{"id":11364,"date":"2017-02-10T15:00:00","date_gmt":"2017-02-10T15:00:00","guid":{"rendered":"https:\/\/zapliance.com\/?p=11364"},"modified":"2022-08-26T14:13:42","modified_gmt":"2022-08-26T14:13:42","slug":"getting-serious-what-are-high-incoming-invoice-amounts","status":"publish","type":"post","link":"https:\/\/zapliance.com\/en\/blog\/getting-serious-what-are-high-incoming-invoice-amounts\/","title":{"rendered":"Getting serious: What are high incoming invoice amounts?"},"content":{"rendered":"\n<p>Part II of this series shows you how to use simple statistical considerations to determine which invoices from a vendor are exceptionally high and thus represent an statistical outlier.<\/p>\n\n\n\n<p>Part 2 of the series: \u201cVery high incoming invoice amount\u201d<\/p>\n\n\n\n<p>1.&nbsp;<a href=\"https:\/\/zapliance.com\/blog\/data-analytics-where-topmanagers-should-keep-an-eye-on?lang=en\">Where Topmanagers should keep an eye on<\/a><br><strong>2. Getting serious: What are high incoming invoices?<\/strong><br>3.&nbsp;<a href=\"https:\/\/zapliance.com\/blog\/analytics-of-high-incoming-invoices?lang=en\">Analytics of high incoming invoices<\/a><br>4.&nbsp;<a href=\"https:\/\/zapliance.com\/blog\/two-graphics-for-evaluating-high-incoming-invoices?lang=en\">Two graphics for evaluating high incoming invoices<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Outlier Detection made easy&nbsp;or what is a statistical outlier?<\/h2>\n\n\n\n<p>As mentioned in my&nbsp;<a href=\"https:\/\/zapliance.com\/com\/zapliance\/www\/en\/blog\/sap-data-analytics-keep-an-eye-on.html\">last article<\/a>, it is not the sorting of incoming invoices with high amounts&nbsp;but the consideration of statistical outliers.&nbsp;In order to assess what a statistical outlier is, one must look at the variation&nbsp;of a vendor\u2019s invoices by their mean value.&nbsp;The variation&nbsp;around the mean value is measured by the standard deviation.&nbsp;Now comes a formula and I hope I do not lose half the readership, as is often said when formulas come. But auditors are quite accustomed:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/zapliance.com\/wp-content\/uploads\/stddev-1.jpg\" alt=\"\" class=\"wp-image-7136\"\/><\/figure>\n\n\n\n<p>Where:<\/p>\n\n\n\n<p>n&nbsp;is the number of invoices and<br>X<sub>i<\/sub>&nbsp;is the i-th invoice of the&nbsp;vendor<br>X&nbsp;the mean value of the vendor\u2019s invoice&nbsp;amounts<\/p>\n\n\n\n<p>The simple idea for a statistical outlier is that an invoice with an amount that is greater than the average plus n times the standard deviation is considered an outlier and is thus unusually high.&nbsp;<\/p>\n\n\n\n<p>How high is&nbsp;<strong>n<\/strong>?<\/p>\n\n\n\n<p>Depending on n, you are going to classify an outlier.&nbsp;Luckily, the statistics is helping here too, because intuitively one can judge n hard. In order to approach the solution, we assume for the time being that the invoice amounts of the incoming invoices are normally distributed.&nbsp;In the normal distribution one can always deduce how the frequency of calculation amounts within an interval of mean value +\/- n times the standard deviation is:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/zapliance.com\/wp-content\/uploads\/normal-1.png\" alt=\"\" class=\"wp-image-7134\"\/><\/figure>\n\n\n\n<p>\u03c3 is determined as the standard deviation of the vendor invoice amounts.&nbsp;If you look at the graph, you see how many of the invoices would have to lie in an interval +\/- n times standard deviation:<\/p>\n\n\n\n<p>68.2% of the invoices in the interval mean value +\/- one&nbsp;standard deviation<\/p>\n\n\n\n<p>95.4 %&nbsp;of the&nbsp;invoices&nbsp;in the interval mean value +\/-&nbsp;two&nbsp;standard deviations<\/p>\n\n\n\n<p>99,7 %&nbsp;of the&nbsp;invoices&nbsp;in the interval mean value +\/-&nbsp;three&nbsp;standard deviations<\/p>\n\n\n\n<p>E.g. we could choose&nbsp;<strong>n<\/strong>&nbsp;= 3. That means: outliers would have to be (100-99.7) \/ 2 = 0.15% percent of the vendor\u2019s invoices.&nbsp;This provides an individual measure for outliers of a specific vendor.<\/p>\n\n\n\n<p>But watch out! Invoice amounts are often not distributed normally!&nbsp;Then the statements of the normal distribution with respect to&nbsp;<strong>n<\/strong>&nbsp;are also not valid.