Processor Authorization Rate Optimization

Client Segment: Processor Category: Benchmarking / Decline Analytics Owner: Straive Strategic Analytics Year: 2024

Objective

Identify root causes of declined transactions at the processor layer and recommend targeted interventions to improve overall authorisation rates, reducing false declines that erode merchant GMV and cardholder experience.

Methodology

1. Decline code taxonomy — map raw ISO 8583 response codes to strategic categories (insufficient funds, card restrictions, risk rules, technical)
2. Merchant-level and BIN-level decline pattern analysis
3. Issuer benchmarking — compare decline rates by BIN against network averages
4. Intervention simulation — model GMV recovery for each lever
5. Straight-through processing (STP) opportunity sizing

Key Findings Framework

Decline Category	Typical Share	Actionable?
Issuer Risk Rules (false declines)	28–35%	Yes — BIN-level outreach
Insufficient Funds	22–30%	Partial — retry logic
Card Restrictions (caps/blocks)	15–20%	Yes — issuer policy
Technical / Timeout	8–12%	Yes — routing optimisation
Velocity Controls	5–10%	Yes — threshold tuning

Assets

• src/auth_rate_analysis.py — Decline taxonomy, BIN analysis, intervention sizing
• src/retry_optimizer.py — Smart retry logic and timing recommendations
• sql/auth_decline_extract.sql — Authorization event extraction
• sql/bin_benchmarking.sql — BIN-level performance vs. network benchmarks

Requirements

pandas>=2.0
numpy>=1.26
scikit-learn>=1.3
plotly>=5.18
sqlalchemy>=2.0