#Explainability

Articles about Explainability — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with explainability. ← All posts

#A2A (5)#ADK (8)#AG-UI (7)#AI Agents (244)#AI Governance (7)#AML (3)#Accounting (9)#Agent Skills (3)#Agentic AI (23)#Agentic Commerce (12)#Agents (4)#Architecture (22)#Audit (3)#Azure (11)#Azure AI Foundry (10)#BigQuery (6)#Capital Markets (8)#Cards (13)#Checkpointing (5)#Cloud Architecture (3)#Compliance (30)#Concurrency (4)#Context Providers (3)#Conversation State (3)#Cost Optimisation (5)#Credit (14)#Crypto (12)#Custody (9)#Distributed Systems (3)#Engineering (7)#Evaluation (5)#Explainability (1)#FREE-AI (8)#FX (5)#FinOps (5)#FinTech (6)#Financial AI (14)#Fintech (116)#Fraud (10)#Function Tools (6)#GCP (5)#Go (168)#Google ADK (26)#Governance (5)#Guardrails (7)#HIPAA (3)#Harness Engineering (8)#Human-in-the-Loop (7)#KYC (11)#Kubernetes (6)#LLM (4)#LangGraph (11)#Ledger (12)#Lending (14)#MCP (5)#Machine Learning (12)#Markets (4)#Memory (5)#Microsoft Agent Framework (211)#Middleware (9)#Multi-Agent (8)#Multi-Agent AI (14)#Multimodal (4)#Observability (14)#Open Source (6)#OpenTelemetry (5)#Opinion (6)#Orchestration (15)#Payments (49)#Privacy Engineering (3)#Providers (3)#Python (127)#RAG (6)#RBI (3)#Rails (16)#Reconciliation (3)#Regulation (8)#Reliability (15)#Retrieval (3)#Risk (13)#SRE (3)#Security (24)#Sessions (4)#Settlement (9)#Spanner (4)#Streaming (4)#Structured Output (4)#Testing (6)#Trading (8)#Treasury (5)#Workflows (15)
Pratik Dhanave · ·7 min read

Explaining Credit Decisions: SHAP and Adverse-Action Reason Codes

How lenders turn a model's raw probability into the specific, legally required reasons a declined applicant must receive.

Turning model outputs into legally-required adverse-action reason codes. Global vs local explainability, SHAP values, mapping contributions to reason codes, and fairness checks (disparate impact) that regulators expect.

#Financial AI#Machine Learning#Explainability#Credit

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.