&nbsp;Finally, one must say that the probability distribution of the invoicing amounts is not always known.<\/p>\n\n\n\n<p>Can you still specify a rule for&nbsp;<strong>n<\/strong>&nbsp;even though you do not know the probability distribution of the invoice amounts?&nbsp;So to speak, a stable rule that is \u201cimmune\u201d against the ignorance of the probability distribution?<br><em>Yes we can!<\/em><\/p>\n\n\n\n<p>The so-called&nbsp;<a href=\"https:\/\/en.wikipedia.org\/wiki\/Chebyshev%27s_inequality\">inequality of&nbsp;Chebyshev<\/a>&nbsp;helps us to estimate probabilities without the assumption of distribution.&nbsp;The inequality of Chebyshev tells us how large the probability of invoice amounts&nbsp;is \u201cat most for invoice amounts&nbsp;larger mean +&nbsp;<strong>n<\/strong>&nbsp;times the standard deviation\u201d.&nbsp;This expresses the following formula:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/zapliance.com\/wp-content\/uploads\/tscheb-1.png\" alt=\"\" class=\"wp-image-7126\"\/><\/figure>\n\n\n\n<p>Where:<\/p>\n\n\n\n<p>\u03c3 is the determined standard deviation<\/p>\n\n\n\n<p>\u03bb&nbsp;stands for the n-fold<\/p>\n\n\n\n<p>This means the following:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td>n or&nbsp;\u03bb<\/td><td>The probability that an invoice amount&nbsp;is above n times the standard deviation above the mean value is at most<\/td><\/tr><tr><td>3<\/td><td>11,11%<\/td><\/tr><tr><td>4<\/td><td>6,25%<\/td><\/tr><tr><td>5<\/td><td>4,00%<\/td><\/tr><tr><td>6<\/td><td>2,77%<\/td><\/tr><tr><td>7<\/td><td>2,04%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>The right column indicates the probability that invoice amounts&nbsp;are above the mean plus n times standard deviation.&nbsp;However, this value is the highest possible probability. That is, depending on the actual (unknown) probability distribution, this probability is generally much smaller.<\/p>\n\n\n\n<p>My recommendation:<\/p>\n\n\n\n<p>I would take 6 times the standard deviation.<\/p>\n\n\n\n<p>So we come to our definition, which is an unusually high invoices amount:<\/p>\n\n\n\n<p><em>An incoming invoice from a vendor is unusually high if the invoice amount is above the mean value of the vendor\u2019s invoice amounts plus six times the standard deviation of the vendor\u2019s invoice amounts.<\/em><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How to get data for analytics&nbsp;of high input invoices?<\/h2>\n\n\n\n<p>If you want to analyse unusually high incoming invoices in your SAP, you need the necessary data structures and data from your SAP system.&nbsp;You can perform a data download from your SAP system using zapliance.<\/p>\n\n\n\n<p>The analytics of high incoming invoices are&nbsp;part of&nbsp;<a href=\"https:\/\/zapliance.com\/zap-audit?lang=en\">zap Audit<\/a>.&nbsp;<\/p>\n\n\n\n<p>In the next part of this series, we will show you how these analytics can be done in&nbsp;Excel and prepare a template for your analytics. You have any questions or suggestions? Then leave a comment.&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Part II of this series shows you how to use simple statistical considerations to determine which invoices from a vendor are exceptionally high and thus represent an statistical outlier. Part 2 of the series: \u201cVery high incoming invoice amount\u201d 1.&nbsp;Where Topmanagers should keep an eye on2. Getting serious: What are high incoming invoices?3.&nbsp;Analytics of high [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":10699,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[38,37,40],"tags":[],"class_list":["post-11364","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-en-audit","category-en-compliance","category-en-finance"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Getting serious: What are high incoming invoice amounts? - zapliance<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/zapliance.com\/en\/blog\/getting-serious-what-are-high-incoming-invoice-amounts\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Getting serious: What are high incoming invoice amounts? - zapliance\" \/>\n<meta property=\"og:description\" content=\"Part II of this series shows you how to use simple statistical considerations to determine which invoices from a vendor are exceptionally high and thus represent an statistical outlier. 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