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AI central to credit management, but adoption gaps persist
AI adoption has accelerated dramatically across Australian businesses, with nearly 95 per cent reporting positive outcomes from using the technology, according to CreditorWatch.
AI is becoming an essential capability for credit managers as they contend with growing data volumes, tighter margins and heightened insolvency risk, a new report has found.
CreditorWatch’s newly published report, Implementing Best Practice AI for Credit Managers, has outlined how AI is now transforming complex credit data into clear, actionable insights, automating time-consuming or dull administrative tasks, identifying financial stress earlier, achieving more defensible decisions, and allowing credit managers to act earlier on risk.
This is freeing teams to target higher-value judgement and stakeholder engagement, the report said.
However, while 69 per cent of large Australian businesses operated with AI-integration, this was true for only a third of small businesses. Reportedly, this was holding back small firms, increasing their exposure to and vulnerability from cash flow and credit risks.
For Patrick Coghlan, chief executive of CreditorWatch, the way credit managers approach technology and risk management has shifted, and as a result, AI has moved beyond a “nice to have”.
“With almost all AI adopters reporting positive results, the greater risk now lies in standing still while others use AI to make faster, more informed and more proactive credit decisions.”
This widening adoption gap across the business landscape has created a capability imbalance with far-reaching economic risks and structural implications: those that fall behind the market are increasingly exposed to insolvency, client displacement, information asymmetry, market correlation, and governance gaps.
Small organisations, however, remained cautious about AI adoption, reportedly due to a lack of expertise, data security concerns, budget constraints and uncertainty about return on investment. The report suggested a responsible and staged approach to adoption rather than a large-scale transformation.
The report also outlined that transparent AI models would be crucial for ensuring accuracy and accountability. It highlighted the importance of human oversight, especially where credit decisions carry financial or reputational consequences.
According to Coghlan, “responsible AI adoption is about trust as much as technology”.
“When credit managers understand how insights are generated and how they should inform decisions, AI becomes a powerful support tool rather than a black box,” he added.
Again, regularly reviewing outputs and user feedback, and refining workflows ensures AI delivers despite shifts in market conditions or risk profiles.
The report concluded that the managers who implement AI appropriately will find improved efficiency, strengthened cash-flow resilience and earlier notification or warning signs.
AI adoption has accelerated dramatically across Australian businesses, with nearly 95 per cent reporting positive outcomes from using the technology, according to CreditorWatch.
AI is becoming an essential capability for credit managers as they contend with growing data volumes, tighter margins and heightened insolvency risk, a new report has found.
CreditorWatch’s newly published report, Implementing Best Practice AI for Credit Managers, has outlined how AI is now transforming complex credit data into clear, actionable insights, automating time-consuming or dull administrative tasks, identifying financial stress earlier, achieving more defensible decisions, and allowing credit managers to act earlier on risk.
This is freeing teams to target higher-value judgement and stakeholder engagement, the report said.
However, while 69 per cent of large Australian businesses operated with AI-integration, this was true for only a third of small businesses. Reportedly, this was holding back small firms, increasing their exposure to and vulnerability from cash flow and credit risks.
For Patrick Coghlan, chief executive of CreditorWatch, the way credit managers approach technology and risk management has shifted, and as a result, AI has moved beyond a “nice to have”.
“With almost all AI adopters reporting positive results, the greater risk now lies in standing still while others use AI to make faster, more informed and more proactive credit decisions.”
This widening adoption gap across the business landscape has created a capability imbalance with far-reaching economic risks and structural implications: those that fall behind the market are increasingly exposed to insolvency, client displacement, information asymmetry, market correlation, and governance gaps.
Small organisations, however, remained cautious about AI adoption, reportedly due to a lack of expertise, data security concerns, budget constraints and uncertainty about return on investment. The report suggested a responsible and staged approach to adoption rather than a large-scale transformation.
The report also outlined that transparent AI models would be crucial for ensuring accuracy and accountability. It highlighted the importance of human oversight, especially where credit decisions carry financial or reputational consequences.
According to Coghlan, “responsible AI adoption is about trust as much as technology”.
“When credit managers understand how insights are generated and how they should inform decisions, AI becomes a powerful support tool rather than a black box,” he added.
Again, regularly reviewing outputs and user feedback, and refining workflows ensures AI delivers despite shifts in market conditions or risk profiles.
The report concluded that the managers who implement AI appropriately will find improved efficiency, strengthened cash-flow resilience and earlier notification or warning signs.
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