BuildNexTech

The Cost of the "Review Queue"

‍

Rule-based fraud detection trades scale for customer friction. Rigid rules fail to adapt to new vectors, pushing thousands of transactions into manual review queues. This operational lag is exactly where fraud happens.

Aggressive rulesets also generate high false-positive rates that decline legitimate customers leading to immediate churn while analysts burn out investigating low-confidence alerts. The true cost of inaction isn't just missed fraud; it's lost lifetime value and bloated overhead.

‍

The AI Fraud Detection Workflow

We engineer custom, automated workflows operating in milliseconds, sitting directly between your payment gateway and core ledger.

Monitor

Real-time data ingestion without latency.

Detect

ML models assess complex anomalies against your historical baseline.

Score

Dynamic risk scoring is instantly applied.

This requires robust AI Integration & AI Security to connect to legacy infrastructure, often augmented with Agentic AI to autonomously gather investigation data before a human analyst opens the ticket. Explore our AI Services.

Proven ROI

We stop revenue leakage and accelerate operations.

‍

Digital Payments Platform

31% reduction in transaction fraud losses in Q1.

Read Case Study

Financial Services Firm

$1M prevented annually, 60% faster investigations, 45% fewer false positives.

Read Case Study

Comprehensive Protection

A custom model learns to identify anomalies across the threat spectrum:

‍

Transaction Fraud

Stolen card usage and unauthorized transfers.

Account Takeover (ATO)

Irregular login locations and velocity.

Synthetic Identity

Blended real/fake credentials during onboarding.

Document Fraud

Automated verification of altered KYC documents.

High-Volume Environments

FinTech & Payments

Secure P2P transfers and high-throughput global gateways with sub-second latency.

Banking

Protect core ledgers and new account provisioning workflows.

Insurance

Flag anomalous claims prior to pay-out.

E-Commerce

Reduce friendly fraud without creating checkout friction.

Build Requirements & Security

To build a highly accurate detection layer, we require

Data

6–12 months of transaction logs and labelled fraud instances.

Access

API access to your payment gateway/CRM.

Enterprise Security

Data is encrypted at rest and in transit, and never shared across clients.

Custom Build vs. SaaS

Off-the-shelf SaaS tools force your data into generic models with escalating per-transaction pricing. BNXT.ai offers

No Vendor Lock-in

You own the model
and IP.

Bespoke Accuracy

Trained exclusively on your transaction data, not global averages.

Deep Integration

Sits natively inside your existing CRM and LOS - no clunky third-party dashboards.

Frequently Asked Questions

How does AI detect fraud in real time?

AI models process hundreds of variables (location, velocity, device ID) in milliseconds. By scoring these against known patterns, the system approves or blocks transactions before payment authorization completes.

What is the difference between rule-based and AI fraud detection?

Rule-based systems rely on strict manual thresholds easily bypassed by fraudsters. AI is dynamic; it learns hidden relationships between variables, adapting automatically as bad actors change tactics.

How long does implementation take?

A custom enterprise model typically takes 8 to 12 weeks from data ingestion to deep CRM/payment integration, depending on data readiness and infrastructure complexity.

Can AI reduce false positives?

Yes. Because machine learning evaluates the entire transaction context rather than triggering on a single strict rule, it accurately differentiates between legitimate customer anomalies and actual fraud.

Can AI integrate with legacy core banking systems?

Yes. We engineer secure API middleware that allows modern machine learning models to communicate seamlessly with legacy banking ledgers and on-premise infrastructure without latency.

Automate Fraud Detection with AI