AI/ML strategy: in-house или vendor capability

Когда возникает развилка

Multiple AI use cases — credit scoring, fraud detection, NBA, AML, chatbot. Build in-house team (data scientists, ML engineers) или buy vendor models per category.

Banking-specific criteria

Regulatory traceability. Models must be explainable per cbu.uz. Vendor models — need access к internals, не black box.

Bias monitoring. Disparate impact обязательно для credit. Vendor должен support bias dashboards.

Model risk management. Banks under formal model validation requirements (Basel-style). In-house easier to govern.

Data residency. ПДн в стране — vendor должен process locally или sufficient anonymization.

Regulatory approvals. New ML model для credit may require regulator review.

Talent availability. Local data science talent constrained — in-house difficult to scale.

Когда in-house

Strategic ambition data-driven bank.

Capital для team build (10-50 ML people).

Strong existing data foundation.

5+ year horizon.

Regulator engagement preferred direct.

Когда vendor

Limited talent availability.

Standard use cases (vendor offerings mature).

Time to market priority.

Can negotiate vendor for explainability, audit access.

Когда hybrid (most common)

In-house для strategic models (credit decisioning, fraud).

Vendor для commodity capabilities (chatbot, OCR, generic analytics).

Centre of Excellence in-house с vendor augmentation.

Где обычно ошибаются

In-house без realistic talent assessment. 2 years later team под-staffed.

Vendor model for credit без access к internals. Regulator audit fails.

Hybrid без clear governance — modes conflict.

Связанное

• /architecture/banking-mlops-architecture/ — MLOps
• /insights/banking-ai-governance/ — AI governance
• /insights/banking-model-risk-management/ — model risk
• /expertise/banking-data-discipline/ — data discipline