{"id":52357,"date":"2025-10-21T12:10:29","date_gmt":"2025-10-21T10:10:29","guid":{"rendered":"https:\/\/www.altares.be\/?p=52357"},"modified":"2025-10-21T12:10:30","modified_gmt":"2025-10-21T10:10:30","slug":"data-driven-trade-credit-risk-using-insight-to-make-automated-decisions","status":"publish","type":"post","link":"https:\/\/www.altares.be\/en\/blog\/credit-risk-management\/datagedreven-trade-credit-risk-met-inzicht-naar-automatische-beslissingen\/","title":{"rendered":"Data-driven trade credit risk: using insight to make automated decisions"},"content":{"rendered":"<p>Modern organizations distinguish themselves in trade credit risk management through the use of real-time data and automated processes. Financial screening and risk management are no longer separate tasks: they are fully integrated within CRM and ERP systems. This creates a continuous, data-driven risk and compliance approach throughout the entire customer life cycle.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img fetchpriority=\"high\" decoding=\"async\" width=\"880\" height=\"472\" src=\"https:\/\/www.altares.be\/wp-content\/uploads\/2025\/10\/datagedreven-trade-credit-risk.webp\" alt=\"verkeerslicht\" class=\"wp-image-52358\" srcset=\"https:\/\/www.altares.be\/wp-content\/uploads\/2025\/10\/datagedreven-trade-credit-risk.webp 880w, https:\/\/www.altares.be\/wp-content\/uploads\/2025\/10\/datagedreven-trade-credit-risk-300x161.webp 300w, https:\/\/www.altares.be\/wp-content\/uploads\/2025\/10\/datagedreven-trade-credit-risk-150x80.webp 150w, https:\/\/www.altares.be\/wp-content\/uploads\/2025\/10\/datagedreven-trade-credit-risk-768x412.webp 768w, https:\/\/www.altares.be\/wp-content\/uploads\/2025\/10\/datagedreven-trade-credit-risk-18x10.webp 18w, https:\/\/www.altares.be\/wp-content\/uploads\/2025\/10\/datagedreven-trade-credit-risk-24x13.webp 24w, https:\/\/www.altares.be\/wp-content\/uploads\/2025\/10\/datagedreven-trade-credit-risk-36x19.webp 36w, https:\/\/www.altares.be\/wp-content\/uploads\/2025\/10\/datagedreven-trade-credit-risk-48x26.webp 48w\" sizes=\"(max-width: 880px) 100vw, 880px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">One central, real-time overview<\/h2>\n\n\n\n<p>All core data creditworthiness, payment behavior, outstanding balances, and risk signals are automatically collected and processed. This ensures that every department works with the same up-to-date information, making decisions faster, more consistent, and less prone to error. And that goes beyond just the credit control department. Marketing, sales, and finance also benefit from integrated availability in systems. All risk signals can be used to optimize customer lifetime value. All of the departments mentioned above have a responsibility in this regard.&nbsp;<\/p>\n\n\n\n<p><strong>Interesting read:<\/strong> <a href=\"https:\/\/www.altares.be\/en\/blog\/credit-risk-management\/the-biggest-challenges-in-trade-credit-risk-for-the-coming-years\/\">The biggest challenges in trade credit risk for the coming years<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Automated decisioning as a strategic advantage<\/h2>\n\n\n\n<p>The next step in professionalization is automated decisioning: decisions that are made instantly and automatically based on predefined rules and risk profiles. This not only speeds up decision-making, but also makes it more consistent. It reduces the margin for error and ensures scalability, as processes are no longer dependent on the availability and interpretation of individual employees.<\/p>\n\n\n\n<p><strong>Interesting read: <\/strong><a href=\"https:\/\/www.altares.be\/en\/blog\/credit-risk-management\/de-sleutel-tot-effectief-credit-risk-management-scope-schaalbaarheid-en-datakwaliteit\/\">The key to effective credit risk management: scope, scalability and data quality<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Examples of automated decisioning throughout the customer life cycle<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">1. Prospecting<\/h3>\n\n\n\n<p>During the prospect phase, companies are screened directly via external data sources (such as creditworthiness and company status). Leads with a high risk profile are automatically excluded or flagged.<\/p>\n\n\n\n<p>Value: Sales focuses exclusively on promising prospects, which improves the quality of the pipeline and reduces acquisition costs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2. Targeting<\/h3>\n\n\n\n<p>Marketing connects payment behavior and financial stability to customer segmentation. Campaigns are automatically targeted at customers with the highest chance of conversion and a reliable payment history.