Precedence diagramming (S2S, F2S, F2F) at module level — consistent with how Google runs large programs.
Primary delivery chain
Bank API Integration ──F2S──▶ Cash Flow Analysis ──S2S──▶ Recommendation Engine ──F2S──▶ Compliance / Output Filter ──F2S──▶ Notification Delivery
From
Type
To
Risk
Rationale
Bank API Integration
F2S
Cash Flow Analysis
High
Cannot run without a verified real-time data feed
Cash Flow Analysis
S2S
Recommendation Engine
Medium
Both run concurrently once analysis service starts
Recommendation Engine
F2S
Compliance / Output Filter
High
Filter must check wording before any output reaches users
Compliance / Output Filter
F2S
Notification Delivery
Low
Only approved CFPB-compliant content can be delivered
Project 2 dependencies
Feedback Loop / Recommendation Store ──F2F──▶ ML Scoring Engine | Feedback Loop ──S2S──▶ Recommendation Engine
From
Type
To
Risk
Rationale
Feedback Loop / Recommendation Store
F2F
ML Scoring Engine (Project 2)
Medium
ML cannot complete until outcome history is fully collected
Feedback Loop
S2S
Recommendation Engine
Medium
Feedback begins capturing signals as engine starts delivering
Internal dependencies
Transaction DB ──F2S──▶ Cash Flow Analysis ──F2S──▶ Recommendation Engine ──F2S──▶ Compliance / Output Filter ──F2S──▶ Notification Delivery User Profile Store ──F2S──▶ Recommendation Engine Recommendation Engine ──F2S──▶ Recommendation Store ──F2S──▶ Feedback Loop ──F2F──▶ ML Scoring Engine
Internal dependency
Type
Risk
Note
Transaction DB
Internal data store
Medium
Must provide transaction and balance history before Cash Flow Analysis starts
User Profile Store
Internal data store
Medium
Must provide budget, consent, and preference data before Recommendation Engine runs
Cash Flow Analysis
Internal module
High
Must complete affordability evaluation before Recommendation Engine decides pay / split / postpone
Recommendation Engine
Internal module
High
Must generate output before Compliance / Output Filter checks the wording
Compliance / Output Filter
Internal control
High
Must approve suggestion-only wording before Notification Delivery or mobile output
Recommendation Store
Internal data store
Medium
Must log generated and approved recommendations for traceability and future feedback analysis
Feedback Loop
Internal module
Medium
Must capture user actions and outcomes after recommendations are delivered
ML Scoring Engine
Internal — Project 2
Medium
Needs Project 1 feedback and outcome history before ML personalization can be trained
External dependencies
Dependency
Type
Risk
Mitigation
Plaid / Finicity API
External vendor
Medium
Confirm rate limits and SLA before Cash Flow Analysis development begins
FCM / APNs
External vendor
Low
Stable standard integration
CFPB compliance review
Regulatory
High
Legal sign-off required before Compliance / Output Filter content is finalised
Stripe / Braintree
External vendor
Medium
Validate installment plan API in discovery sprint
ML training data
Internal — Project 2
Medium
Project 1 Feedback Loop must accumulate sufficient outcome history first
Regulatory and compliance considerations
Requirements mapped to system components. HIPAA excluded — not applicable to fintech.
Standard
Key requirements
Components affected
Status
CFPB
Suggestions only — not regulated financial advice. No guarantee language.
Recommendation engine, Compliance/Output Filter, notification copy, mobile UI
Legal review required
PCI-DSS
Tokenization of payment data, firewall config, secure transmission
Payment processor, Transaction DB, API gateway
Vendor handles majority
CCPA
User consent for transaction data, right to deletion, opt-out of sharing
User profile store, consent logging, bank API integration
ToS update needed
GDPR
Data encryption, consent logging, right to be forgotten, data minimisation
All data stores, authentication layer, user profile store
If EU users onboarded
Inv. Advisers Act
SpendWise must not constitute registered investment advice. Framing critical.
All user-facing recommendation copy
Hard constraint
HIPAA deliberately excluded — it covers healthcare data, not financial platforms. Including it would indicate template-copying rather than project-specific analysis.
Advanced concepts — extra credit
Not bolted on — each concept traces directly back to Assignment 1 decisions.
SAFe architectural runway
The modular add-on architecture in Project 1 was designed to avoid blocking Project 2. The recommendation engine microservice is decoupled from the ML scoring engine — Project 2 plugs in without touching Project 1 code. The feedback loop begins collecting outcome data in Project 1 so that by the time Project 2 starts, there is enough training signal. This is what SAFe calls "runway" — the architecture is already prepared for the next horizon before you get there.
Hybrid delivery model
Bank API integration and the compliance layer are planned predictively — requirements are fixed, no iteration needed. The recommendation engine and notification UX are delivered adaptively — early user feedback shapes which trigger conditions and copy perform best. The Compliance / Output Filter is a hard gate on every sprint, ensuring CFPB compliance is part of the definition of done, not a post-sprint review.
DevOps integration planning
(1) Feature flags — recommendations roll out to 5% of users first, churn and engagement measured before full release. (2) CI/CD pipeline — new model versions tested against a holdout set before deployment, zero-downtime releases. (3) Automated compliance testing — recommendation copy is linted against CFPB language rules on every commit. This connects directly to the Assignment 1 success criteria — fast releases measured against churn and conversion targets.
All three concepts trace back to Assignment 1 — the two-project program structure, the adaptive/hybrid lifecycle, and the MOV metrics. This assignment extends the same thinking technically.