{"id":99,"date":"2018-06-21T10:05:23","date_gmt":"2018-06-21T10:05:23","guid":{"rendered":"http:\/\/creditrisk.valuatum.com\/?page_id=99"},"modified":"2020-12-17T17:30:35","modified_gmt":"2020-12-17T14:30:35","slug":"credit-risk-assessment-methods","status":"publish","type":"page","link":"https:\/\/creditreports.dk\/da\/produkter\/kreditrisikovurdering\/","title":{"rendered":"Kreditrisikovurderings metod"},"content":{"rendered":"<div class=\"alignfull wp-block-ugb-header ugb-header ugb-f542533 ugb-header ugb-header--v3 ugb-header--design-plain ugb-main-block ugb-main-block--inner-wide ugb--has-block-background ugb--has-background-overlay\" id=\"\"><div class=\"ugb-inner-block\"><div class=\"ugb-block-content\"><div class=\"ugb-header__item\"><div class=\"ugb-content-wrapper\"><h1 class=\"ugb-header__title\">VORES KREDITRISIKOMODEL<\/h1><p class=\"ugb-header__subtitle\">Bag den tekniske implementering af CreditReports.dk er der mennesker med lang historie inden for konkursrisikovurderinger og kreditrisikoanalyser. Efter en grundig forskning har vi v\u00e6ret i stand til at udvikle moderne maskinl\u00e6ringsmodeller, der forudsiger kreditrisiko med stor pr\u00e6cision og genererer letforst\u00e5elige kreditvurderinger og resultater.<\/p><\/div><\/div><\/div><\/div><\/div>\n\n\n\n<div class=\"alignfull wp-block-ugb-container ugb-container ugb-6835083 ugb-container--v2 ugb-container--design-basic ugb-main-block ugb-main-block--inner-full ugb--has-block-background\" id=\"\"><div class=\"ugb-inner-block\"><div class=\"ugb-block-content\"><div class=\"ugb-container__wrapper ugb-6835083-wrapper ugb--shadow-0\"><div class=\"ugb-container__side\"><div class=\"ugb-container__content-wrapper ugb-6835083-content-wrapper\">\n<h2 class=\"wp-block-heading\" data-block-type=\"core\">Kernen i kreditrisikovurderingen<\/h2>\n\n\n\n<p data-block-type=\"core\">For at bestemme det valgte selskabs kreditrisiko bruger vi som hoved-input i vores vurdering b\u00e5de finansielle oplysninger samt branche- og virksomhedskarakteristika. Vores kreditrapport pr\u00e6senterer resultaterne detaljeret, men ogs\u00e5 ved hj\u00e6lp af intuitive virksomhedskreditvurderinger og resultater.<\/p>\n\n\n\n<p data-block-type=\"core\">Nedenfor forklares nogle af de vigtigste parametre for vores kreditrisikovurdering:<\/p>\n\n\n\n<p data-block-type=\"core\"><\/p>\n\n\n\n<ul class=\"wp-block-list\" data-block-type=\"core\"><li>Soliditet er den vigtigste faktor. Soliditet kan f.eks. beskrives med <strong>Kapitalandel<\/strong>forholdet mellem egenkapital og samlet balance.<\/li><li>Rentabilitet er grundlaget for forretningen. Hvis en virksomhed ikke kan tjene penge, bliver det sv\u00e6rere at tilbagebetale sine forpligtelser. Imidlertid kan rentabiliteten svinge af naturlige \u00e5rsager, s\u00e5 historie og tendens er ogs\u00e5 vigtig. Rentabilitet kan f.eks. beskrives med <strong>Afkast af aktiver (ROA)<\/strong>forholdet mellem EBIT og samlede aktiver.<\/li><li>Likviditet beskriver virksomhedens evne til at opfylde sine daglige forpligtelser. Det er \u00e5benbart interessant for debitorer. Likviditet kan beskrives med <strong>Hurtigforhold<\/strong>forholdet mellem likvide aktiver (kontanter osv.) og kortfristede forpligtelser.<\/li><li>Den <strong>branche<\/strong> , en virksomhed opererer i, p\u00e5virker ogs\u00e5 kreditrisikoen. Nogle industrier er mere p\u00e5virket af de makro\u00f8konomiske \u00e6ndringer end andre mere defensive industrier.<\/li><li><strong>Virksomhedens st\u00f8rrelse<\/strong> betyder noget: stor st\u00f8rrelse er typisk en indikator for tidligere succes, men vigtigere er det, at store virksomheder har bedre ressourcer til at overleve eksterne chok.<\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" data-block-type=\"core\">Hvorfor og hvordan vi bruger maskinindl\u00e6ring<\/h2>\n\n\n\n<p data-block-type=\"core\">Den aktuelle model bruger maskinindl\u00e6ring. For det f\u00f8rste har vi brugt vores viden og erfaring til at v\u00e6lge parametre, der bedst og omfattende korrelerer med konkurssandsynlighed. For det andet dannes og \u201ctr\u00e6nes\u201d modellen ved hj\u00e6lp af maskinl\u00e6ringsalgoritmer. Dette betyder, at computeren f\u00e5r mange historiske data om udvalgte parametre og konkurser. Ved at behandle dataene identificerer algoritmen korrelationer mellem inputvariabler (parametre) og det valgte resultat (konkurs).