Banking
Enterprise AI Platform for Banking
Enterprise AI Platform for banking, built around the constraint that defines the sector: core banking systems are not to be touched, so everything integrates around them.
- Regulations in scope
- 4
- Systems we integrate
- 5
- Typical first release
- 6 weeks
What changes when it is banking
Cost allocation is the feature that gets a platform funded. The moment finance can see spend by team and by feature, the conversation changes entirely.
In banking, core banking systems are not to be touched, so everything integrates around them. That single fact reshapes how enterprise ai platform has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.
The workload we are most often asked to take on first is account opening documentation, usually integrated against Flexcube. We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.
Deployed across regulated and unregulated sectors, with audit trails where the regulator expects them. You own the code, the models where they are open-weight, and the documentation to run it without us.
The sector constraints we design around
- Defining constraint
- core banking systems are not to be touched, so everything integrates around them
- Regulations in scope
- RBI master directions · PMLA and AML · DPDP Act 2023 · cybersecurity framework for banks
- Systems of record
- Finacle · Flexcube · core banking platforms · CRM · loan management systems
- Where we usually start
- account opening documentation
Enterprise AI Platform workloads in banking
- account opening documentation
- AML alert triage
- customer service automation
- loan file assembly
- branch reporting
What is included
- Model gateway across providers with failover
- Central prompt and template registry with versioning
- Per-team quotas, budgets and cost allocation
- Policy enforcement, PII handling, allowed models, data residency
- Full audit log of every prompt and response
- Self-service onboarding for product teams
Questions from this sector
Will this touch our core banking system?
No. We integrate through supported interfaces and read replicas, never by modifying the core.
How do you handle AML false positives?
Context enrichment and tuned scoring so alert volume matches investigator capacity, with every decision explainable in a case file.
Why not let teams call the APIs directly?
Because you lose cost visibility, audit trail and policy enforcement, and you end up with keys in a dozen repositories. A gateway gives teams the same speed with none of that exposure.
Does it lock us to one model provider?
The opposite, the gateway is what makes providers swappable, with failover when one has an outage.
How long does a platform take?
A usable first version with gateway, logging and quotas typically lands in six to eight weeks; governance depth grows from there.
Other capabilities for banking
- AI Agent Development for Banking
- Agentic Workflow Automation for Banking
- LLM Application Development for Banking
- RAG & Knowledge Retrieval for Banking
- Chatbot Development for Banking
- Voice AI Agents for Banking
- Document Processing & IDP for Banking
- AI Copilot Development for Banking
- Predictive Analytics & Forecasting for Banking
- Data Engineering for Banking
Enterprise AI Platform for banking, worth a conversation?
Tell us the workload and the regulation it sits under. We will tell you what is realistic.
Or email bd@dtrasglobal.com · call +91 74118 77878
