model Implementation

Session 12: Model Implementation

22.11.2021, 10:00 - 12:00

Session 13: Digital Lending & Loan Origination

24.11.2021, 10:00 - 12:00

Session 14: Digital Lending & Scoring Model Integration

29.11.2021, 10:00 - 12:00

Module Five - Relevant Regulations & International Standards (Special focus topic)

Session 15:  Regulations and International Best Practicies

06.12.2021, 10:00 - 12:00

Module Six - Agricultural Finance (Speical focus topic)

Session 16: Agricultural Scoring (1/2)

08.12.2021, 10:00 - 12:00

Session 17: Agricultural Scoring (2/2)

09.12.2021, 10:00 - 12:00
Module Seven - The Way Forward

Session 18: Outlook: Integrating ESG-Data / Climate Aspects into Scoring Models (1/2)

14.12.2021, 14:00 - 16:00

Session 19: Outlook: Integrating ESG-Data / Climate Aspects into Scoring Models (2/2)

15.12.2021, 16:00 - 18:00

Session 20: Beyond Scoring - Other Machine Learning Use Cases in Financial Sector

20.12.2021, 10:00 - 12:00
Module Eight - Advanced Machine Learning

Session 21: Deep dive into Selected Machine Learning Algorithms (1/2)

10.01.2022, 10:00 - 12:00

Session 22: Deep dive into Selected Machine Learning Algorithms (2/2)

12.01.2022, 10:00 - 12:00

Session 23: Logistic Regression and Random Forest in R

17.01.2022, 10:00 - 12:00