{"id":23484,"date":"2025-03-19T09:25:38","date_gmt":"2025-03-19T09:25:38","guid":{"rendered":"https:\/\/www.airony.ai\/staging\/?post_type=neuros_case_study&p=23484"},"modified":"2025-04-10T20:29:56","modified_gmt":"2025-04-10T20:29:56","slug":"automated-debtors-risk-management","status":"publish","type":"neuros_case_study","link":"https:\/\/www.airony.ai\/en\/case-studies\/automated-debtors-risk-management\/","title":{"rendered":"Automated Debtors Risk Management"},"content":{"rendered":"
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Automated Debtors Risk Management<\/span><\/h1><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The challenge<\/span><\/h2><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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From large financial institutions to corporate groups, to companies that manage periodic payments from their debtors or customers: all can encounter difficulties in monitoring the creditworthiness of debtors, whose behavior can suddenly change due to crises, both personal and macroeconomic.<\/strong><\/p>

The lack of effective monitoring can quickly lead to liquidity management problems, with the risk of failing to properly plan revenue. Furthermore, long debt collection times can negatively impact the net present value of the contract portfolio, resulting in a loss of value and, in the worst cases, actual erosion of company assets.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The solution<\/span><\/h2><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The solution is based on an Artificial Intelligence engine that analyzes past cash flows and integrates internal and external data to predict payment delays, estimate the value of the contract portfolio, and assess the financial reliability of new customers.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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  • Artificial Intelligence-based engine capable of analyzing historical cash flows and integrating them with master variables (e.g., personal data) and macroeconomic data to predict payment delays and flag the most significant cases.<\/li>
  • Estimate the value of a company's entire contract portfolio, based on future cash flows predicted by the AI engine.<\/li>
  • Evaluate the financial reliability of prospects and assign a predictive rating, developed starting from a minimum set of data collected during the onboarding phase.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we'll augment our intelligence.<\/p>CEO of IBM<\/cite><\/blockquote><\/figure>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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    The benefits<\/span><\/h2><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    The solution allows you to evaluate long-term mortgages and supply contracts, estimating expected collection times and the net present value of the entire company portfolio. It also integrates a classification system that can predict the financial reliability of a new customer (prospect). These features save valuable time normally spent on analysis, evaluation, and monitoring activities.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    • Optimize internal resources: Reduce the time and effort required to assess customer creditworthiness and monitor credit portfolio risk.<\/li>
    • Greater accuracy in financial planning: Allows you to accurately forecast future cash flows and improve company liquidity management.<\/li>
    • Financial risk reduction: allows for timely intervention in critical positions, minimizing losses and preserving the value of assets over time.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

      From large financial institutions to corporate groups, to companies that manage periodic payments from their debtors or customers, all can encounter difficulties in monitoring the creditworthiness of debtors, whose behaviors can suddenly change due to events\u2026<\/p>","protected":false},"author":6,"featured_media":23763,"template":"","neuros_case_study_category":[],"neuros_case_study_tag":[59,63],"class_list":["post-23484","neuros_case_study","type-neuros_case_study","status-publish","has-post-thumbnail","hentry","neuros_case_study_tag-solutions","neuros_case_study_tag-urban"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.airony.ai\/en\/wp-json\/wp\/v2\/neuros_case_study\/23484","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.airony.ai\/en\/wp-json\/wp\/v2\/neuros_case_study"}],"about":[{"href":"https:\/\/www.airony.ai\/en\/wp-json\/wp\/v2\/types\/neuros_case_study"}],"author":[{"embeddable":true,"href":"https:\/\/www.airony.ai\/en\/wp-json\/wp\/v2\/users\/6"}],"version-history":[{"count":31,"href":"https:\/\/www.airony.ai\/en\/wp-json\/wp\/v2\/neuros_case_study\/23484\/revisions"}],"predecessor-version":[{"id":23523,"href":"https:\/\/www.airony.ai\/en\/wp-json\/wp\/v2\/neuros_case_study\/23484\/revisions\/23523"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.airony.ai\/en\/wp-json\/wp\/v2\/media\/23763"}],"wp:attachment":[{"href":"https:\/\/www.airony.ai\/en\/wp-json\/wp\/v2\/media?parent=23484"}],"wp:term":[{"taxonomy":"neuros_case_study_category","embeddable":true,"href":"https:\/\/www.airony.ai\/en\/wp-json\/wp\/v2\/neuros_case_study_category?post=23484"},{"taxonomy":"neuros_case_study_tag","embeddable":true,"href":"https:\/\/www.airony.ai\/en\/wp-json\/wp\/v2\/neuros_case_study_tag?post=23484"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}