Banking

Custom Model Fine-tuning for Banking

Custom Model Fine-tuning 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 fine-tunes and distils models for teams with genuine volume, where the economics of inference have started to matter more than the ceiling of capability.

In banking, core banking systems are not to be touched, so everything integrates around them. That single fact reshapes how custom model fine-tuning 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 loan file assembly, usually integrated against core banking platforms. We build the smallest thing that proves the case, put it in front of real users, and expand only what earns its keep.

Built by engineers who ship production systems, not by a practice that subcontracts the build. 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

Custom Model Fine-tuning workloads in banking

  • account opening documentation
  • AML alert triage
  • customer service automation
  • loan file assembly
  • branch reporting

What is included

  • Honest assessment of whether fine-tuning is warranted
  • Training data curation and quality review
  • LoRA or full fine-tune as the workload justifies
  • Evaluation against the prompted baseline
  • Inference deployment and cost comparison
  • Retraining pipeline as your data grows

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.

Should we fine-tune?

Usually not first. Prompting and retrieval solve most problems more cheaply. Fine-tuning wins for consistent format, narrow domain style, and high-volume tasks where a smaller model can replace a larger one.

How much data do we need?

For LoRA on a narrow task, often a few thousand high-quality examples. Quality matters far more than volume. We review the dataset before training anything.

Can we own the model?

With open-weight base models, yes. You hold the weights and can run them on your own infrastructure indefinitely.

Custom Model Fine-tuning 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