Learning from Neighbors: Agricultural Bankers from Burkina Faso and Mali visit Senegal
Burkina Faso, Mali, and Senegal have many things in common. They are all countries located in West Africa; they share the same official language (French), the same currency (CFA), produce the same crops (cotton, rice, millet, sorghum, maize, etc.), and have a similar financial sector set-up. Given all of the above, agricultural bankers from these countries have to deal with many common challenges and sometimes the best solutions may be found just across the border.
That certainly seemed to be the case with senior bankers from Burkina Faso and Mali who traveled last month to Senegal to learn from Crédit Mutuel du Sénégal’s (CMS) experience in using the Agricultural Loan Evaluation System (ALES) to assess its SME clients. In French, the system is called Programme d'évaluation des crédits (PEC) and was originally developed by the Frankfurt School of Finance & Management and is currently being utilized in a number of countries including Senegal, Tajikistan, Ukraine, and Turkey.
The tool helps Financial Institutions (FIs) overcome challenges that they face when expanding their agricultural lending business. Agricultural production is very diverse and constantly variable, making it difficult for banks to apply pragmatic and efficient credit risk assessment procedures.
Expanding agricultural lending business: key challenges for FIs
Study Tour Approach
The first part of the study tour offered participants an opportunity to understand the algorithm of ALES and how the tool is used by loan officers. Among other things, participants learned about the type of information and data required by the tool, the various steps of analysis, and the importance and methodologies of updating the tool. Participants also tested the tool by completing the client questionnaire, they discussed and analyzed the evaluation report, and received practical understanding of the logic and calculations of ALES.
ALES is an agricultural scoring tool, supporting financial institutions to conduct reliable risk evaluations of agricultural credit applications. You may find additional information and the recording of an AgriFin Webinar hosted in February 2013 featuring the tool. You can also find a tool sample here.
The tool carries out individual client assessment and calculates the farmer’s most likely minimum income. This calculation is carried out on the basis of average regional yields. If the client’s calculated income is less than the bank’s risk management ratio of loan amounts versus calculated likely minimum income, the credit amount(s) will be reduced or refused.
ALES calculates the credit risk of farmer clients based on three variables:
When the farmer client approaches the bank for a credit application, he/she is provided a questionnaire which he/she completes before meeting with the credit officer. The credit officer enters the answers of the questionnaire into the CAP program (Client Assessment sheet) upon which the tool will automatically carry out the calculations and provide clear recommendations on the amount of credit(s) to be considered.
The second part offered participants an opportunity to simulate real-life experiences using the tool. This was done via role playing where some participants were the farmers asking for agricultural loans and the others loan officers entering the data into the tool.
Participants playing the farmer seeking a loan were disappointed when their seasonal loan was approved, but the medium-term loan for equipment rejected. The loan officers understood that it may not suffice to get an automated response and inform the client accordingly, but that some translation and explanation would be needed to avoid arguments and tensions, and ultimately the loss of the client.
The final part of the study tour sent participants to the field to experience first-hand how the data is collected and how it is used to update ALES. This part of the study tour highlighted, among other things, the importance of collecting production information from five or six different farmers or farmer groups for each crop in order for the tool to calculate reliable average production cost data.
ALES automatically calculates farmer client’s "most likely minimum income" on the basis of “average production costs” (for each crop cultivated). The “average production costs” are calculated based on tech cards in the ALES tool. Each crop production has its own tech card and up to 30 different crops cultivated in the region can be entered into the tool.
The data in the tech cards must be accurate and realistic, to provide reliable income calculations of the client. For this reason a minimum of 3-4 – and preferably 5-6 – individual tech cards must be filled in for each crop. On this basis, the ALES tool calculates the average production costs for the assessment of individual clients. Regular updating of the tech cards gives ongoing improvement of the calculation data, and support the accuracy of the client evaluations.
When working in the field with rice producers, the banker realized how difficult it is to get accurate data on production quantity and quality, the different locations of sale and their respective terms and conditions, especially the sales prices, the variation of prices over the year, and the logic underlying the risk-profit mix adopted by the farmers. They also saw the difficulties that farmers face in accessing the needed inputs and technology for their productions, as well marketing and storage systems for selling their products at better prices.
- The Study Tour enabled participants from BNDA, Mali to better understood the limitations of their tools on SME currently being designed within the bank, and how a tool for mass services could be distilled out of it to reduce processing time.
- Participants from CMS, Senegal got a deeper insight into the limitations of their new tool, and that loan officers must develop approaches to deal with applications that were rejected by the system.
- All participants understood the advantages of using an automated system to calculate parameters and scores based on the data entered. In certain cases, however, automated responses may be challenged, especially by clients from cooperative credit unions where they are also owners.
- In an environment where loan officers are motivated to expand lending, have more sympathy with their clients than with the financial institution, and will make special efforts to accommodate their requests, a program that does not permit loan officers to manipulate parameters and algorithms has important advantages over the alternative.
- All participants understood well that there could be different approaches to generating the initial stock of agricultural and economic data needed to establish a database for parameterized decision making. Participants also acknowledged that some approaches require regular updating of the database, involving additional costs for hiring of agronomists, an expense that not all institutions are prepared to shoulder, while other systems do generate such data as a by-product at almost zero costs.
- For larger loans, for example SMEs in the agriculture sector, the systems and tools should permit a more accurate assessment of the profitability and associated risks, gradually increasing with the loan amount processed. Each financial institution must seek its optimum between duration of processing, accuracy of prediction, amount reserved for loan processing, and number of cases treated per year.
Participants found the study tour to be an extremely valuable learning experience for them. While the ALES tool may not be a panacea to all of the issues they face, they believe that the lessons they learned have empowered them to make important improvements in their agricultural lending operations within their institutions.
For more information about ALES and if you would like to learn about its application by a financial institution in Turkey, click here to view video presentations from AgriFin's Financing Agriculture Forum 2013 in Colombo, Sri Lanka. You may also listen to a recording of an AgriFin Webinar hosted in February 2013 featuring the tool. You can also find a tool sample here.
If you are interested cooperating with the Frankfurt School of Finance and Management to develop ALES tool for your institution, please contact Katia Görtz, firstname.lastname@example.org.