Agriculture Finance Support Facility

Transcription

1 Florentin Lenoir of Lenddo on Automated Credit Scoring Using Digital Data Agriculture Finance Support Facility (AgriFin) Webinar: Participant Questions May 10, 2017 Agriculture Finance Support Facility Automated Credit Scoring Using Digital Data 1

2 This digital finance topic examines the technological aspects for financial institutions who want to design and implement automated credit scoring. The Frequently Asked Questions discussion presented here provides a degree of insight into the feature types (mobile, network, and social media data) used to build predictive features that may help approve more applicants and reduce risk by combining traditional data with new data to improve existing score cards and complement traditional underwriting tools. 'FinTech' is an innovation that has the potential to expand financial access to underserved actors of the agriculture sector. The focus of this article is on Lenddo's experience using their technologies and products for increasing financial inclusion. Lenddo is a software-as-service company helping leading financial institutions globally, make more accurate and quicker decisions across the customer lifecycle and drive higher levels of growth and profitability. Big data analytics and automation are technologies helping mobilize the agricultural finance sector in urban and rural markets. The article lists questions asked by participants who attended the webinar. The questions are organized by theme, including Data Collection and Social Media Sourcing; Regulation/KYC Compliance; Focus on Agriculture; Education; Miscellaneous. Data Collection & Social Media Sourcing Q: Who extracts the different data sets? The client or Lenddo? Lenddo would be extracting information directly from social media (FaceBook, LinkedIn, etc.), (Gmail, Outlook, Microsoft) or smartphone (Android, ios). The internal data (client data, demographic data, transaction history, etc.) will be shared by the client of Lenddo. Lastly, any data from third party providers (Telco, Credit Bureau, etc) could be extracted by either Lenddo or the client, whoever has the relationship and the connection (APIs). For instance, we either work directly with FICO and Experian, or build an API for client to access credit Bureau data through their platform. Q: How do you see social media data sources evolving? Increasingly in Africa, people are moving to closed groups with encrypted messages. It is important not limit oneself to just one type of data source. We continue to explore new possibilities to access data. For example, currently, WhatsApp doesn t seem to have an API that would be available for us to use, but Viber and other messaging apps start have some that we are analyzing. FinTech must always reinvent itself and innovate to keep providing services. Q: For customers with feature phones, how would you go about mining data? We have a few options available for featured phones that are currently being evaluated (for instance, psychometric tests through features phone). 2

3 Q: In areas absent of a Credit Bureau but with active Facebook users, can this model work? The model would look at targeting mobile deployment using demographic data plus android data, which we know could be quite predictive. Q: We have seen in some cases that the difficulty is not the analytics, but actually getting data. Specifically: 1) The cost of accessing data. How costly can data sources be for customer acquisition? This is a valid question and a case to case basis. Some data would be accessible without any additional cost, some would need to be bought. We would need to do some cost analysis to evaluate what make sense. A new business model could also be defined (revenue sharing for instance). 2) Is there any way of knowing the cost/benefit of specific data streams so that we know what is nice to have vs vital for scoring? The most vital data streams for Lenddo would be /android. That can easily be combined with demographic data and offers a robust starting point. Q: What about the difficulty of accessing the data, both in getting partners like MNO s to share data, but also around making disparate data sources workable for a single model. How much energy has to go into cleaning and restructuring data to make it workable? (i.e. is there a very specific set of conditions around data availability that make this workable?) This is a hard question to answer, as it will really depend on a case to case basis. We consider that we need about 2 to 4 weeks to integrate with other data sources. However, it could take more time to have a party agree on sharing information. Q: How exactly is the data sourced? Do you have agreements with Telco companies etc., and how does that relate to privacy considerations? Some of the data (Facebook, smartphone/android, ) would be captured through our platform with an opt-in process, the users agreeing explicitly to share their data. Telco data (CDR, etc.) would need to be accessed through an agreement with the Telco. Data privacy is a valid consideration and should be addressed carefully. FinTech providers must make sure to comply to the rules and regulations that apply to a partner or to the country where they operate. 3

