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Early Warning &

ApPello’s Early Warning & Monitoring system serves as an ideal instrument for promptly identifying any disparities in loan repayments, enabling timely interventions to proactively address potential challenges.

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Product Details

In addition to conventional credit monitoring, this solution actively facilitates the swift management of emerging concerns by initiating policy actions at the onset, effectively preventing any further decline in a client’s financial condition.

The inbuilt monitoring component enables the seamless and routine evaluation of the entire loan portfolio.

The Early Warning and Monitoring System is a technologically crafted innovative tool, based on our modern, low-code microservices platform.


Early warning

Early identification of non-performing loans, efficient credit monitoring

Warning signals

Parameterizable alerting mechanism, Automated detection of early warning signals


Classification, segmentation and strategy management

risk management

Action plans and risk intervention management


Provision and RWA mitigation, asset quality and solvency improvement

Expert estimation

Expert estimation of risk categorisation (Loans, Customers)

Early Warning

Data gathering

  • Internal data sources
  • External data sources

Warning signal

  • Configurable signal definition
  • Automatic / manual generation
  • Configurable signal rating

Customer/Loan classification

  • Evaluation of signals
  • Automatic risk evaluation
  • Manual adjusment

Action plan

  • Configurable action catalogue
  • Automated action planning
  • Action plan classification


  • Task execution
  • Customer/Loan evaluation
  • Outcome: Problem free/Watchlisted/Workout

Statistical based flexible
Early Warning calculation engine

The system operates using a personalized and expandable collection of early warning signals, which can be automatically or manually generated. The specifics of monitored data, the criteria for automatic signal generation, and the severity of each signal are all governed by business rules, easily parameterized by Power users. These signals might encompass factors like reduced account turnovers, delayed payments, alterations in client ratings, or data from an external credit blacklist, among others.

Appello wirkflow

Workflow based
escalation process

The classification process initiates automatically, driven by business rules that assess the warning signals and suggest an initial client rating. In tandem, business and risk management specialists contribute their evaluations, which further inform the client’s rating and subsequently guide the development of an action plan. Following the rating stage, the system employs an approval process to determine the necessary course of action. Decision-making benefits from a comprehensive 360° client overview, enriched with client group insights, ensuring a well-informed approach.


Action plans and handling
of risk mitigation tasks

Beyond the mere identification of issues, the system takes charge of their resolution through the formulation of adaptable action plans, devised according to the indicator signals. This entails the generation of fresh tasks for each action, often involving a process of reevaluation (rerating). The system then meticulously oversees the allocation, execution, and subsequent reevaluation of these tasks, ensuring a thorough and well-monitored progression.


Integrated loan monitoring

The Monitoring module adeptly fulfills the regulatory mandate for routine client assessments. Through automated processes, the system generates monitoring sheets encompassing client details and exposures, subsequently assigning them to designated users for assessment. Within these monitoring sheets, each client’s exposures receive individual ratings, and the approval process is diligently shepherded by a guided workflow.

Appello AI Engine


ApPello’s AI Engine plays a pivotal role in detecting early stress signals through the utilization of advanced machine learning capabilities.

Harnessing the capabilities of AI, the system unearths latent patterns within customer behavior, thereby shifting manual monitoring from a retrospective approach to a proactive stance in risk management.

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