How Flexible Data Usage Can Save Banks Over €100 Million.

Monolithic systems built on rigid frameworks make adapting to new data sources a heavy lift for underwriting teams. Lack of flexibility also limits the ability to customize models for unique segments that fall outside one-size-fits-all scoring.

A modular architecture addresses these constraints and also enables maximum, flexible data utilization:

• Sub-models are designed around specialized data inputs, such as transaction details, bureau attributes, or application verification results. This allows real-time ingestion of both external and internal customer data.
• Risk-scoring logic unique to niche segments can be configured without rebuilding fundamental parameters.
• Alternative data signals supplement traditional sources without disrupting legacy integrations.
• Upper-level meta scores provide consistency while allowing flexibility across modules.

With economic uncertainty of current times, configurability and localization will be what sets you apart from conventional underwriting processes:

• Macro conditions vary sharply across geographies, requiring regional fine-tuning.
• Different portfolio exposures experience disparate impacts, needing segment-specific adjustments.
• Speed to test and update sub-components gives first-mover advantage.

Rather than treated as one-off projects, building modular foundations facilitates continuous improvement. New data can be rapidly tested and applied to models with little to none down time. We are talking 1-3 months from conception to production.

The ability to surgically modify parts while keeping your systems going is essential. It empowers underwriting teams to course-correct in real-time.?

This increased agility and flexible data usage enabled by modular architecture can drive substantial bottom-line impact, with over €100 million in additional profit potential for mid-sized banks mainly derived from SMEs.

Oz Mizrahi

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