MODELCATALOG

Use one business problem to connect classical NLP, Transformers, retrieval and MLOps.

NLP / Capstone / End-to-End

16. Capstone: Banking Complaint Intelligence

Use one business problem to connect classical NLP, Transformers, retrieval and MLOps.

01 Foundations Represent language 02 Classical NLP Build baselines 03 Neural NLP Learn sequence 04 Transformers Learn attention 05 Applications Apply models 06 Engineering Operate safely 07 Capstone Integrate

01 Learning objectives

By the end of this module, you should be able to explain the concept in plain language, connect it to a use case, and recognize when it is — and is not — appropriate.

  1. Objective 1

    Build a classical baseline.

  2. Objective 2

    Compare with a Transformer classifier.

  3. Objective 3

    Add semantic search/RAG.

  4. Objective 4

    Design production controls.

02 Phase 1 — Classical baseline

Dataset: complaint text + resolution/category label.

Pipeline:

clean_text → TF-IDF (1,2 grams) → Logistic Regression → category probability

Start here because it is fast, explainable and provides a benchmark.

03 Phase 2 — Transformer classifier

Fine-tune an encoder model for the same labels.

Compare:

  • macro F1,
  • minority-category recall,
  • latency,
  • training/inference cost,
  • explainability.

04 Phase 3 — Investigator retrieval

Embed historical resolved complaints and policy documents. Retrieve similar cases or policy passages for a new complaint.

05 Phase 4 — RAG assistant

Combine:

  • complaint record,
  • structured customer/account facts,
  • retrieved policy,
  • similar historical cases,
  • LLM-generated investigator summary.

Keep human review for consequential decisions.

06 Phase 5 — Production controls

Version the model/tokenizer/index, capture predictions and confidence, monitor drift, test policy grounding, enforce metadata access controls, and preserve audit traces.

07 Pitfalls and nuances

These are the mistakes most likely to create a wrong mental model or a weak implementation.

  1. Pitfall 1

    Jumping straight to an LLM without a baseline.

  2. Pitfall 2

    Using generated text as an unreviewed final decision.

  3. Pitfall 3

    Mixing training/test periods in ways that leak future information.

08 Checkpoint questions

Try answering these without looking back at the module. If the explanation is clear in your own words, you are ready to continue.

  1. Check 1

    Which component should be your benchmark?

  2. Check 2

    What would justify the extra cost of a Transformer?

  3. Check 3

    What controls would you require before using RAG in a regulated workflow?