Next collection action with ML

Intelligent Selection of Debt Collection Actions - Maximum Effectiveness, Minimum Costs

Challenges addressed

High cost and time-consuming debt collection processes.
Lack of personalization of collection actions for different types of debtors.
Escalations to court too quickly generating additional expenses.

SLS-powered solutions

Automatic analysis of repayment history and scoring of debtors.
Dynamic selection of the most effective and cheapest method of contact.
Personalized debt collection strategies based on machine learning.
Integration with CRM, debt collection systems and call centers.

Results achieved

Reduction of recovery time by 30%.
Reduction of debt collection costs by 40%.
Increased debt collection efficiency by 35%.

Next collection action with ML

Traditional debt collection processes are often costly and suboptimal - escalating too quickly to court or ineffective actions generate unnecessary expenses.

Our no-code/low-code platform uses machine learning (ML) to intelligently select the optimal recovery method, helping companies recover debts faster, cheaper and more efficiently.

Data-driven debt collection - efficient and cost-effective.

With our technology, you can:

  • Analyze repayment history and debtor behavior - ML identifies the most effective action for each case.
  • Automatically select the cheapest and most effective method - instead of immediately taking the case to court, the system suggests, for example, e-mail, SMS, telephone or negotiation.
  • Personalize recovery strategies - dynamically adjust the approach depending on risk and customer profile.

How it works

  • Collect data on the debtor - transactions, previous interactions, repayment history, credit score.
  • The ML model analyzes patterns of successful actions - the system learns what methods worked best in similar cases.
  • Choose the optimal contact strategy - e.g., an initial reminder by email, escalation to the phone, and only as a last resort legal action.
  • Automatically trigger action - send a message, task the call center, refer the case for negotiation.
  • Monitor effectiveness and optimize - ML algorithms constantly adjust strategy based on new data.

Applications for B2C and B2B

  • Banks and fintechs - optimize loan collection, reduce litigation costs.
  • Leasing and factoring companies - smart negotiation instead of immediate legal escalation.
  • E-commerce and BNPL - precise targeting of debtors with effective contact methods.
  • Telecommunications and subscription services - minimizing churn with optimal collection strategies.

Technology that improves recovery efficiency

  • No-code/low-code - flexible customization of rules and strategies without programming.
  • AI and machine learning - intelligent selection of actions based on data and patterns.
  • API integrations - collaboration with CRM, collection systems and call centers.
  • Decision automation - the system itself selects and launches the most effective action.

Don't overpay for debt collection - recover money in the most efficient way.
Automate your processes and let AI choose the best strategy for each debtor.

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