Issue of Discussion: When looking at the developed lending markets of North America and Britain from one of the developing markets elsewhere it may not always be clear how successful strategies from the former regions can be applied in the latter.

However, automated credit risk strategies offer value to all lenders, regardless of their level of development and in this article I discuss the key steps that need to be mastered at each stage of development.

When the fundamentals that underpin credit risk strategies are well understood they can be kept constant even while their outward appearance might have to change to facilitate an implementation in a new environment.

By whom and where: Brendan L., Global Business Consultant at Experian Decision Analytics, Copenhagen Area, Denmark [In Microfinance Focus LinkedIn Group]

Respondent 1

Comments:

Kaizad Cama- Brendan, I agree with your point about elevating credit risk to the front office and focusing on risk-based profitability. In the US, I think we're starting to see this shift. But turning back to developing markets, it is often a challenge to build reliable risk scorecards, is it not? On a recent trip to South/SE Asia, the dominant form of scorecards I saw was based on management judgment, not quantification.

Where does one find the "pooled third-party data that relates to a portfolio that is similar to the one in which it is to be implemented"?

Respondent 2

Comments:

Ascanio Graziosi- Yes, the problem is just to have a reliable and similar data to a given portfolio. To evaluate a risk a good basis is the availability of a Credit Bureau to match a given data with a wider population.

However besides credit risk there are other kind of risks: financial, operational, market, exchange rate, country, etc. So, is this model flexible to be adapted to different categories of MFI (size, people target: poverty, income generating activities, enterprise development)?

Moreover, in the emerging economies there is is well known asymmetric information's problem, which is sorted out by the lender capability that is unquantified. In conclusion, you should provide some more details.

Perhaps, you should post it in my Group to get more audience involved in.

Respondent 3

Comments:

Brendan Le Grange- Thanks Kaizad. You are right, the building a scorecard can be difficult in an environment where no data exists. However, the important thing about any new scorecard is not so much that it is 'world-class' but rather that it is better than the previous solution while also enabling an even better solutions in the future.

So, a scorecard built only based on management insight still adds value by automating the decisioning process and by capturing the data that is needed to build a 'proper' scorecard in the future.

By the same principal, a generic scorecard built using pooled data (purchased from a third-party supplier in the market - credit bureau, statistics agency, etc.) or from another market is likely to be better than an expert model and is likely to provide even better data for a future bespoke model.

Ascanio - thanks for your input. I have looked here only at matters relating to credit risk and mainly at matters relating to the credit decisioning process as it would be for more established lenders in developing markets. Many of the same principals can be applied to the smaller, more purely MFIs with some adaptations and I am working on a follow-up article which will address this.

I also agree that there are a number of risks beyond credit risk in these markets but I am unfortunately not qualified to offer too many opinions on those. I will look-up your group though and see if we can get some good ideas from there to head towards a more comprehensive model for risk in developing markets.