<br><\/p>\n\n\n\n<div class=\"wp-block-image\" data-block-type=\"core\"><figure class=\"alignright size-large is-resized\"><a href=\"https:\/\/creditreports.dk\/wp-content\/uploads\/sites\/9\/2020\/06\/ml_models.png\" target=\"_blank\" rel=\"noopener noreferrer\"><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/creditreports.dk\/wp-content\/uploads\/sites\/9\/2020\/06\/ml_models.png\" alt=\"En illustration af kreditrisikomodeller, der viser input, kreditvurderingsmodellen og dens output.\" class=\"wp-image-951\" width=\"365\" height=\"171\" title=\"En illustration af kreditrisikomodeller\" srcset=\"https:\/\/creditreports.dk\/wp-content\/uploads\/sites\/9\/2020\/06\/ml_models.png 572w, https:\/\/creditreports.dk\/wp-content\/uploads\/sites\/9\/2020\/06\/ml_models-300x142.png 300w\" sizes=\"(max-width: 365px) 100vw, 365px\" \/><\/a><figcaption>En illustration af kreditrisiko modeller, der viser eksempel input og output. \nModellen er baseret p\u00e5 store m\u00e6ngder historiske data ved hj\u00e6lp af maskinindl\u00e6ringsalgoritmer.  <\/figcaption><\/figure><\/div>\n\n\n\n<p data-block-type=\"core\">Den anden del ovenfor, udf\u00f8rt af maskinl\u00e6ringsalgoritmer, er noget, som intet menneske kunne udf\u00f8re manuelt. Desuden er algoritmen i stand til at justere v\u00e6gten af parametre for hver enkelt virksomhed baseret p\u00e5 andre parametre. For eksempel, er v\u00e6gten p\u00e5 likviditet meget h\u00f8jere for virksomheder, der taber end for dem, der er rentable. Dette er grunden til, at maskinindl\u00e6ring g\u00f8r, at modellen er s\u00e5 pr\u00e6cis som mulig med de tilg\u00e6ngelige oplysninger.<\/p>\n\n\n\n<p data-block-type=\"core\">Vores modeller er udviklet i samarbejde med Valuatum, og du kan l\u00e6se mere om modellerne p\u00e5 <a rel=\"noreferrer noopener\" href=\"https:\/\/www.valuatum.com\/credit-risk\/bankruptcy-risk\/\" target=\"_blank\">Valuatum webside<\/a>.<\/p>\n\n\n\n<p data-block-type=\"core\"><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" data-block-type=\"core\">Data om betalingsadf\u00e6rd og standardoplysninger<\/h2>\n\n\n\n<p data-block-type=\"core\">Vores model er fokuseret p\u00e5 mellemlang til langsigtede sandsynlighed for misligholdelse, og den er baseret p\u00e5 de sidste par \u00e5rs \u00e5rsregnskab. En virksomheds \u00f8konomiske situation kan dog pludselig forv\u00e6rres, efter at det sidste hel\u00e5rsregnskab er offentliggjort. Dette er sj\u00e6ldent, men kan ske p\u00e5 grund af en pludselig stor og enorm \u00e6ndring, s\u00e5som konkurs hos en st\u00f8rre leverand\u00f8r eller klient, tilbagetr\u00e6kning af st\u00f8rre importleverand\u00f8r eller en stor \u00e6ndring af iv\u00e6rks\u00e6tterens sundhedstilstand i en lille virksomhed.&nbsp;<\/p>\n\n\n\n<p data-block-type=\"core\">Derfor skal betalingsadf\u00e6rdsdata og standardoplysninger bruges parallelt med vores model, eller alternativt b\u00f8r virksomhedens situation ellers v\u00e6re kendt. Data om betalingsadf\u00e6rd viser, hvor mange dage virksomheden har om at betale sine fakturaer. Derfor er tilbagevendende langvarig faktureringstid normalt en indikator for betalingsvanskeligheder. Sm\u00e5 betalingsforsinkelser f\u00f8rer dog ikke n\u00f8dvendigvis til standardh\u00e6ndelser meget hurtigt. S\u00e5ledes kan en forl\u00e6ngelse af betalingstider betragtes som en tidlig advarsel. Derfor er betalingsadf\u00e6rdsdata mere omfattende end standardoplysninger. Disse oplysninger kan bruges til at supplere og opdatere den opfattelse af virksomheden, der er opn\u00e5et fra \u00e5rsregnskabet.<\/p>\n\n\n\n<p data-block-type=\"core\">CreditReports.dk er i \u00f8jeblikket i en beta-fase og under udvikling. Som s\u00e5dan indeholder den ikke data om betalingsadf\u00e6rd. Vi leder efter muligheder for at integrere disse data i vores rapport eller partnere, hvis data vi kan s\u00e6lge.