Northeast India

Fraud & Anomaly Detection across Assam

Detection systems tuned to the cost of a miss versus the cost of a false positive, because they are not equal. Covering every district and PIN code in Assam.

Districts
23
PIN codes
571
Cities mapped
14

Fraud & Anomaly Detection in Assam

Every flagged decision needs an explanation an investigator can act on. 'The model said so' fails in a case file and fails harder in a regulatory review.

Assam runs on tea, petroleum and natural gas, agriculture and handloom and silk, plantation and energy operations spread across difficult terrain, which makes remote monitoring and field-data capture the recurring need. Where fraud & anomaly detection earns its budget here usually follows directly from that mix.

Integration comes before intelligence. A model that cannot reach your systems of record is a demo with good manners. Six weeks to something running in production, not six quarters to a strategy document.

নমস্কাৰ , Nomoskar. We work in Assamese and English across Assam.

Assam coverage

State / UT
Assam
Region
Northeast India
Districts covered
23
PIN codes covered
571
Cities mapped
14
Working languages
Assamese, English

What is included

  • Hybrid rules-and-model scoring, because rules encode known fraud well
  • Real-time decisioning within your latency budget
  • Case management for investigators
  • Explanations attached to every flagged decision
  • False-positive rate tuned against investigation capacity
  • Feedback loop from confirmed outcomes

Questions

Do you cover all of Assam?

Yes, all 23 districts and 571 PIN codes. Delivery is remote-first, so coverage is genuinely statewide rather than limited to the cities we happen to have offices in.

Which Assam sectors do you work with most?

Across Assam the economy leans towards tea, petroleum and natural gas, agriculture, handloom and silk. Plantation and energy operations spread across difficult terrain, which makes remote monitoring and field-data capture the recurring need.

How do you reduce false positives?

By tuning the threshold against your actual investigation capacity, adding context features, and feeding confirmed outcomes back into the model. The goal is the alert volume your team can genuinely work.

Can it explain its decisions?

Yes, feature-level explanations on every flag, which investigators need for case files and regulators expect to see.

How fast does it score?

Real-time within a payment authorisation window where required; batch where the use case allows it and the cost is lower.

Fraud & Anomaly Detection in Assam

Covering all 23 districts. Tell us what you are trying to change.

Or email bd@dtrasglobal.com · call +91 74118 77878