Agriculture Finance Support Facility

Transcription

1 FAQ: Including Psychometric Testing Into Credit Risk Assessment Amie Vaccaro and Julia Reichelstein of the Entrepreneurial Finance Lab (EFL) on Including Psychometric Testing Into Credit Risk Assessment Agriculture Finance Support Facility (AgriFin) Webinar: Participant Questions June 28, 2017 Agriculture Finance Support Facility Including Psychometric Testing Into Credit Risk Assessment 1

2 This digital finance topic examines the technological aspects for financial institutions who want to transition to digital technology for the purpose of increasing the scale of operations; namely to increase financial services without adding more staff or increasing costs. The Entrepreneurial Finance Lab s (EFL) use of testing psychometrics, or the objective measurement of skills and knowledge, abilities, attitudes, personality traits, and educational achievements, to predict credit worthiness and the ability to repay loans, is different from most other fintech currently available. The Frequently Asked Questions discussion presented here provides a degree of insight into how the psychometric test is applied and how EFL calculates results. 'FinTech' is an innovation that has the potential to expand financial access to underserved actors of the agriculture sector. Identifying behavior traits using a scientific process helps paint the picture of a person's ability to repay loans and the willingness to do so. The FAQ lists questions asked by participants who attended AgriFin s webinar on this subject. Three themes organize the questions: Agriculture, Psychometric Algorithm and Processing, and Miscellaneous. EFL provides credit-scoring technology for banks in emerging markets to improve access to finance. EFL uses alternative data and innovative technology to power lending in the world's leading financial institutions. Their application collects and analyzes non-traditional information about borrowers to better forecast their likelihood of default. Agriculture Related Questions Q: Has this product been used with rural clients engaged in the agriculture sector as well as urban entrepreneurs? Yes. EFL is currently working with Juhudi Kilimo in Kenya (an agricultural lender who only works with farmers in rural areas). We are at the beginning stages of rolling out the score to be used in active application decisions, but we ve seen strong model predictive power so far. Q: Have you tested your scoring for agri-primary producers (i.e. smallholders)? If yes what is the result of it? Please see above. Q: Is this relevant for most agricultural applications? It is relevant, though by itself not a sufficient risk score. We can think of risk as being introduced by two factors the individual level and the macro level. With agricultural lending, perhaps a larger percentage of the risk is introduced by the macro level (macro-shocks such as droughts, pests, regional instability, etc.). Psychometrics can provide insight only on the individual level (as we are assessing a person s character; their willingness to repay). Thus, it can be a useful 2

3 datapoint to help lenders understand the risk when making agricultural loans, but it is not sufficient to predict the macro-level risk farmers incur. Psychometric Algorithm and Processing Questions Q: How can you protect the SMS model when applicants find out the best answers to the survey? Is it possible to "game" or cheat the survey? In all of our psychometric assessment modules, there are no right answers. Each answer interacts with the other psychometric and demographic answers in the test, which means that if two people respond to the same question in the same way, that answer may affect each of their scores differently (because they have different answers to other questions, and are demographically not identical). Thus, it s not possible to game the survey. To stop too many people from seeing the same questions, we have a larger pool of questions that we pull from, and can randomize the questions and the question order, to ensure not everyone sees the same assessment. Q: Are the SMSs in English or local languages? The SMS will be in whatever language the people applying for credit speak. Currently, EFL offers the SMS in English and Swahili, but we can fairly easily translate it into other languages as well, depending on demand. Q: What personality traits do you use to assess willingness and ability to pay respectively? We try to capture and assess a wide variety of traits and characteristics that we have seen to be predictive of credit risk. While we don t share the full list, nor trait descriptions, we can share some examples. For willingness to repay, we assess traits like optimism, agency, attitude, control, etc. Some traits we evaluate can be used to measure both willingness and ability to repay, such as fluid intelligence, memory, business acumen, budgeting, etc. Q: I would like to understand a few of the common variables EFL is using in the algorithm (as opposed to data points collected). I understand there s intellectual property subjectivity, but 2-3 would help at least. We look at a wealth of data from each assessment capturing and analyzing both psychometric stated answers as well as behavioral metadata. The latter is data we collect on how someone answers the questions. Unfortunately, we can't share specific questions or variables that we capture. 3

4 Q: How long is the questionnaire? On average, we see assessment times around minutes for digital web assessments. The time depends on literacy and tech-comfortability. Additionally, the assessment is adaptive so it may be longer or shorter for each applicant. For the SMS assessment, it very much depends on how focused the applicant is. If they answer all the questions in one sitting, the test can be as short as 7 minutes. On average, applicants took around 30 minutes to complete the SMS assessment. Q: What proof is there that willingness to pay is the most important cause of default relative to ability to pay? The relative importance of willingness to pay versus other factors of risk (affordability, macroshocks, etc) depends on the type of product and the population segment. I think we see the willingness to pay to be more of a factor with small, digital, consumer loans, for example. Alternatively, for farmers who encounter macro-shocks, willingness to pay may be a less important factor of risk. Q: When the algorithm is used early to sort out potential clients, how do you keep compliant with customer protection laws and Treating Customers Fairly principles? We have worked in 30 countries with a wide range of regulations and consumer protection laws, and our experience with regulators has been positive. Regulators are looking for ways to include more people into formal financial systems and see the EFL methodology as a valuable and helpful way to do it. They also see that we've been doing this for ten years and have shown great results. We feel confident that we can address any regulatory concerns that may come up in your market. Miscellaneous Q: Is there a written document describing the Equity Bank Kenya case study, methods used and results? Beyond the slides from the webinar, not yet. We will share as it s available. Q: Are you finding certain geographic regions more successful than others? We find certain products and certain use cases more successful in some markets versus others, but across emerging markets including Latin America, Asia, and Africa we find access to formal credit due to lack of data an issue that EFL can help tackle. 4

5 Q: Where else has this product been tested apart from Kenya? In Africa specifically, our product is in use in Kenya, Ethiopia, Ghana, South Africa, Zimbabwe, Uganda, Rwanda, Malawi, Namibia and Zambia. Etc. Outside of Africa, we are working in India, Indonesia, Philippines, México, Perú, Ecuador, Guatemala, Argentina and more. 5