Quick Recap: Software gets better when you fix bugs. Models get worse when the world changes. A software update that doesn't touch core logic stays stable forever. A model deployed today degrades tomorrow even if you never change it. Understanding this difference changes how you manage production systems.
Your software team ships a bug fix to the API on Monday. The fix solves a problem. Code stays fixed. Three years later? Still fixed.
Your ML team ships a new fraud model on Monday. The model works great. Detects fraud 94% accurately. Regulators approve it.
Three months later? Same model, same code, same training data—but fraud tactics evolved. Model accuracy drops to 88%. Fraudsters adapted. Code didn't change. Model degraded anyway.
This shouldn't be possible. But it is.
Software: Code stays the same → behavior stays the same.
Models: Code stays the same → behavior gets worse.
Software engineers fix bugs once. ML engineers fight degradation forever.
This difference shapes everything about how you manage production AI.
Why Models Degrade (Even When Nothing Changes)
Root cause: The world changes. Models assume the world stays the same.
When you trained the model (2022):
Interest rates near zero
Fraud tactics focused on card testing
Customer demographics: tech workers 35% of applicants
Employment patterns: traditional W-2 jobs dominant
Today (2025):
Interest rates at 5%+
Fraud tactics shifted to account takeover via AI
Customer demographics: tech workers 52% of applicants
Employment patterns: gig economy expanded to 30%
Model learned 2022. Serving 2025 customers. World changed. Model degraded.
Three Fundamental Differences
Difference 1: Software Assumes Stability
Software design: "Once we fix this bug, it stays fixed."
Reality: Bug stays fixed (if no new bugs introduced).
Assumption: External world is mostly stable. API endpoints don't change behavior. Customer requirements stay constant.
Difference 2: Models Assume... Nothing
ML design: "Once we train this model, it adapts to new data... by degrading."
Reality: Model learns historical patterns. When patterns change, accuracy drops.
Assumption: World stays similar to training data. Customer behavior doesn't shift. Fraud tactics don't evolve.
Difference 3: Software Requires Code Changes to Degrade
To break software, someone must:
Write new buggy code
Deploy it
Shipping introduces the problem
To break a model, nothing needs to happen:
No code change required
No deployment required
World changes on its own
Model degrades on its own
Impact on Management
Software Lifecycle
Deployment: Release version 1.0.
Monitoring: Check for bugs. If found, write fix. Test fix. Deploy fix.
Regression Prevention: Don't ship breaking changes. Test backward compatibility.
Change Control: Only change code if you find a bug or need a feature.
Revalidation: Not needed (unless code changes).
Long-term: Version 1.0 works for years with only occasional bug fixes.
Model Lifecycle
Deployment: Release version 1.0 (trained on 2022-2023 data).
Monitoring: Check for performance degradation. Even without code changes, model gets worse.
Regression Prevention: Can't prevent degradation. World changes on its own.
Change Control: Model may need retraining every quarter without any code changes.
Revalidation: Must happen continuously. Even if code is identical, model behavior changes.
Long-term: Version 1.0 works great for 3 months. By month 6, accuracy has dropped. By month 12, needs replacement.
BFSI-Specific Reality
The Credit Model Case Study
Month 0 (2022, model deployed):
Trained on 2021-2022 data
Interest rates near zero
AUC 89%
Performance stable
Customers happy
Month 3:
Interest rates unchanged
Model code unchanged
AUC still 89%
Everything normal
Month 6:
Interest rates start rising
Model code still unchanged
Customer default patterns shift
AUC drops to 87%
"Something wrong? No code changed."
Month 9:
Interest rates at 4%
Model code still unchanged
Defaults correlate differently with rates
AUC drops to 84%
"Why is model worse? We didn't change anything!"
