Autor: Nermin Sefić
How AI is changing credit risk management - expanded signals, continuous monitoring, the explainability problem, and regulatory compliance. An in-depth analysis.
How AI expands available signals, enables continuous monitoring, and why model explainability becomes as important as its accuracy.
When discussing artificial intelligence in a corporate context, the conversation almost inevitably turns toward generative models, chatbots, and content automation. Considerably less attention goes to a question that could have greater long-term financial impact for most medium and large organisations: how AI is changing the very nature of credit and counterparty risk management.
Classical credit risk assessment models, built on decades of statistical practice, rely on a relatively small number of structured variables - payment history, debt-to-equity ratio, liquidity indicators, external agency credit ratings. These models work well for stable, well-documented business entities with long histories, but systematically underperform for new, fast-growing, or structurally unusual business partners - precisely the categories organisations increasingly deal with in a globalised, rapidly changing economic environment.
The problem is not the mathematics of traditional models itself, but the limited breadth of data they rely on. A business partner founded two years ago, growing quickly but lacking sufficiently long history to generate a reliable credit rating through traditional agencies, represents a blind spot for organisations relying exclusively on classical models - despite real, measurable signals about their health potentially existing in other, less structured data sources.
Machine learning models, particularly those capable of processing unstructured data - text from public filings, payment pattern data from transaction records, activity on public registries, even signals from publicly available news and regulatory disclosures - enable significantly broader insight into a business partner's health than traditional models can offer.
This expanded data breadth carries dual value. First, it enables assessment of partners who would otherwise remain beyond the reach of traditional models due to lack of long history. Second, and perhaps more importantly, it enables earlier detection of deterioration among existing partners - AI models capable of tracking subtle changes in payment patterns, tone of public communications, or public registry activity can signal rising risk months before that deterioration would show up in traditional, quarterly-updated financial indicators.
It is worth noting that this early detection capability does not replace traditional analysis, but complements it. Organisations treating AI models as a complete replacement for human judgement, rather than an additional layer of information informing that judgement, risk a new kind of error - excessive reliance on a black box whose internal decision-making is not always transparent or explainable.
This brings us to one of the most important practical challenges in applying AI to credit risk assessment: the demand for explainability. When a traditional statistical model assesses risk as elevated, an analyst can relatively easily trace which specific factors - say, a liquidity ratio falling below a certain threshold - led to that assessment. With more complex machine learning models, particularly those based on deep neural networks, that path from input data to output risk assessment can be considerably less transparent.
This lack of transparency is not merely an academic concern. Regulatory frameworks across multiple jurisdictions increasingly explicitly require organisations to explain why a particular business partner was denied credit or a business relationship, especially when that decision significantly affects that partner's operations. A model unable to offer a comprehensible explanation of its own assessment creates regulatory risk regardless of how statistically accurate it may be.
This has spurred development of an entire subfield known as "explainable AI" (XAI), which attempts to build models whose decisions can be understandably translated into language a human analyst, and eventually a regulator, can follow and verify. Organisations selecting AI tools for credit risk assessment today should treat explainability as an equal criterion alongside pure predictive accuracy, not a secondary consideration addressed after the fact if a regulator raises a question.
One of the most valuable practical shifts AI enables is the transition from periodic to continuous credit risk monitoring. Traditionally, a business partner's credit risk assessment occurs at discrete points - when establishing the relationship, perhaps annually at contract renewal, or reactively when a concrete problem signal appears.
This periodic approach creates natural gaps in oversight - deterioration occurring between two scheduled assessments goes unnoticed until the next scheduled review, which could be months away. AI systems capable of continuously processing available signals - new public filings, changes in transaction patterns, public registry updates - enable a shift toward a model where risk is tracked nearly in real time, with automatic alerts when a defined concern threshold is crossed.
This shift also carries organisational implications extending beyond the technology itself. Continuous monitoring generates a significantly higher volume of signals than periodic review, requiring clearly defined escalation thresholds - not every mild signal deserves immediate human analyst intervention, since that would quickly lead to alert fatigue that ultimately reduces, rather than increases, the overall effectiveness of the monitoring system.
The technical capability of an AI model to generate precise, timely credit risk signals is worth little if the organisation lacks a clear structure for converting those signals into concrete action. This is an area where many organisations, despite significant investment in the AI technology itself, fail to realise the full value of that investment.
The practical question is: who is responsible when an AI system signals elevated risk at a key business partner? Does that person or team have clear authority to act - halting further transactions, requiring additional guarantees, escalating to senior management - or does the signal simply remain logged in the system without a clear owner to act on it?
Organisations most successful at integrating AI into credit risk management are typically those that clearly defined the organisational process before introducing the technology - who receives signals, what criteria they use for prioritisation, what response speed is expected, and to whom escalation occurs when a signal crosses a certain severity threshold. Technology without this organisational framework creates merely an additional data source, not a genuinely improved risk management system.