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div>\n\n\n\n<p data-block-type=\"core\"><\/p>\n<style class=\"advgb-styles-renderer\">.ugb-f542533 .ugb-header__title{font-family:\"Poppins\",Sans-serif !important;font-size:56px;font-weight:500;letter-spacing:0.5px;text-align:left !important}.ugb-f542533 .ugb-inner-block{text-align:left}.ugb-f542533.ugb-header{justify-content:flex-start;background-color:#ffffff;background-image:url(https:\/\/d3gt1urn7320t9.cloudfront.net\/library\/block-cta-elevate-call-to-action\/block-background-background-media-url.jpg)}.ugb-f542533.ugb-header > .ugb-inner-block{width:906px !important;min-width:auto !important}.ugb-f542533.ugb-header:before{background-color:#ffffff;opacity:0.9}@media screen and (max-width:768px){.ugb-f542533.ugb-header{min-height:50vh !important}}.ugb-6835083-wrapper.ugb-container__wrapper{border-radius:0px !important}.ugb-6835083-wrapper > .ugb-container__side{align-items:center !important}.ugb-6835083-content-wrapper.ugb-container__content-wrapper{width:65% !important}.ugb-6835083.ugb-container{padding-right:0px !important;padding-left:0px !important}.ugb-6835083{padding:0 !important}@media screen and (max-width:768px){.ugb-6835083-content-wrapper.ugb-container__content-wrapper{width:100% !important}}<\/style>","protected":false},"excerpt":{"rendered":"<p>OUR CREDIT RISK MODEL Behind the technical implementation of CreditReports.dk are people with long history in bankruptcy risk estimation and credit risk analysis. As a result of thorough research we have been able to develop modern machine learning models that predict credit risk with great precision and generate easy to understand company credit ratings and scores. The core of credit risk assessment To determine the chosen company&#8217;s credit risk we use as main inputs in our assessment financial information as&hellip;<\/p>","protected":false},"author":1,"featured_media":0,"parent":15,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"om_disable_all_campaigns":false,"advgb_blocks_editor_width":"","advgb_blocks_columns_visual_guide":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-99","page","type-page","status-publish","hentry"],"aioseo_notices":[],"featured_image_urls_v2":{"full":"","thumbnail":"","medium":"","medium_large":"","large":"","1536x1536":"","2048x2048":"","trp-custom-language-flag":"","portfolio":""},"post_excerpt_stackable_v2":"<p>OUR CREDIT RISK MODELBehind the technical implementation of CreditReports.dk are people with long history in bankruptcy risk estimation and credit risk analysis. As a result of thorough research we have been able to develop modern machine learning models that predict credit risk with great precision and generate easy to understand company credit ratings and scores. The core of credit risk assessment To determine the chosen company&#8217;s credit risk we use as main inputs in our assessment financial information as well as industry and company characteristics. Our credit risk report presents the results in detail but also with the help of&hellip;<\/p>\n","category_list_v2":"","author_info_v2":{"name":"valuatum","url":"https:\/\/creditreports.dk\/da\/author\/valuatum\/"},"comments_num_v2":"0 comments","coauthors":[],"author_meta":{"author_link":"https:\/\/creditreports.dk\/da\/author\/valuatum\/","display_name":"valuatum"},"relative_dates":{"created":"Udgivet 8 \u00e5r siden","modified":"Opdateret 5 \u00e5r siden"},"absolute_dates":{"created":"Udgivet juni 21, 2018","modified":"Opdateret december 17, 2020"},"absolute_dates_time":{"created":"Udgivet juni 21, 2018 10:05 am","modified":"Opdateret december 17, 2020 5:30 pm"},"featured_img_caption":"","featured_img":false,"series_order":"","_links":{"self":[{"href":"https:\/\/creditreports.dk\/da\/wp-json\/wp\/v2\/pages\/99","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/creditreports.dk\/da\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/creditreports.dk\/da\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/creditreports.dk\/da\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/creditreports.dk\/da\/wp-json\/wp\/v2\/comments?post=99"}],"version-history":[{"count":9,"href":"https:\/\/creditreports.dk\/da\/wp-json\/wp\/v2\/pages\/99\/revisions"}],"predecessor-version":[{"id":1891,"href":"https:\/\/creditreports.dk\/da\/wp-json\/wp\/v2\/pages\/99\/revisions\/1891"}],"up":[{"embeddable":true,"href":"https:\/\/creditreports.dk\/da\/wp-json\/wp\/v2\/pages\/15"}],"wp:attachment":[{"href":"https:\/\/creditreports.dk\/da\/wp-json\/wp\/v2\/media?parent=99"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}