Month 12:
Interest rates at 5%
Model code identical to day 0
Economic regime completely different
AUC drops to 80%
Model is broken
But no code was touched
Month 15 (action finally taken):
Team realizes: world changed, model didn't
Retrain on 2023-2024 data
Incorporate rate sensitivity
New AUC 91%
Performance recovers
Lesson: Stable code ≠ stable behavior for models. External world changes. Model must adapt. Requires continuous retraining, not just bug fixes.
The Re-validation Problem
Why Testing Is Different
Software Testing:
Test: "Does this code work?"
Answer: Yes or No
Doesn't change: If code is frozen, behavior is frozen
Model Testing:
Test: "Does this model work?"
Answer: "Yes, but for how long?"
Changes constantly: Even frozen model gets worse over time
Re-validation Schedule
Software typically needs testing once:
When code changes
When dependencies update
Quarterly security scan
Models need testing continuously:
Weekly performance check (is AUC declining?)
Monthly deep dive (which segments degrading?)
Quarterly retraining assessment (do we need new model?)
Annually rebuild (or stay at risk)
Feedback Loops Make It Worse
Software: Bug is permanent. Fix it once. Done.
Models: Feedback loops accelerate degradation.
Example:
Model denies risky customers
Over time, only safe customers remain (approval bias)
Training data becomes biased (survivors only)
Model learns wrong patterns (future defaults higher than expected)
Degradation accelerates
Common Mistakes
Mistake 1: Deploy Once, Monitor Never
❌ "We deployed the model. Now we manage it like software (occasional patches)." ✅ "We deployed the model. Now we monitor it continuously (weekly checks minimum)."
Models need active management.
Mistake 2: Assume Frozen = Stable
❌ "Model code hasn't changed, so model behavior is stable." ✅ "Model code frozen, but world changed. Performance will degrade anyway."
External world changes force model degradation.
Mistake 3: Test Once, Release Forever
❌ "We validated the model before deployment. It's good for years." ✅ "We validated the model once. We revalidate monthly. It's good until degradation signals a retrain."
Validation has expiration date.
Mistake 4: Ignore Feedback Loops
❌ "Model denies some customers, but that's just the model working." ✅ "Model denies some customers. Training data becomes survivor-biased. Model quality degrades. Need careful re-sampling."
Feedback loops create hidden degradation.
Mistake 5: Software Mindset to ML Problems
❌ "Our software team manages models. They deploy changes and expect stability." ✅ "ML is different. Passive monitoring isn't enough. Active retraining is required."
Different tools for different problems.
Looking Ahead: 2026-2030
2026: Continuous revalidation becomes regulatory requirement
Can't just validate once before deployment
Regulators expect ongoing performance tracking
Quarterly revalidation reports become standard
2027-2028: Automated retraining triggers based on drift
System detects performance degradation
Automatically queues retraining
Humans approve new model before deployment
2028-2029: Model quality frameworks emerge
Standards for how long model is "valid"
Policies: retrain annually, monthly, or continuously
Different policies for different model types
2030: Continuous learning becomes standard
Models learn from production data automatically (carefully)
Human oversight prevents learned biases
Active vs passive performance tracking integrated
HIVE Summary
Key takeaways:
Software stays fixed when you stop changing it. Models degrade when world changes. Fundamentally different lifecycle.
Software: bug fix is permanent. Model: performance degradation is inevitable (even with no code changes).
Re-validation isn't optional. Model validated in 2022 is invalid in 2025. World changed. Model must be retested.
Feedback loops accelerate degradation. Approval bias means training data becomes skewed. Detection of this requires active monitoring.
Change control for models is different. Not about preventing code changes. About detecting world changes and triggering retraining.
Start here:
If monitoring monthly: Increase to weekly. Monthly cycle is too long to catch degradation early.
If testing once before deploy: Add continuous monitoring. Track AUC, precision, recall weekly. Flag degradation trends.
If no retraining schedule: Create one. Plan retraining every quarter (or when performance drops 2-3%).
