Read the full paper here.
Download the template here.
Multilateral Development Banks (MDBs) are mandated by their shareholder to help Emerging Market and Developing Economies (EMDEs) achieve the 2030 Sustainable Development Goals (SDGs) adopted by United Nations member states in 2015. Recently there have been even more urgent calls for MDBs to assist EMDEs in responding to the environmental emergency facing the world. How MDBs react to these challenges will significantly affect the economic prosperity and general well-being of much of the world’s population.
To boost their financial firepower and expand their lending, MDBs are pursuing multiple avenues. One important such avenue is risk transfer. Through insuring risks on their balance sheets, MDBs can create room to lend more without reducing their own financial stability. Risk transfer can take many forms, including guarantees, securitisation, and credit insurance, for individual exposures, or at the level of portfolios. In all cases, external investors or insurers must evaluate the risk of the loan or loans in question, analyse the structure of the proposed contract and determine the price of taking this on. After a transaction has occurred, if the risky loans remain on the balance sheet of the original lender or continue to be serviced by the original lender, investors will require regular updates on the performance of the loans and information on any characteristics that have changed.
Information is key to this process. Investors need to be able to assess the credit quality of an institution’s loans by analysing their historical performance. They must have full information about the loans that they are to insure, and this information should be updated over the period of the insurance. The information should provide the data required (i) to analyse credit quality, (ii) to understand loan cash flows and, hence, price claims, assuming the structure of the risk transfer makes this material, and (iii) to analyse the Environmental, Social and Governance (ESG) characteristics and impact of the loans.
MDBs may seek to develop data formats and templates to convey the information just mentioned but a case may be made for adopting a common set of data templates. This document describes the results of a project by Risk Control to devise such a data template. The single consideration guiding the design of this data template has been to provide information in a way maximally useful to investors in MDB risk transfers.
The project forms part of Risk Control’s work for the MDB Challenge Fund. The Fund was created in 2022 to sponsor activities that would help MDBs to implement the recommendations of the Independent Panel on MDB Capital Adequacy Frameworks, set up by the G20. The Risk Control team includes banking professionals with decades of experience in designing and implementing risk transfers on behalf of banks and risk specialists with extensive experience of risk and valuation analysis of MDB exposures.
An influential group of MDBs and Development Finance Institutions (DFIs), named the Global Emerging Markets Risk Database (GEMs) Consortium, has established common ways of recording credit-related loan information and combined their respective historical loan data in a database accessible to consortium members. Although the objectives of the GEMs Consortium may be evolving, the GEMs data was originally intended to facilitate risk model calibration and high-level reporting of MDB loan performance. The reports generated have only recently been made publicly available by the Consortium.
In our understanding, the data template proposed in this report, aimed at investor needs only, differs significantly from the GEMs template where the focus is principally on default and asset classes, countries and ratings. Among credit-relevant fields, the data template provides financial information that investors are likely to require on borrower, collateral, amortisation, spreads, and coupons, in order to make to make investment decisions. On non-credit aspects, it covers sustainability and ESG indicators.
The data template here described is influenced by investors’ requirements for securitisation transactions (both synthetic and true sale) but the objective of the project is to develop a common way of describing loans that can support a wide range of risk transfer approaches including traditional guarantees and credit insurance and forms of risk transfer at origination such as syndication.