4 Regulation/KYC Compliance Q: Do you have Blockchain, KYC and biometric authentication examples? Our verification solution will assist in doing KYC authentication but does not replace the KYC procedures in place in a country. Blockchain remain a complex technology that regulators have a hard time to fully understand. Regulations in general move slower than technology. Q: How are regulators looking at your credit score model and what barriers have you found for adopting your model in Asia? We have done, and still do, significant work to help regulators understand the value of the data we use, and why it should be used in credit scoring. We believe that there should be regulations in place to ensure the protection of the users and their data. In Asia, all countries have very different regulations and we have to remain flexible to be able to adjust our architecture to satisfy each need and requirement. Focus on Agriculture Q: Credit Bureaus are important, but not perfect in developing countries and agriculture tends to be even farther away from being accurately covered. Social media activity is different challenge. What parts of the Lenddo model could be agri-relevant? As more and more farmers tend to have access to smartphones, we believe that there would be a strong value proposition in using demographic information and smartphone data. There are also agri-specific data points that we could consider using (form filling analytics, agri-focus fields in the application form) and that should be investigated. One of the key aspect in not so much in the data, but in the way the data are being captured. Making sure the application process is being done digitally is the first step towards building predictive models for farmers. Q: Most banks want to lend to Farmer organizations/co-ops as opposed to individual farmers. However, farmers are geographically organized, not necessarily forming groups with "similar" friends. Any ideas as to how to build the Lenddo model? Although who is in your network would be predictive of your own creditworthiness, we would be looking at each applicant independently of their group or network, based on the features we have identified in the webinar presentation. 4

5 Q: What are your thoughts about what this model adds in value for small farmers who do not have mobile phones, who have little access to markets, and who have little social capital recorded digitally, etc? Yes, the approach presented in the webinar assumed that there was access to a population that is at a minimum, digitally active. This being said, there are solutions (either from Lenddo or other providers) that would address the specific needs of these small farmers, with little access to a device or market. In the future, we believe that the smartphone penetration will keep increasing and allow more and more of this population to be given financial services. Q: What about the price variability of agriculture output which impacts the repayment capability? This is a valid input and will be taken into consideration when we deploy our models. We also believe that this element should also be analyzed at the Financial institution level. Q: If I have an app that collects agro-data, would I be able to use your credit scoring to offer more value for my financial institutional clients? It would be valuable to see what data you already collect and how we could combine it with other data sources. We would embed our software development kit (SDK) in your mobile app to capture specific data and evaluate what could be built from there. Q: It is possible to use this platform with meteorological parameters in order to understand how the climate will be affected in the short term to mitigate the risk in the agriculture portfolio? Some technology providers are using that information. We have yet to fully analyze it at Lenddo but believe there is value in being able to use such data. Education Q: How do you help users who may have low levels of literacy or digital literacy to understand the privacy implications? We encourage the financial institutions to promote financial education along with the services they offer. When we provided lending services, we used to have a lot of marketing around making sure the applicants are fully aware of the implications, and what they need to trade off to access better financial services. Lenddo only works in the background after securing the consent from the users, the frontend will be handled by financial institutions. 5

6 Miscellaneous Q: Does the repayment behavior analysis take into account the country/ethnic group? Yes, we customize all our models based on country, targeted population, product offered, etc. Q: How does this data collection and its interpretations, take care of different behavioral patterns that exist, specifically for India? We have different model for different use cases. We have built an Indian model, and are building Indian models to address specific use cases. We would test each of the 300+ features we have built and keep building to find the ones that are the most predictive and build models with those features. Q: I note you are not in Thailand, why? We have had several discussions with banks and other financial institutions in Thailand and believe we will be live within the next three to six months. Q: How does Lenddo make sure to avoid "Algoracism" namely to ensure that certain segments of underserved/unserved population are not discriminated through using alternative data and innovative credit scoring? Good question that would probably need its own webinar! The data we look at (mainly meta data around messages) are analyzing behavioral patterns and would not take into consideration any discriminatory features or information. You also have a lot of regulation in place that should prevent this from happening (Fair Lending Act). Q: How transparent is the Lenddo "box" compared to say a traditional score in which a borrower knows what are the ratios and what to do to improve its score? We try not to provide a black box to the financial institutions, but instead, make sure to build custom models and to explain, either to financial institutions or regulators, the variables we use and why it is predictive. We would collect repayment data to assess the performance of our models and define how we could improve them. 6