Changing Eligibility Criteria? Why Traditional SDLC Breaks and How Software Platform Anatomy Ensures Stability
- Krish Ayyar
- Mar 28
- 4 min read
Updated: Jun 24
Category: Rules & Motivations in Flux
Series: Rethinking Requirements: How the ICMG Enterprise Anatomy Model Makes Systems Change-Ready
Perspectives Covered: Strategy, Business Process, System, Component Specification, Implementation, Operations
Key Variables Impacted: Rule, Data, Event, UI, Function, Network
In this post, we explore a requirement change scenario driven by a shift in credit risk policy—an example of a Rule/Motivation change. We examine how such a change impacts the lending architecture and how the ICMG Enterprise Anatomy Model (Project Edition) enables precise, coordinated responses across the architectural perspectives and variables involved.
Perspectives covered: Strategy, Business Process, System, Component Specification, Implementation, Operations
Key variables impacted: Rule, Data, UI/Access Channel, Event
The Challenge of Frequent Requirement Changes
Retail lending is a dynamic space. Loan eligibility criteria evolve constantly due to shifts in credit risk appetite, regulatory directions, and business strategy.
Consider a typical requirement change:The credit policy team increases the minimum credit score for loan eligibility from 650 to 700.
While the change may seem simple, in most lending systems it leads to significant disruption:
Developers must modify code across multiple systems
UI and backend display inconsistent eligibility decisions
Pre-approved customers are incorrectly accepted or rejected
Testing becomes reactive and manual
Business teams lose confidence in delivery
These problems arise because most teams react at the implementation level without understanding the system’s architectural anatomy. The ICMG Enterprise Anatomy Model (Project Edition) addresses this challenge through a structured, multi-perspective, variable-driven approach.
Why Conventional SDLC Approaches Fail
What goes wrong:
Credit score rules are hardcoded across backend services, APIs, and decision engines
Customer portals display outdated or inconsistent messaging
Loan officers manually override system behavior
Compliance reports reflect incorrect approval decisions
The root cause:
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