<\/p>\n\n\n\n<p>Value: Sales focuses exclusively on promising prospects, which improves the quality of the pipeline and reduces acquisition costs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3. Onboarding<\/h3>\n\n\n\n<p>During customer acceptance, automatic checks are performed on credit limits, compliance (sanctions lists, UBOs), and fraud indicators. Depending on the outcome, the customer is immediately approved, rejected, or submitted for manual review.<\/p>\n\n\n\n<p>Value: Fast onboarding, high percentage of automated acceptance, reduced risk of fraud, and improved regulatory compliance.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">4. Customer management<\/h3>\n\n\n\n<p>Real-time monitoring of credit limits and payment behavior automatically determines the conditions for new orders or contract renewals. Any deviations trigger workflows directed toward sales or credit management.<\/p>\n\n\n\n<p>Value: Healthy customers can grow, limits are adjusted in a timely manner, risks remain under control.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">5. Collections<\/h3>\n\n\n\n<p>For outstanding invoices, reminders are automatically sent and collection processes are initiated, depending on the customer's payment history and risk profile. Customers who require personal attention receive it; the rest is fully automated. This allows human resources to be deployed in a much more targeted manner.<\/p>\n\n\n\n<p>Value: Faster and more consistent payment collection, less pressure on cash flow, and less manual follow-up required.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">6. Recovery<\/h3>\n\n\n\n<p>For customers experiencing financial difficulties, the most appropriate course of action is automatically determined: payment plan, transfer to a collection agency, or termination of the partnership. All steps are recorded in the CRM or ERP system for transparency.<\/p>\n\n\n\n<p>Value: More efficient risk management, maintaining valuable customer relationships where possible, and minimizing losses.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">From reactive to proactive customer management<\/h2>\n\n\n\n<p>Through automated decision-making at every stage of the customer lifecycle, organizations are moving from reactive to proactive management. They identify risks before they cause problems and capitalize on growth opportunities more quickly. The result: an organization that is not only more efficient, but also more resilient and future-proof.<\/p>\n\n\n\n<p class=\"translation-block\">Automation is only as strong as the data that feeds it. With Altares Dun &amp; Bradstreet's business information, credit ratings, and risk signals, organizations can take their trade credit risk management to the next level. By integrating this data directly into CRM and ERP systems, risks become visible in real time and decisions are better informed. In this way, Altares Dun &amp; Bradstreet helps companies act faster, limit risks, and grow with confidence in an ever-changing market. Read more about our credit risk solutions <a href=\"https:\/\/www.altares.be\/en\/credit-risk-management\/\" id=\"loggerlink_50639837\" target=\"_self\">here<\/a>.<\/p>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>Successful organizations no longer make decisions based on gut feeling, but on data. With real-time insights and automated processes, trade credit risk management becomes faster, smarter, and more consistent. Discover how integrated decision-making in CRM and ERP systems reduces risks and increases growth opportunities.<\/p>","protected":false},"author":8,"featured_media":52358,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[21],"tags":[],"class_list":["post-52357","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-credit-risk-management"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Datagedreven trade credit risk: met inzicht naar automatische beslissingen - Altares<\/title>\n<meta name=\"description\" content=\"Ontdek hoe realtime data en automatisering trade credit risk management slimmer en effici\u00ebnter maken.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.altares.be\/en\/blog\/credit-risk-management\/data-driven-trade-credit-risk-using-insight-to-make-automated-decisions\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Datagedreven trade credit risk: met inzicht naar automatische beslissingen - 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