{"id":963,"date":"2020-07-07T08:27:01","date_gmt":"2020-07-07T08:27:01","guid":{"rendered":"https:\/\/website.prod.unilu.spikeseed.cloud\/fr\/news\/how-machine-learning-can-help-assess-sme-credit-risk\/"},"modified":"2020-07-07T08:27:01","modified_gmt":"2020-07-07T08:27:01","slug":"how-machine-learning-can-help-assess-sme-credit-risk","status":"publish","type":"news","link":"https:\/\/www.uni.lu\/fr\/news\/how-machine-learning-can-help-assess-sme-credit-risk\/","title":{"rendered":"How Machine Learning Can Help Assess SME Credit Risk"},"content":{"rendered":"<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\"><p>A new partnership between SnT and Yoba Smart Money, a Luxembourgish technology start-up, will explore the use of machine learning to assess the credit risk associated with Small and Medium Enterprises (SMEs). Yoba Smart Money specialises in the research and development of risk management tools for the financial services sector using artificial intelligence.<\/p><p><strong>SME\u2019s Credit Challenges<\/strong><\/p><p>The ease with which consumers can access credit does not exist for <a href=\"https:\/\/ec.europa.eu\/growth\/smes\/business-friendly-environment\/sme-definition_en\" target=\"_blank\" title=\"\" rel=\"noopener\">SMEs<\/a>. Consumers can purchase almost anything on their credit card and easily obtain overdrafts, and they can do this because their payslip demonstrates their repayment capacity. For a typical consumer, once they have the paperwork, obtaining credit approval takes a matter of minutes. On the other side, the owners of SMEs live a very different reality that is frustrating and time-consuming.\u00a0 Few suppliers accept credit cards, and lenders need to assess their credit risk by thoroughly analysing their business, which is expensive and takes time, so overdrafts are less common.<\/p><p>This is a problem as many SMEs have payment cycles that drain cash flow. Payment terms are often based on industry norms outside of their control or dictated by large market participants. When a business has a month with slow sales it can be forced to delay the payment of invoices to the next month. This can jeopardise important supplier relationships as well as potentially causing a domino effect further along the supply chain. This has been particularly problematic during the COVID-19 crisis.<\/p><figure class=\"wp-block-dev4-reusable-blocks-image  object-fit--contain\">\n    \n<img decoding=\"async\" class=\"wp-block-image unilux-custom-image-block\"\n                alt=\"\"\n            src=\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2023\/07\/coffee_shop.jpg\"\n                    style=\"object-position: 50.00% 50.00%; font-family: &quot;object-fit: contain; object-position: 50.00% 50.00%;&quot;; aspect-ratio: 16\/9; object-fit: contain; width: 100%;\"\n        loading=\"lazy\"\n\/>    <\/figure><p><\/p><p><strong>Introducing Machine Learning<\/strong><\/p><p><a href=\"https:\/\/www.yoba.com\/\" target=\"_blank\" title=\"\" rel=\"noopener\">Yoba<\/a>\u2019s idea to solve this challenge for SMEs is to harness the wide availability of data today to support the assessment of credit risk for certain types of businesses. This is already possible in some countries, such as the Nordics, thanks to the availability of high-quality public data.\u00a0 A credit risk rating that provides an assessment of the credit worthiness of <a href=\"https:\/\/www.yoba.com\/post\/smes-pain-points-and-the-future\" target=\"_blank\" title=\"\" rel=\"noopener\">SMEs is crucial to create a truly efficient credit market<\/a>.<\/p><p><a href=\"https:\/\/www.yoba.com\/\" target=\"_blank\" title=\"\" rel=\"noopener\">Yoba<\/a> has therefore initiated a <a href=\"https:\/\/wwwfr.uni.lu\/snt\/research\/sedan\" target=\"_blank\" title=\"\" rel=\"noopener\">joint project with SnT<\/a> to determine how traditional and alternative data sources could be used to perform automated credit analysis of SMEs in select European markets. Together with SnT researchers, Yoba will look at how technologies such as natural language processing, machine learning, and data modelling can be developed to be used for this purpose. The research project will determine the optimal data points that could be used to create an accurate credit rating, how that information about a client should be accessed and processed, and also create models that will allow the potential of an SME to default on a loan to be predicted.