There is a subtle but real risk accompanying successful AI system implementation for credit risk assessment: gradual erosion of human critical judgement in favour of unquestioning trust in the model. When an AI system demonstrates consistently good accuracy for months or years, there is a natural tendency for human analysts to gradually stop questioning its recommendations, treating them as nearly infallible.
This pattern becomes particularly dangerous precisely at moments when critical human judgement is most needed - during unusual, structurally novel economic conditions for which the model may not have been trained. Machine learning models, however sophisticated, ultimately learn from historical data, and their ability to predict behaviour in genuinely novel, unprecedented situations remains uncertain.
Organisations wanting to avoid this risk must actively cultivate a culture treating AI recommendations as valuable but not infallible input, maintaining regular human verification practice - particularly for high-value or high-risk decisions, where the cost of misjudgement significantly outweighs the cost of additional time spent on human verification.
The regulatory framework governing AI use in financial decision-making is undergoing active evolution worldwide. The European Union, through its comprehensive approach to artificial intelligence regulation, classifies systems affecting credit access as high-risk applications, subject to stricter transparency, documentation, and human oversight requirements than low-risk AI applications.
Organisations implementing AI systems for credit risk assessment today should treat these regulatory requirements not as an obstacle to be circumvented through minimal compliance, but as a useful framework that, if genuinely applied, naturally leads toward more robust, reliable systems - documentation enabling a regulator to understand the model is the same documentation enabling an internal team to detect and correct problems before they become serious.
For organisations considering or already beginning AI integration into credit and counterparty risk assessment, several practical principles emerge from the experience of organisations that have already traversed this path. First, model explainability deserves equal attention to its predictive accuracy, not a secondary role addressed only when a regulator raises a question.
Second, continuous monitoring requires an equally carefully designed escalation process as the model itself - technical capability to generate signals without a clear organisational path for acting on those signals creates false security without genuine risk management improvement.
Third, human critical judgement must remain an active, not passive, part of the process, particularly for high-value decisions - this requires a deliberate organisational culture actively encouraging questioning of AI recommendations, not merely a formal policy permitting this on paper while practice gradually slides toward unquestioning acceptance.
GNK ASG d.o.o. monitors developments in this area as part of a broader approach to financial risk management within the GNK DINAMO Ltd. Group, in the conviction that AI represents a valuable but insufficient tool for credit risk assessment - genuine value stems from thoughtful integration of technology with robust organisational processes and sustained human critical judgement.
While precisely quantifying the advantage of AI-assisted risk assessment remains challenging due to contextual differences between organisations, available data suggests significant, measurable benefits for organisations that have thoughtfully implemented this transition. Early detection of partner creditworthiness deterioration - months before traditional, quarterly-updated indicators would signal it - directly translates into reduced exposure to bad receivables.
Equally important, expanded data breadth enables organisations to enter business relationships with partners who would otherwise remain beyond reach due to lack of long credit history - opening business opportunities a more conservative, exclusively traditional approach simply could not recognise as acceptable.
This dual benefit - reduced risk alongside expanded business opportunities - explains why an increasing number of organisations, despite genuine explainability and regulatory compliance challenges, continue investing in this technology as a long-term component of their risk management infrastructure.
The discussion so far has focused predominantly on assessing direct business partners - clients, suppliers with whom the organisation directly contracts. But AI also opens the possibility of extending risk assessment deeper into the supply chain, toward suppliers' suppliers, whose health directly affects the reliability of the direct partner, but traditionally remains entirely outside the organisation's field of view.
This capability - mapping and assessing risk across multiple supply chain tiers - becomes increasingly relevant as global supply networks grow in complexity, and geopolitical and climate risks increasingly cause disruptions originating deep within the chain, beyond the direct field of view of the organisation that ultimately feels the consequences.
Organisations investing in this expanded visibility, though requiring more significant initial investment in data collection and processing, position themselves for better prediction and mitigation of disruptions that would otherwise appear as complete surprises, despite their early signals having existed deep within the supply chain months before materialising as a visible problem for the organisation itself.
The final lesson emerging from the experience of organisations most successful at integrating AI into credit and counterparty risk management is that technology functions as an amplifier of existing organisational discipline, not a substitute for it. Organisations with already robust risk management processes see AI as a tool making those processes faster, broader, and more precise. Organisations lacking fundamental risk management discipline risk AI simply automating and accelerating existing weaknesses rather than correcting them.
This sets a clear priority sequence for organisations considering this transition: before investing in sophisticated AI technology, it is worth ensuring that fundamental organisational processes - clear decision ownership, defined escalation thresholds, a culture nurturing critical judgement rather than unquestioning acceptance - already exist or are being actively built in parallel with the technological investment.
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Cjelovit tekst i izvor: https://gnk-asg.hr/en/analyses/artificial-intelligence-credit-counterparty-risk/
Autor i urednička odgovornost: Nermin Sefić. Izdavač: GNK ASG d.o.o..
#GNKASG #GNKDINAMOLtd #NerminSefic #BusinessIntelligence #artificialintelligence #creditrisk #counterpartyrisk #NerminSefić #explainableAI
