Increasing Sales Volume through Limit Optimization

3 Key Insights

• The Challenge: Organizational silos between risk assessment and credit limit departments hindered the ability to achieve holistic optimization.

• The Impact: Achieved a $1.35M increase in transaction volume by raising approval rates by 2.4%, with a negligible 0.18% increase in default rates.

• The Solution: Developed a unified KPI framework to analyze the interplay between risk and profitability, enabling optimized credit limit strategies for every segment.

  
  

Impact

A leading Thai credit card issuer achieved a remarkable $1.35M surge in transaction volume by raising its approval rate by 2.4%. This growth was executed with surgical precision, keeping the impact on default rates at a near-zero level of just 0.18%. By accurately identifying "high-value, low-risk" segments—customers who are both financially stable and highly active—the provider was able to proactively expand credit limits. Conversely, for higher-risk groups, the issuer applied a rigorous limit-reduction policy backed by clear, explainable logic, ensuring that risk mitigation was handled without causing unnecessary customer friction or default triggers.

Challenge

The financial landscape in Thailand is currently defined by significant regulatory headwinds, with household debt reaching 90% of GDP. Consequently, the central bank has enforced stringent guidelines, causing card approval rates to stagnate around 40%, while personal loan approvals have dropped even further. This environment forced a strategic pivot from aggressive new customer acquisition to "profitability expansion" within the existing portfolio. However, this transition was historically difficult because of organizational friction: the risk evaluation teams and credit limit setting teams operated independently, making it nearly impossible to align risk appetite with revenue-generating strategies.

Solution

To bridge this gap, DEIN developed an integrated KPI designed to intuitively analyze the correlation between delinquency risk and credit utilization. By combining estimated default rates with predicted spending patterns, the team isolated "super-segments" for targeted limit expansions. For the more complex "high-risk, high-usage" segments, the team conducted a granular profitability analysis instead of applying blunt, across-the-board cuts. This allowed for a selective limit-reduction policy that neutralized potential default risks while preserving the revenue generated by active users.

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