Banking
Recommendation & Personalisation for Banking
Recommendation & Personalisation 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
Orqent Labs builds recommendation systems evaluated by controlled experiment, reported against revenue or engagement rather than a leaderboard metric.
In banking, core banking systems are not to be touched, so everything integrates around them. That single fact reshapes how recommendation & personalisation 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 AML alert triage, usually integrated against Finacle. 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. We hand over with runbooks, tests and a team that knows how it works, not a dependency.
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
Recommendation & Personalisation workloads in banking
- account opening documentation
- AML alert triage
- customer service automation
- loan file assembly
- branch reporting
What is included
- Event tracking design, since most projects start with inadequate data
- Baseline popularity model to beat
- Hybrid collaborative and content-based ranking
- Cold-start handling for new users and new items
- A/B testing framework with proper statistics
- Business-metric reporting, not just offline accuracy
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.
How much data do we need?
Less than people assume to start. A content-based approach works from day one; collaborative filtering improves as interaction volume grows.
How do you handle new products?
Content-based features carry new items until interaction data accumulates, with deliberate exploration so new items get a fair chance to be seen.
How do we know it is working?
Controlled A/B tests measured on revenue or engagement, with proper statistical treatment rather than eyeballing a dashboard.
Recommendation & Personalisation in other sectors
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
Recommendation & Personalisation 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