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Learning Roadmap

How to Become a AI Default Prediction Specialist

A step-by-step, phase-based learning path from beginner to job-ready AI Default Prediction Specialist. Estimated completion: 7 months across 6 phases.

6 Phases
26 Weeks Total
High Entry Barrier
Advanced Difficulty
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  1. Foundations: Credit Risk & Financial Data

    4 weeks
    Goals
    • Understand PD/LGD/EAD concepts and IFRS 9 / CECL accounting frameworks
    • Learn to query and wrangle loan-level datasets in SQL and pandas
    • Grasp the structure of credit bureau data, financial statements, and macro indicators
    Resources
    • Coursera 'Credit Risk Management' by NYIF
    • Book: 'Credit Risk Analytics' by Baesens, Roesch, and Scheule
    • Kaggle 'Home Credit Default Risk' dataset for hands-on exploration
    Milestone

    You can pull a loan-level dataset, compute vintage curves, and explain default rate vs. loss rate to a non-technical stakeholder.

  2. Core Modeling: Gradient Boosting & Logistic Baselines

    6 weeks
    Goals
    • Build, tune, and validate XGBoost/LightGBM models for binary default classification
    • Master feature engineering techniques specific to credit data (WoE, IV, target encoding)
    • Implement rigorous out-of-time and cross-validation testing protocols
    Resources
    • Book: 'Introduction to Statistical Learning' (Hastie et al.) - chapters on tree methods
    • Open-source: ScorecardPy / toad for WoE-based scorecard building
    • Kaggle 'Give Me Some Credit' competition for benchmark practice
    Milestone

    You can build a production-quality credit-scoring model, defend your validation methodology, and generate reason codes for predictions.

  3. Deep Learning & NLP for Financial Signals

    5 weeks
    Goals
    • Apply LSTM and Transformer architectures to borrower-behavior time-series data
    • Fine-tune a HuggingFace model on financial texts (10-K filings, earnings transcripts) to extract default-predictive signals
    • Use LangChain to build a retrieval-augmented pipeline over a corpus of credit agreements
    Resources
    • HuggingFace 'NLP Course' (free)
    • Paper: 'FinBERT: Financial Sentiment Analysis with Pre-trained Language Models'
    • LangChain documentation and cookbook examples
    Milestone

    You can augment a tabular credit model with NLP-derived features (sentiment scores, covenant flags) and measure the incremental lift.

  4. MLOps, Governance & Regulatory Compliance

    4 weeks
    Goals
    • Set up an end-to-end MLOps pipeline with MLflow, DVC, and Airflow for automated retraining
    • Implement drift-detection monitors (PSI, KL divergence) with alerting
    • Draft model risk management documentation compliant with SR 11-7 principles
    Resources
    • MLflow official tutorials
    • Book: 'Machine Learning Engineering' by Andriy Burkov
    • Fed SR 11-7 guidance document (publicly available)
    Milestone

    You can deploy a model behind an API, monitor its health in production, and produce an audit-ready model validation package.

  5. Stress Testing, Scenario Analysis & Executive Communication

    3 weeks
    Goals
    • Design macroeconomic stress-test frameworks (baseline, adverse, severely adverse scenarios)
    • Quantify portfolio-level loss distributions under correlated default assumptions
    • Build executive dashboards and present model outputs to non-technical risk committees
    Resources
    • CCAR/DFAST public stress-test templates from the Federal Reserve
    • Book: 'The Essentials of Risk Management' by Crouhy, Galai, and Mark
    • Tableau or Power BI dashboard tutorials
    Milestone

    You can run a full stress-test cycle, explain tail-risk implications in plain English, and recommend portfolio actions based on model insights.

  6. Capstone: End-to-End Default Prediction System

    4 weeks
    Goals
    • Build a complete default prediction system from data ingestion to model serving
    • Integrate alternative data, NLP features, and ensemble models into a unified pipeline
    • Create a portfolio repository with documentation, tests, and deployment scripts
    Resources
    • Your own GitHub portfolio repo
    • AWS SageMaker or GCP Vertex AI free tier for deployment
    • Peer review from credit-risk communities (Risk.net forums, LinkedIn groups)
    Milestone

    You have a portfolio-quality project demonstrating the full lifecycle of an AI default prediction system, ready to present in interviews.

Practice Projects

Apply your skills with hands-on projects. Ordered by difficulty.

Consumer Credit Default Classifier with SHAP Explainability

Beginner

Build an XGBoost model on the Home Credit or Lending Club dataset to predict loan defaults, complete with SHAP-based reason codes for every prediction and an interactive dashboard.

~30h
Credit risk modeling fundamentalsXGBoostSHAP explainability

NLP-Augmented Corporate Default Predictor

Intermediate

Combine structured financial ratios with NLP features extracted from 10-K filings (risk factor sentiment, MD&A complexity scores) using FinBERT to predict corporate defaults, and measure the lift from text features.

~45h
Deep learning for NLPHuggingFace TransformersFeature engineering

IFRS 9 Expected Credit Loss Calculator with Macro Scenarios

Intermediate

Build a full IFRS 9 staging and ECL computation engine that assigns loans to Stage 1/2/3 based on PD transitions driven by macroeconomic scenarios, producing portfolio-level loss provisions.

~40h
IFRS 9 / CECL frameworksStress testingScenario analysis

LLM-Powered Covenant Risk Extraction Pipeline

Intermediate

Use LangChain and a vector store to build a RAG system that ingests PDF loan agreements, extracts financial covenants and cross-default clauses, and flags high-risk terms for manual review.

~35h
LangChain RAGDocument parsingVector databases

Production MLOps Pipeline for Default Model Retraining

Advanced

Design and deploy an end-to-end MLOps pipeline using Airflow, DVC, and MLflow that automates weekly model retraining, performance validation against drift gates, and staged rollout with canary testing.

~60h
MLOpsApache AirflowMLflow

Graph-Based Contagion Default Model for Connected Borrowers

Advanced

Construct a borrower relationship graph (supply chain, shared directors, guarantor links) and train a Graph Neural Network to predict how default of one entity propagates through the network, validating against historical contagion events.

~55h
Graph neural networksNetwork analysisCounterparty risk

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