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Workshop topics Friday, September 18

Topic 1

Credit scoring done right.

Speakers: Mr. Joachim Bald and Mr. Jules Ndambu, Frankfurt School

Overview Credit scoring is a process and not a secret formula. It is a formalized approach to institutional learning in credit risk assessment. And credit risk assessment is the one thing a lender should never outsource. This course is about demystifying the statistics in credit scoring (briefly!) and giving you the vocabulary to lead the discussion on how to fully internalize scoring at your institution. We will provide real numbered examples for the full scope of scoring applications: statistical application scoring, credit bureau scoring, expert scoring, various hybrid scoring models, collections scoring, scoring versus rating etc.  This is the easy part.   The challenges are in generating the socio-economic client data, in improving data quality, getting the buy-in from the sales staff, efficiently integrating the scoring into the credit decision process, in maintaining and improving the predictive power of the scoring models etc.

Goals Nobody leaves the room without knowing how to calculate a statistical credit score on your own data using logistic regression in Excel. If you are using some form of statistical or expert scoring already, you will know how to perform a back test on the predictive performance of your existing model. If you retained the elementary risk factor assessments from your expert score in a database, you can ask your staff to optimize the factor weights using logistic regression. Do not hire consultants to deliver a turn-key scoring model. We can teach your own team how to manage the entire scoring and rating process.

Requirements Everyone is welcome. We'll go easy on the statistics. Please share your own experiences and ask many questions.

topic 1. relevant documents

Topic 2

How currency and interest rate risk transpires as credit risk.

Speaker:  Mr. Per van Swaay, TCX, The Netherlands

Overview We will discuss the nature and behavior of exchange rate volatility with reference to emerging and frontier markets examples. We will illustrate how a simple tool can help to measure impact of FX crisis on a company’s balance sheet.

Goals To raise awareness of the drivers of exchange rate market volatility and its impact on credit risk and to increase awareness of remedial measures.

Requirements None.

Topic 3

Information and Business model innovations to reduce Credit Risk.

Speaker:  Mr. Jose MantillaEnclude

Overview Credit risk is all about identifying and adjusting to borrowers' willingness and capacity to pay at origination and over the life of the loan.  This has traditionally be exceedingly difficult in the case of small and medium enterprises (SMEs) due to information asymmetries, as the borrower may be unable or unwilling to share information with the lender, and the lender has few options for gathering that information.  But we live in the age of information! Today, data is available from a myriad of sources, and some of that data even lies unused in our institutions.  Alternative lenders and peer-to-peer loan “marketplaces” in the USA and Europe are taking advantage of such information, carving out significant niches in the process.  Closer to home, interesting new business models are being tried out to “share the risk”. So how should we approach the issue? Beyond information, are there other ways for financial institutions to share credit risk?  Can we combine both?  What roles do BDS and financial education play? Can we thus increase lending to SME with a much lower risk than we could just a few years ago? 

Goals • To review some of the most interesting new concepts in SME lending from around the world, as well as some tools and business model innovations already in use by banks in the developing world.  • To discuss if and how some of those can be applied in the institutions of those present, and the strategic decisions they may imply on the way we gather, use data, and even do business. • To generate open and frank discussion amongst all the participants as to data trends in banking and the implications for our institutions. Note: This is not a course of data mining or credit scoring, the focus is on strategy, the need to think out of the box and identify proxies to inform our credit decision and business.

Requirements Everyone is welcome. If you are doing something non-traditional at your institution all the more so, as we would love to hear about it.

Topic 4

Incorporating Environmental & Social risk into credit risk analysis.

Speakers: Mr. Lawrence Pratt, INCAE, and Mr. Javier Barnes, BAC

Overview The commercial relationships that financial institutions have with their clients represents potential legal, financial and / or reputational risks. Because environmental and social issues are inherent in business operations, most transactions have some degree of environmental and social risk. Managing those risks is critical to maintaining FI value. Lawrence Pratt, an expert in the field of E&S risk management and sustainability will explore how banks can protect themselves in this regard by engaging Javier Barnes, a Credit Manager from BAC, a Costa Rican Bank that has been through the process of developing and implementing a Environmental and Social Risk Management System (ESMS). Perspectives from FMO will support the exploration, and we invite class participants to share their experiences and opinions.

Goals The goal of this session is to: - Highlight and understand the value drivers for E&S risk management at a successful mid-sized bank. - Develop and understanding of the basic processes and mechanics of E&S risk assessment and reduction. - Provide participants with a practical view on how E&S risk can be addressed, and most importantly, how this can be done cost-effectively. - Elaborate upon some examples of successful risk reduction opportunities. - Collect and harvest within the group lessons learned and recommendations.

Requirements None: just be ready to share your E&S war stories.

Topic 5

A no-nonse approach to risk modelling and big data.

Speaker: Mr. Andre KochStachanov (TBC)

Overview As credit managers we'd like to get a better grip on default risk and at the same time reduce the operational costs in dealing with credit demands. At first glance, this might seem a pipe dream, but this seminar will demonstrate how this can be realised nonetheless. New techniques and technologies founded on what commonly is referred to as “big data analysis” aren’t just for the big Wall Street banks - they are entirely within reach for your MFIs, no matter the size. In the course of this seminar, we will not only unlock the essence of concepts such as “big data”, but also “machine learning”, “Bayesian statistics”, and more. With wit and inspiring examples these concepts will be explained and, what’s more, the question as to what’s in it for the risk managers in terms of both credit scoring and a lot more will be answered to satisfaction. An automated decision making model based on data analysis of the bank’s or MFI’s own credit portfolio will help us to realise our vision. The seminar will steer away from theoretical discourses, but will show how these novel techniques can be implemented in practice, using hands-on, real world cases. Ample examples broader than just credit scoring from current projects with African MFIs will lard the talk’s drier parts. Not unimportantly, by the end of the seminar you will know the answers to some riddles that will even impress your kids.

Goals The goal of the course is to explain and demonstrate that the statistical analysis of an MFI’s own data sets is a simple, yet effective way, to automatically generate default prediction models. The lecturer will urge the participants to put the hand to the plough and start using the approach for themselves after the weekend. 

Requirements Enough sleep and an inquisitive mind.

Topic 6

Stress testing that you always wanted to run but never dared to ask.

Speaker:     Mr. Evgueni Ivantsov, European Risk Management Council

Overview The Masterclass will provide a comprehensive coverage of key elements of stress testing that are vital for every organisation to build and maintain a robust stress testing framework. In particular, the special attention will be given to regulatory requirements and expectations, stress testing governance, an effective approach of stress scenario formulation and selection, a robust way of scenario analysis and building of comprehensive stress mitigating action plan.   

Goals - To give attendees the “big picture” of how a state-of-the-art stress testing should be run, what constitutes a robust stress testing framework, and how an organisation should adapt this framework in order to meet regulatory requirements and senior management expectations.   - To provide a detailed description of what constitutes a good stress scenario, how scenarios should be formulated and selected for implementation. - To explain how stress scenarios should be analyzed, what tools can be used to ensure an effective stress results assessment and what are main pitfalls that should be avoided when stress results are calculated.  - To equip attendees with knowledge of how organisations can efficiently develop mitigating action plan for a stress scenario. - To share the best industry practice in stress testing governance and how the independence of the stress testing process can be achieved.

Requirements There are no special technical requirements for the course.