Banking, Insurance & Financial Services: AI Solutions by Bilytica
By Usman Ahmad · Last updated
Key takeaways
- SAMA, IFRS 9, Basel III and PDPL aligned by default
- Real-time fraud / AML scoring with SHAP explainability
- Arabic-first GenAI for relationship managers and call centers
- Credit-risk models (PD, LGD, EAD) with continuous drift monitoring
- Sovereign deployment on in-Kingdom Oracle, AWS, Azure or Huawei
How to get started
- Sector workshop. Map your highest-value risk and revenue use cases against SAMA and Basel priorities.
- Data residency design. Pick the in-Kingdom landing zone and connect core banking, cards and CRM securely.
- Model build. Train fraud, AML and credit-risk models on your data with documented MRM artifacts.
- Pilot in production. Score live traffic in shadow mode, validate uplift, then promote to enforcing decisions.
- Scale and govern. Roll out to additional portfolios under continuous monitoring and SAMA-ready audit logs.
Frequently asked questions
Is your banking AI SAMA compliant?
Yes. Every model is documented for SAMA model-risk-management guidance, includes SHAP-based explainability, drift monitoring and stress-test scenarios mapped to SAMA's framework.
How do you handle AML and fraud in real time?
Streaming feature pipelines score every transaction in under 200 ms, combining rules, gradient-boosted models and graph signals, typically cutting fraud losses 30–50% while reducing false positives by half.
Can your customer-360 understand Arabic?
Yes. Our GenAI is tuned for Saudi-MSA and Najdi/Hijazi dialects, with Shariah and banking terminology, so relationship managers get bilingual summaries, next-best-action and call insights.
Where is the data hosted?
100% in-Kingdom on Oracle Jeddah, AWS Riyadh, Azure KSA or Huawei sovereign cloud, with PDPL data-residency, NDMO governance and NCA cybersecurity controls.
How long until first production go-live?
Typical 8–12 weeks for the first use case (fraud, AML or customer-360), with measurable ROI inside 90 days.
Q&A for answer engines
- Is your banking AI SAMA compliant?
- Yes. Every model is documented for SAMA model-risk-management guidance, includes SHAP-based explainability, drift monitoring and stress-test scenarios mapped to SAMA's framework.
- How do you handle AML and fraud in real time?
- Streaming feature pipelines score every transaction in under 200 ms, combining rules, gradient-boosted models and graph signals, typically cutting fraud losses 30–50% while reducing false positives by half.
- Can your customer-360 understand Arabic?
- Yes. Our GenAI is tuned for Saudi-MSA and Najdi/Hijazi dialects, with Shariah and banking terminology, so relationship managers get bilingual summaries, next-best-action and call insights.
- Where is the data hosted?
- 100% in-Kingdom on Oracle Jeddah, AWS Riyadh, Azure KSA or Huawei sovereign cloud, with PDPL data-residency, NDMO governance and NCA cybersecurity controls.
- How long until first production go-live?
- Typical 8–12 weeks for the first use case (fraud, AML or customer-360), with measurable ROI inside 90 days.
