Why does credit risk management need a change?
In the B2B environment, granting credit to customers is a strategic bet: it helps boost sales and strengthen relationships, but it also opens the door to credit risk. According to financial technology specialists, many industrial companies continue to manage this risk with manual processes and rigid models based on historical financial statements—an expensive, slow, and error-prone approach. Studies show that organisations that incorporate artificial intelligence (AI) into their financial processes manage to reduce bad debt losses and improve operational efficiency.
Limitations of the traditional model
Traditionally, credit risk assessment relies on trade references and audited balance sheets; credit lines are approved once and are rarely reviewed. This entails several limitations:
Slow and manual processes with incomplete data: Analysing documents and requesting references can take days.
Subjective criteria: The decision depends on the judgement of each analyst, which generates inconsistencies.
Lack of updating: Static models do not detect changes in payment behaviour in real time.
These problems explain why many companies adopt reactive measures and arrive late to the signs of deterioration in their portfolio. By operating in this way, credit and collections teams spend most of their working day on operational tasks and stop focusing on higher-risk customers.
How is AI redefining risk analysis?
Artificial intelligence combines Machine Learning, data analysis, and automation to provide a dynamic view of risk. Unlike static models, AI algorithms can incorporate financial data (liquidity, ageing, DSO), payment behaviour, purchasing volumes, information on protests and default registers, bank debt, etc. With these inputs, AI creates dynamic risk indicators that are updated daily as the data changes.
In addition to improving the quality of scoring, intelligent platforms send early warnings. Payment delays and any information (news, protests, etc.) about the entity are monitored daily to detect anomalous patterns and alert the credit team. AI acts as a trusted assistant to human judgement, allowing managers to act before a problem escalates.
What is credit risk mitigation and how does AI help?
Credit risk mitigation refers to the strategies adopted by companies to minimise the risk of losing money due to customer defaults. Examples include monitoring accounts after approval, analysing internal and external information daily, or limiting exposure by sector. Furthermore, by analysing large volumes of data and automatically updating credit scores, AI reduces late payments and helps companies react before the customer defaults.
What types of data does AI use to assess B2B credit risk?
Advanced analysis systems combine historical financial data, daily payment behaviour—not only with the company but with other companies anonymously—public information, and transaction patterns or online activity. The integration of heterogeneous sources allows for the creation of predictive models that evolve with each new piece of data. Yndika does this on a daily basis.
What real experiences in B2B companies did we see in the webinar?
The ECR Group case
In the webinar “AI applied to credit risk” organised by Atlax 360, Enrique Salinas, Head of Billing and Collections at ECR Group, recounted how his company embarked on their transformation in 2019. They sought to reduce the team’s operational burden and automate processes to decrease uncollectible debt. After evaluating several options, they chose Yndika, an AI platform that natively integrated with their ERP system (SAP) and combined onboarding, monitoring, and collections modules.
The implementation was straightforward; within a few months, the platform began generating default probability alerts and automating repetitive activities. With the crisis of the pandemic, this agility became critical: they were able to segment the portfolio, identify higher-risk customers, and redirect team resources towards analysing the most delicate cases. They now spend more time on strategy and less on manual tasks.
Qroma – Pinturas Tricolor
Gabriela Quijandría, Credit and Collections Coordinator at Qroma, explained that her company has a diverse customer portfolio, with different risks and sizes, and in countries with opposing economic conditions. For them, personalising the credit assessment process was key. By activating the onboarding module and working with Atlax 360 consultants, they defined entry parameters tailored to each business unit: specific financial ratios, weightings for each sector, and automatically proposed credit lines. The result was a flexible model capable of adjusting risk exposure and improving communication between the commercial and finance departments, reducing the credit assessment request, analysis, and response time by hours.
From reactive management to strategic risk control
The use of artificial intelligence in credit risk management is a competitive advantage that is within reach with Yndika. The experiences of companies like ECR Group and Qroma show that adopting intelligent solutions allows for transforming processes, identifying warning signs in time, and dedicating resources to the customers who truly need them.
Combined with a cultural shift and the integration of internal and external data, AI turns risk management into an agile, proactive, and strategic process. For organisations looking to grow sustainably in the B2B market, the strategy involves Atlax 360 implementing AI in an organised manner within their company.
You can watch the full webinar here.
