Case SPA R3: Evolving Fraud Rules? How Software Platform Anatomy Enables Real-Time, Risk-Aligned Adaptation
- Krish Ayyar
- Apr 1
- 6 min read
Updated: Oct 10
Category: Rules & Motivations in Flux
Series Title:"Rethinking Requirements: How the ICMG Enterprise Anatomy Model Makes Lending Systems Change-Ready."
Perspectives Covered: Strategy, Business Process, System, Component Specification, Implementation, Operations
Key Variables Impacted: Rule, Data, Function, Event, Network
Keeping Up with Evolving Fraud Detection Needs
In the world of retail lending, fraud detection rules must constantly evolve to keep up with emerging threats. Whether it’s a new pattern of synthetic identity fraud or real-time risk scoring based on behavioral analysis, adapting these rules without disrupting the system is a significant challenge.
Consider a scenario where a new fraud pattern is detected, prompting the risk management team to update detection rules to include new behavioral parameters (e.g., IP location mismatches or rapid login attempts).
While the rule update may seem minor, traditional lending systems face considerable disruption:
Multiple systems may still rely on outdated rules
Real-time data inconsistency leading to false positives or false negatives
Fraud detection lags, increasing exposure to threats
Customer experience suffers due to unwarranted transaction rejections
These challenges arise because conventional SDLC methods often fail to account for rapid, coordinated rule changes across interconnected components. The ICMG Enterprise Anatomy Model (Project Edition) offers a structured, multi-perspective approach that ensures quick, reliable updates while maintaining system integrity.
Why Conventional SDLC Approaches Fail
Common Problems:
Hard-coded fraud rules scattered across back-end systems
Real-time data feeds not synchronized with updated rule logic
User interfaces displaying outdated risk warnings
Events not fired correctly during rapid rule updates
Manual interventions to compensate for system failures
Root Causes:
The root of these issues lies in the fragmented approach of traditional SDLC practices, which lack:
Integrated rule management across architectural perspectives
Real-time data traceability and synchronization
Coordinated updates to functions and events
Clear visibility into how fraud detection rules link to business processes
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