Solutions
ACP Early Warning Systems (EWS)
From a reactive to a proactive approach for credit portfolio monitoring
Late detection of deteriorating credit worthiness leads to fewer mitigation options and potential losses. ACP Early Warning System (EWS) offers both the established models of traditional EWS and the diverse data sources of AI-powered EWS to produce proactive predictive capabilities.
1/ Data collection & integration
Extract, transform, huge volume of structured and unstructured data (transactional, credit , financial statements, and customer information).
2/ Feature Engineering
Advanced techniques to create new variables key variables or features (payment history, debt-to-income ratio, and customer demographics).
3/ Models & pattern detection
Include all customer data, account activity, ATM transactions, and any tracked interactions to identify, track, and report changes in customer behavior patterns.
4/ Threshold setting
Determine threshold values for each predictive model to classify loans into different risk categories, such as low risk, moderate risk, or high risk.
5/ Monitoring and integrated alerts
Dashboards reflecting portfolio health and forecasting delinquency. Remediation workflows can be initiated when loans exhibit EWS or breach predefined thresholds.
6/ Automated remerdial action
Develop actionable workflows for addressing identified risks, including early borrower intervention, loan restructuring, and collection initiatives.
Leveraging traditional and AI-powered EWS for timely detection of delinquent loans
Moving from a reactive to a proactive portfolio monitoring approach
Track significant customer data, financial transactions, and other relevant interactions or activities
- Automate knowledge extraction and analysis through state-of-the-art techniques such as Natural Language Processing (NLP), Predictive Analytics, Big Data Mining, and Clustering.
- New analytical perspectives based on customer performance and interactions are available
- Changes in customer behavior patterns are carefully tracked, reported, and assigned appropriate severities to improve delinquency prediction accuracy
Shifting towards a proactive approach to portfolio monitoring.
- Move from an intermittent and limited scope checking to a regular and wide range portfolio monitoring
- Benefit from a holistic screening and categorization of the credit portfolio.
- Compelling dashboards reflecting portfolio health, and warnings history.
- Delinquency forecast with the most risky exposures.
- Trigger action plan workflows.
Trigger alerts based on a set of signals
- Build and customize robust and performing Machine Learning pipelines.
- Support tech-savvy business experts in the ingestion and processing of structured and unstructured data.
- Produce and deploy high-performing AI-Powered Early Warning Signals models.
- Monitor internal & external data quality.
- Algorithms are fine-tuned over time and increasingly acquire a high level and measurable accuracy.
Axe Credit Portal is trusted by
Société Générale
“Société Générale Group is growing business very rapidly and successfully in around 20 African countries. To support its development and fully catch the continent’s growth potential, we were looking for a cutting-edge credit granting tool, that adjusts to multibank setup and allows for digital, reliable, and fast processing. We are very confident that Axe Finance's technology will significantly contribute to making our clients’ experience even more unique.”
ACP Early Warning System
FAQ
Why is an Early Warning System important in lending?
Late detection of deteriorating creditworthiness reduces mitigation options and increases the likelihood of losses. An Early Warning System (EWS) enables lenders to move from a reactive to a proactive approach, spotting risks before they escalate. By continuously monitoring borrower behaviour, financial transactions, and external signals, lenders can intervene early, improve portfolio resilience, and reduce delinquency rates.
What types of data does the ACP Early Warning System analyse?
ACP EWS processes both structured and unstructured data at scale. This includes transactional records, financial statements, payment histories, customer demographics, account activity, ATM transactions, and even external data sources such as news or filings. Advanced analytics techniques like Natural Language Processing (NLP), clustering, and predictive modelling help uncover subtle behavioural shifts and emerging risks that traditional monitoring may miss.
How does ACP Early Warning System integrate with existing credit operations?
ACP EWS is embedded into the Axe Credit Portal ecosystem and can be seamlessly connected to existing lending workflows via APIs. This allows real-time monitoring, alerts, and remediation steps to feed directly into loan servicing, collection, and portfolio management systems without disrupting established processes. Lenders can tailor thresholds, workflows, and dashboards to match their specific risk policies and operational requirements.