Banking

Predictive Analytics & Forecasting for Banking

Predictive Analytics & Forecasting 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 forecasting and risk models that are backtested honestly and monitored for drift, because a model that was accurate last year is not evidence about this one.

In banking, core banking systems are not to be touched, so everything integrates around them. That single fact reshapes how predictive analytics & forecasting 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 core banking platforms. 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

Predictive Analytics & Forecasting workloads in banking

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

What is included

  • Data audit before any modelling, with gaps reported
  • Baseline model so improvement is measurable
  • Error bars and confidence intervals on every forecast
  • Feature importance you can explain to the business
  • Backtesting against held-out historical periods
  • Monitoring for drift once live

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 history do you need?

Generally two to three seasonal cycles for demand work, less for churn or risk scoring. The data audit in week one tells us what is realistically achievable with what you have.

How accurate will the forecast be?

We report error against a naive baseline on held-out periods. If the model does not beat the baseline meaningfully, we say so rather than shipping it.

Can the business understand the output?

Yes, feature importance and driver explanations are part of the deliverable. A forecast planners cannot interrogate is a forecast they will override.

Predictive Analytics & Forecasting 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