{"id":4276,"date":"2020-09-15T10:27:33","date_gmt":"2020-09-15T09:27:33","guid":{"rendered":"https:\/\/www.alvantia.com\/?p=4276"},"modified":"2020-09-15T10:27:34","modified_gmt":"2020-09-15T09:27:34","slug":"data-management-in-trade-finance-do-we-really-make-the-most-of-the-opportunities-provided-by-technology","status":"publish","type":"post","link":"https:\/\/www.alvantia.com\/en\/data-management-in-trade-finance-do-we-really-make-the-most-of-the-opportunities-provided-by-technology\/","title":{"rendered":"Data management in trade finance: do we really make the most of the opportunities provided by technology?"},"content":{"rendered":"\n

There is a key aspect managing the financing of trade credit, in general, and reverse factoring, in particular, which is not always well designed and used, from a trade, operational and risk point of view, in contrast to other financing arrangements: big data<\/strong>.<\/p>\n\n\n\n\n\n\n\n

Financing\ncommercial credit means managing and processing a \u201ccell\u201d which contains a lot\nof information, sometimes more than it seems. We are not referring\nexclusively to the information associated with the cell itself (amount, period\nof time, debtor, etc.), but to the behaviour and the information that all cells\nas a whole are capable of providing us<\/strong>: signs of potential insolvency, cash\nflow needs, suitable price levels, readjustment of limits and capacities, more\nfavourable or suitable periods for proposing a specific action, etc.<\/p>\n\n\n\n

We cannot\ndiscover anything if, at this point, we talk about data management as a\nfundamental thrust of any service or product which has numerous customers or\nusers on the other side. That said, are we properly using the capacities\nthat technology offers us to draw correct conclusions on behaviour and\ntendencies when we refer to legal persons or professionals?<\/strong> Do we\nunderstand, think and rationalise from the same viewpoint as an SME or a\nself-employed worker? It is clear that this is not an exclusively technological\nmatter, but is also related to listening, learning, experience and extensive\nknowledge of the variables used to be able to interpret the foregoing\nbehaviour.<\/p>\n\n\n\n

Here, we refer to \u201cthinking as a company does every day\u201d in order to understand how it sets its priorities, understand its needs, learn about how it rationalises its decisions, know when certain messages and offers are appropriate and when these may be unwanted and annoying.<\/p>\n\n\n\n

This work\narea is so important that we can venture to say that providing and engaging\ntime and resources to this end is a key differentiating factor<\/strong> between the\noperators and entities. In general, we are dedicated to seeking \u201cthe sale\u201d, so\nwe do not stop to reflect and improve retention, the quality of the service and\nuse of the information buffer we have. It is important to measure responses,\nestablish quality criteria, and have SLAs and service indicators, but it is\nalso important to allocate time in our daily routines to understanding the behaviour\nof our customers, their needs, real priorities and way of assessing the\nservice, and anticipating what is important for a provider and when it is\nimportant.<\/p>\n\n\n\n

If we focus on the Reverse Factoring activity, we can see how the Spanish market, as indicated by the AEF statistics, makes over \u20ac160 billion of payments through this system every year. If we agree that the average amount of an invoice could be between \u20ac6,000 and \u20ac9,000, we would be able to simply deduce that in our market between 27 million and 18 million of invoices or payment orders use Reverse Factoring every year (let\u2019s say 23 million). On the other hand, if we study the amount of data and information we manage with every order processed (by accessing the standard Reverse Factoring format published by the AEF) and we add other relevant information about the process, price offer, acceptance or not of the offer, advance period, etc., we can also agree that we have no less than 20 fields\/pieces of data per order. In other words, the Spanish market handles at least 460 million pieces of data directly related to the management of Reverse Factoring programmes<\/strong>. <\/p>\n\n\n\n

We say\ndirectly because we can indirectly estimate that there are several hundred\nmillion more pieces of data, probably as rich and important as the initial\npieces of data. We refer to data regarding the operation of the company, its\nbalance sheet, activity, profit and loss statement and business sector. The\nvalue of the data itself from a Reverse Factoring programme put into context\nand in relation to the external or public information of a company can identify\nnumerous situations and behaviours to be able to adjust and tailor our offer to\nunexpected limits.<\/p>\n\n\n\n

The\nimportance of the methodical, systematic, intelligent and evolutionary\ncross-referencing, emulsion and mixing of all this information is essential if\nwe want to optimise the return of a service<\/strong> whose\nresults can become arbitrary or difficult to control and predict.<\/p>\n\n\n\n

The use of new\ntechnology applied<\/strong> to this end becomes essential when processing a horde of\ndata and information which seemingly move from independent axes. The \u201cevolutionary\nmachine learning<\/strong>\u201d methodology, which is continuously learning and\ncorrecting, provides us with evident conclusions and returns, but the route for\nimplementing and adjusting the operation of these tools is not trivial as it\nentails an extensive loading of applied technology and functional knowledge of\nthe product. <\/p>\n\n\n\n

Another\nfundamental axis of these works is to reach a \u201cmanageable simplification\u201d of\nthe conclusions<\/strong>. The processing of such information requires simple,\nsemi-automatic and continued action proposals. We are not referring to an\noccasional study or analysis which concludes a sporadic action. We understand\nthat this management must be part of the DNA of the service and the business\nmodel, and must therefore entail a methodology which enables continual\nimprovement when applied to the matter. <\/p>\n\n\n\n

Applying\nthese advanced data analysis systems to the business must enable us not only\nto adjust and improve our business expectation in terms of activity, but also\nto help us to anticipate decisions that revolve around other aspects<\/strong> to\nwhich we often pay little attention: systems of reassigning risk limits to\noptimise use of capacities, and optimise consumption, alerts and indicators for\nthe early detection of behaviours indicating unwanted situations, assessment of\noperational resource capacities assigned to customer or provider service, etc.<\/p>\n\n\n\n

Although we\nknow that the results and conclusions of these actions and mechanisms do not\nderive from an exact science, as we are speaking about behaviours, we believe\nthat the effort and dedication of these works, which are based on significant\nvolumes managed and processed, merit our attention and interest. <\/p>\n\n\n

<\/div>","protected":false},"excerpt":{"rendered":"

There is a key aspect managing the financing of trade credit, in general, and reverse factoring, in particular, which is not always well designed and used, from a trade, operational and risk point of view, in contrast to other financing arrangements: big data.<\/p>\n

Continue Reading<\/i><\/a><\/p>\n","protected":false},"author":5,"featured_media":4268,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[189,186,211,227,207,208,187,206],"tags":[347,9,18,294],"class_list":["post-4276","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-alvantia-es-en","category-alvantia-2","category-banca-en","category-banking","category-factoring-en","category-financiacion-comercial-en","category-technology","category-tecnologia-en","tag-big-data","tag-confirming","tag-factoring","tag-trade-finance","not-single"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t