Looking ahead (2026-2030):
Continuous revalidation becomes regulatory mandate
Automated retraining triggers based on drift detection
Model quality frameworks with explicit validity periods
Continuous learning from production (with safeguards)
Open questions:
How often should models be retrained? Monthly? Quarterly? Annually?
When does degradation become "too much" and require immediate retrain vs wait till next cycle?
Can feedback loops be prevented? (Hard. Requires careful design.)
Jargon Buster
Model Degradation: Performance decrease over time due to distribution shift. Even without code changes. Why it matters in BFSI: Inevitable. Requires continuous monitoring and periodic retraining.
Re-validation: Testing model performance after deployment. Not one-time. Continuous. Why it matters in BFSI: Regulatory requirement. Performance changes over time.
Distribution Shift: Change in input data or outcome patterns. Customer demographics shift. Fraud tactics evolve. Regulatory environment changes. Why it matters in BFSI: Causes model degradation. Must be detected early.
Feedback Loop: Model's decisions create data that trains next version of model. Approval bias is feedback loop. Why it matters in BFSI: Accelerates degradation. Requires careful handling.
Change Control: Process for controlling code/model changes. For software: preventing buggy code. For models: detecting need for retraining. Why it matters in BFSI: Different for models than software. Focus on world changes, not code changes.
Retraining: Taking model and training new version on fresher data. Why it matters in BFSI: Required periodically. Software doesn't need this. Models do.
Model Validity: How long model is acceptable for deployment. Software validity: indefinite (if bug-free). Model validity: months to quarters. Why it matters in BFSI: Models have expiration dates.
Approval Bias: Training data becoming skewed because model denies certain customers. Denials don't appear in future training data (can't verify default risk). Why it matters in BFSI: Hides true default risk. Causes future model degradation.
Fun Facts
On Feedback Loop Damage: A bank's credit model learned to deny customers with certain employment patterns (gig workers). Those denials meant future training data had no gig worker defaults (denied customers = no default data). Model thought gig workers were infinitely risky. Actually: they were just rare in training data. When retraining on new data including gig worker outcomes, true default risk was much lower. Lesson: approval bias is invisible until you look for it. Requires careful re-sampling when retraining.
On Model Validity Timeframe: A fintech deployed a fraud model in Q1 2022. By Q3 2022, fraudsters had adapted. Model accuracy dropped 6% in 3 months. Team didn't notice (checked quarterly). By Q1 2023, accuracy was down 15%. Cost: $500K+ fraud loss. Lesson: world changes fast. Monthly monitoring would have caught this in month two. Re-validation every quarter is not enough for fraud models in high-velocity environments.
For Further Reading
Why Machine Learning Models Degrade (Polyzotis et al., 2018) - https://arxiv.org/abs/1809.03383 - Academic foundation for understanding model degradation and distribution shift.
Managing ML Systems at Scale (Huyen, 2022) - https://huyenchip.com/2022/12/21/twelve-ml-mistakes.html - Practical guide on lifecycle differences between software and ML systems.
Continuous Monitoring and Revalidation in Production ML (Google MLOps, 2025) - https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation - Industry practices for ongoing model validation.
Feedback Loops in Machine Learning (Selbst & Barocas, 2019) - https://arxiv.org/abs/1901.10002 - Research on how model decisions create training bias. Critical for understanding approval bias.
Federal Reserve Guidance on Model Lifecycle Management (Federal Reserve, 2024) - https://www.federalreserve.gov/supervisionreg/srletters/sr2410.pdf - Regulatory expectations for continuous model monitoring and revalidation.
Next up: Week 9 Wednesday explores document intelligence automation—using OCR, named entity recognition, and retrieval-augmented generation to extract structured data from insurance claims, automatically validate information, and route to processing queues.
This is part of our ongoing work understanding AI deployment in financial systems. If you've encountered model degradation and had to explain to stakeholders why a frozen model performs worse over time, share your experience.
— Sanjeev @ AITechHive.com