<\/p><p><strong>Changing the Conversation<\/strong><\/p><p>\u201cOne of the most frustrating things for entrepreneurs to experience is negative credit decisions, after waiting for that decision for months. Our proposed solution will assess customers automatically and in seconds,\u201d said <a href=\"https:\/\/www.linkedin.com\/in\/attesuominen\/\" target=\"_blank\" title=\"\" rel=\"noopener\">Atte Suominen<\/a>, CEO of Yoba Smart Money. \u201cThe current way of working is not sustainable, and we can see companies, especially now during Covid-19 times, struggling with access to working capital financing. Despite initiatives to solve this challenge, such as Government guarantees that are meant to create easier access to working capital for SMEs, banks still have the same processes and assessment methods, which is the root cause.\u201d<\/p><figure class=\"wp-block-dev4-reusable-blocks-image  object-fit--contain\">\n    \n<img decoding=\"async\" class=\"wp-block-image unilux-custom-image-block\"\n                alt=\"\"\n            src=\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2023\/07\/atte_ceo_yoba.png\"\n                    style=\"object-position: 50.00% 50.00%; font-family: &quot;object-fit: contain; object-position: 50.00% 50.00%;&quot;; aspect-ratio: 16\/9; object-fit: contain; width: 100%;\"\n        loading=\"lazy\"\n\/>            <p class=\"wp-block-dev4-reusable-blocks-image-caption\">\n            Atte Suominen, CEO of Yoba Smart Money        <\/p>\n    <\/figure><p><\/p><p>The <a href=\"https:\/\/www.yoba.com\/\" target=\"_blank\" title=\"\" rel=\"noopener\">Yoba Smart Money team<\/a> has a strong background in both banking and entrepreneurship and has seen how the bottleneck in credit assessment impacts the economy. Thanks to their extensive experience and knowledge of credit granting for consumers they are well placed to help SMEs in this domain.<\/p><p>\u201cWe believe that we can tackle the problem of SME credit best through collaboration with the financial services industry and technology partners, which is why we initiated this project with SnT,\u201d continued <a href=\"https:\/\/www.linkedin.com\/in\/attesuominen\/\" target=\"_blank\" title=\"\" rel=\"noopener\">Suominen<\/a>. \u201cIt is all about people with the right mindset and a professional way of working.\u201d<\/p><p>\u201cWorking on industrial projects such as this pushes our research further as it gives us access to the real-world testing and data we need,\u201d said <a href=\"https:\/\/wwwfr.uni.lu\/snt\/people\/mats_brorsson\" target=\"_blank\" title=\"\" rel=\"noopener\">Mats Brorsson<\/a>, SnT\u2019s principal investigator on the project. \u201cYoba\u2019s proposed solution is compelling and could change the way SMEs are able to do business, which is exciting as it means our research will have a direct impact on the day-to-day lives of many people.\u201d<\/p><\/div><\/section>","protected":false},"excerpt":{"rendered":"<p>A new partnership between SnT and Yoba Smart Money, a Luxembourgish technology start-up, will explore the use of machine learning to assess the credit risk associated with Small and Medium Enterprises (SMEs). Yoba Smart Money specialises in the research and development of risk management tools for the financial services sector using artificial intelligence.<\/p>\n","protected":false},"author":0,"featured_media":0,"template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false},"news-category":[4,3],"news-topic":[],"organisation":[184,194,226],"authorship":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How Machine Learning Can Help Assess SME Credit Risk - Universit\u00e9 du Luxembourg<\/title>\n<meta name=\"description\" content=\"A new partnership between SnT and Yoba Smart Money, a Luxembourgish technology start-up, will explore the use of machine learning to assess the credit risk associated with Small and Medium Enterprises (SMEs). 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