AI in Central Asian Banking: Real Cases vs Hype

Over the past two years the banking AI market in Central Asia has grown fast. Every bank wants to "implement AI". But what does that mean in practice?

What Actually Works

1. Call Centre Speech Analytics

This is the most mature and in-demand direction. Banks with call centres of 100+ agents can already get real ROI from analysing 100% of calls.

What works: automatic topic tagging of enquiries, service quality monitoring, fraud detection through speech patterns.

2. ML Scoring for Retail Loans

A classic — and it keeps delivering. Additional features from transaction data give a Gini improvement of 5–8% over traditional scorecards. For banks with portfolios above $100M this pays for itself quickly.

3. Real-Time Anti-Fraud

ML-based systems are already displacing rule engines. A 30–40% reduction in false positives while maintaining fraud detection rate — these are real numbers from live projects.

What's Still Hype

LLM Chatbots as Agent Replacements

The technology is not yet ready to fully replace agents in CA banking. Language models perform poorly with Uzbek and Kazakh, regulatory constraints limit AI-driven decisions, and hallucination risk is too high in a financial context.

Generative AI in Underwriting

Great demos, complex implementation. Explainability of decisions is a fundamental issue for regulators. Too early.

Practical Advice

1. Start with a task where you already have data and a measurable KPI

2. A 3-month pilot beats a 12-month project

3. An internal team is essential — otherwise you're dependent on the vendor forever

4. The Central Bank of Uzbekistan is open to dialogue — use that

Want to discuss a specific AI use case for your bank? Write to me.