SCBX is testing AI-assisted market conduct monitoring to analyze eligible customer-interaction transcripts for required disclosures, responsible sales behavior, and professional debt-collection practices. In the current proof of concept, Automatic Speech Recognition converts recorded AutoX conversations to text and an AI model checks them against defined conduct criteria. Results are emailed to service agents in an average of 5–8 minutes, compared with traditional mystery-shopper feedback that may take days or one to two weeks. CardX applies a related approach to identify missing explanations and professionalism issues in debt-collection conversations.
AI Powered Market Conduct Monitoring
To address these challenges, SCBX explored how large language models and AI driven analysis could automate the monitoring of customer interactions while maintaining regulatory standards.
Instead of evaluating only a limited number of interactions through manual audits, the system analyzes conversation transcripts and automatically evaluates whether agents follow required conduct guidelines.
This approach enables institutions to monitor interactions continuously and at scale, providing a far more complete picture of customer treatment, and delivering feedback fast enough to be genuinely useful.
Example Applications
AutoX: Market Conduct Detection
For AutoX, the system focuses on ensuring that financial products such as loans and insurance are offered in a responsible and transparent manner.
When an agent interacts with a customer, the conversation is captured through the agent’s smart device. The system then uses Automatic Speech Recognition (ASR) technology to transcribe the voice conversation into text in real time. Once transcribed, the conversation is analyzed by an AI model that checks whether the interaction follows the Market Conduct standards required by the Bank of Thailand.
The system evaluates whether agents clearly explain product terms, disclose risks appropriately, and avoid coercive or misleading sales tactics.
Unlike traditional manual audits that review only a small sample of calls, the AI system can monitor every recorded interaction, significantly improving compliance coverage and reducing regulatory risk.
Near real-time feedback for agents
Another key advantage, and arguably one of the most impactful, is the dramatic reduction in feedback time. In traditional mystery shopper processes, it can take days or weeks for a compliance issue to surface, travel up through managerial reviews, and eventually reach the agent. By that point, the agent may have forgotten the details of the interaction, making the feedback far less actionable.
Our current proof of concept demonstrates an AI response time of just a few minutes, averaging 5 to 8 minutes per interaction, with the potential to be significantly faster as execution capacity scales. Results are sent directly to the service agent’s email, allowing them to review their performance while the interaction is still fresh in their mind.
This shift from weeks to minutes transforms feedback from a retrospective audit into a real-time learning loop. Agents can recognize and correct mistakes almost immediately, which makes coaching more effective and behavioral improvement more sustainable over time.
CardX: Quality Control in Debt Collection
For CardX, the focus is ensuring that debt collection interactions remain respectful, professional, and compliant with legal standards.
The system analyzes conversation content and tone to determine whether collectors maintain appropriate professionalism and provide required explanations to customers. If required disclosures or explanations are missing, the system can flag these issues and alert supervisors.
By automatically generating performance reports and behavioral scores, management gains better visibility into service quality and can provide more targeted coaching to improve customer experience.
AI Powered Market Conduct Monitoring
To address these challenges, SCBX explored how large language models and AI driven analysis could automate the monitoring of customer interactions while maintaining regulatory standards.
Instead of evaluating only a limited number of interactions through manual audits, the system analyzes conversation transcripts and automatically evaluates whether agents follow required conduct guidelines.
This approach enables institutions to monitor interactions continuously and at scale, providing a far more complete picture of customer treatment, and delivering feedback fast enough to be genuinely useful.
Example Applications
AutoX: Market Conduct Detection
For AutoX, the system focuses on ensuring that financial products such as loans and insurance are offered in a responsible and transparent manner.
When an agent interacts with a customer, the conversation is captured through the agent’s smart device. The system then uses Automatic Speech Recognition (ASR) technology to transcribe the voice conversation into text in real time. Once transcribed, the conversation is analyzed by an AI model that checks whether the interaction follows the Market Conduct standards required by the Bank of Thailand.
The system evaluates whether agents clearly explain product terms, disclose risks appropriately, and avoid coercive or misleading sales tactics.
Unlike traditional manual audits that review only a small sample of calls, the AI system can monitor every recorded interaction, significantly improving compliance coverage and reducing regulatory risk.
Near real-time feedback for agents
Another key advantage, and arguably one of the most impactful, is the dramatic reduction in feedback time. In traditional mystery shopper processes, it can take days or weeks for a compliance issue to surface, travel up through managerial reviews, and eventually reach the agent. By that point, the agent may have forgotten the details of the interaction, making the feedback far less actionable.
Our current proof of concept demonstrates an AI response time of just a few minutes, averaging 5 to 8 minutes per interaction, with the potential to be significantly faster as execution capacity scales. Results are sent directly to the service agent’s email, allowing them to review their performance while the interaction is still fresh in their mind.
This shift from weeks to minutes transforms feedback from a retrospective audit into a real-time learning loop. Agents can recognize and correct mistakes almost immediately, which makes coaching more effective and behavioral improvement more sustainable over time.
CardX: Quality Control in Debt Collection
For CardX, the focus is ensuring that debt collection interactions remain respectful, professional, and compliant with legal standards.
The system analyzes conversation content and tone to determine whether collectors maintain appropriate professionalism and provide required explanations to customers. If required disclosures or explanations are missing, the system can flag these issues and alert supervisors.
By automatically generating performance reports and behavioral scores, management gains better visibility into service quality and can provide more targeted coaching to improve customer experience.
From Experimentation to Organizational Impact
These solutions demonstrate how AI can transform compliance monitoring from a manual sampling process into a scalable oversight system.
By enabling continuous monitoring across all customer interactions, the approach significantly improves regulatory visibility while reducing operational costs. Just as importantly, the near real-time feedback loop ensures that insights translate into actual behavior change at the agent level, rather than being lost to the delays of traditional auditing cycles.
The system provides both institutions and regulators with stronger assurance that market conduct standards are consistently upheld.
This innovation, which has been patented, represents a step toward more transparent, accountable, and customer centered financial services across the SCBX ecosystem.
