Using LLM Embeddings to Transform Risk Data Analysis

machine-learning embeddings aws sagemaker

In my previous post on building an LLM pipeline with SageMaker, we covered training and deploying models. But there's a powerful technique that sits between raw data and user-facing insights: embeddings. They're the bridge that lets you take complex risk data or trained outputs and turn them into something genuinely useful for end users.

What Are Embeddings?

At their core, embeddings are dense vector representations of data — typically text, but they work for images, audio, and structured data too. Instead of representing a word or sentence as a sparse one-hot vector, embeddings map it into a continuous, high-dimensional space where semantic similarity becomes geometric proximity.

Think of it like this: if you embed the phrases "high credit risk" and "elevated default probability," they'll end up close together in vector space. Meanwhile, "low volatility" will be far away. This property is what makes embeddings so powerful for analysis.

# Example: Two semantically similar phrases will have similar embeddings
embedding_1 = model.encode("high credit risk")
embedding_2 = model.encode("elevated default probability")
embedding_3 = model.encode("low volatility")

# Cosine similarity between 1 and 2 will be high
# Cosine similarity between 1 and 3 will be low

Modern LLMs generate these embeddings as part of their architecture. The same model you trained in SageMaker can produce them directly.

How Embeddings Work

When you pass text through an LLM, the model processes it through multiple transformer layers. Each layer refines the representation, capturing increasingly abstract patterns. The final hidden states — or sometimes a pooled version of them — become the embedding.

Here's a simplified view of the process:

  1. Tokenization: Break text into tokens (subwords or characters)
  2. Token Embeddings: Map each token to a learned vector
  3. Positional Encoding: Add information about token position
  4. Transformer Layers: Process tokens through attention and feed-forward layers
  5. Pooling: Aggregate token representations into a single vector (mean, max, or CLS token)

The result is a fixed-size vector (often 768 or 1024 dimensions for models like BERT, or up to 4096+ for larger models) that encodes the meaning of your input.

import torch
from transformers import AutoTokenizer, AutoModel

# Load a pre-trained model
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")

# Generate embedding
text = "Customer exhibits high transaction volatility"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)

with torch.no_grad():
    outputs = model(**inputs)
    # Use mean pooling to get sentence embedding
    embedding = outputs.last_hidden_state.mean(dim=1)

print(embedding.shape)  # torch.Size([1, 384])

Using Embeddings for Risk Data Analysis

Now, let's connect this to something practical: analyzing risk data from your SageMaker pipeline. Suppose you've trained a model on financial transaction data, and you want to surface insights to users in a way that's intuitive and actionable.

Problem: Making Sense of Thousands of Risk Signals

You have thousands of risk events — fraud alerts, credit score changes, compliance flags. Users can't scroll through raw logs. They need a way to:

  • Find similar incidents quickly
  • Cluster related risks
  • Ask questions in natural language
  • Get summarized, ranked results

Solution: Embed Everything

Step 1: Generate embeddings for all your risk events.

import boto3
import json
from sentence_transformers import SentenceTransformer

# Initialize embedding model
embed_model = SentenceTransformer('all-MiniLM-L6-v2')

# Fetch risk data from S3 (output from your SageMaker pipeline)
s3 = boto3.client('s3')
response = s3.get_object(Bucket='my-bucket', Key='risk-events/latest.json')
risk_events = json.loads(response['Body'].read())

# Generate embeddings for each event description
embeddings = []
for event in risk_events:
    description = event['description']
    embedding = embed_model.encode(description)
    embeddings.append({
        'event_id': event['id'],
        'embedding': embedding.tolist(),
        'metadata': event
    })

# Store embeddings in a vector database or S3

Step 2: Build a semantic search layer.

When a user searches for "suspicious wire transfers," you embed their query and find the closest risk events in vector space.

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

# User query
query = "suspicious wire transfers"
query_embedding = embed_model.encode(query)

# Compute similarity to all stored embeddings
event_embeddings = np.array([e['embedding'] for e in embeddings])
similarities = cosine_similarity([query_embedding], event_embeddings)[0]

# Get top 5 most similar events
top_indices = similarities.argsort()[-5:][::-1]
results = [embeddings[i]['metadata'] for i in top_indices]

for result in results:
    print(f"Event: {result['description']} | Score: {similarities[top_indices[0]]:.3f}")

Using a Vector Database

For production, you'll want a proper vector database like Pinecone, Weaviate, or OpenSearch with k-NN. They handle indexing, approximate nearest neighbor search, and scaling.

from pinecone import Pinecone, ServerlessSpec

# Initialize Pinecone
pc = Pinecone(api_key="your-api-key")

# Create index
index = pc.Index("risk-embeddings")

# Upsert embeddings
vectors = [
    (e['event_id'], e['embedding'], e['metadata'])
    for e in embeddings
]
index.upsert(vectors=vectors)

# Query
query_embedding = embed_model.encode("high credit risk customers").tolist()
results = index.query(vector=query_embedding, top_k=10, include_metadata=True)

for match in results['matches']:
    print(f"{match['metadata']['description']} | Score: {match['score']:.3f}")

Building User-Friendly Outputs

Once you have embeddings in place, you can build interfaces that feel intelligent:

1. Semantic Search

Users type natural language queries and get relevant risk events instantly, ranked by semantic similarity.

2. Automatic Clustering

Group similar risks together without manual tagging. Run k-means or HDBSCAN on your embeddings to discover patterns.

from sklearn.cluster import KMeans

# Cluster risk events
kmeans = KMeans(n_clusters=10)
clusters = kmeans.fit_predict(event_embeddings)

# Add cluster labels to metadata
for i, event in enumerate(embeddings):
    event['metadata']['cluster'] = int(clusters[i])

3. Anomaly Detection

Identify outlier events by measuring distance to cluster centroids or using isolation forests on embeddings.

4. Conversational Queries

Let users ask complex questions like "Show me credit risks from Q4 that are similar to this flagged account." Embed the question, filter by metadata, and return results.

Integrating with Your SageMaker Pipeline

Here's how this fits into the pipeline we built before:

  1. Training — Your SageMaker job produces a model that can generate embeddings
  2. Batch Transform — Run batch inference to embed your entire risk dataset
  3. Storage — Write embeddings to a vector database or S3
  4. Endpoint — Deploy a SageMaker endpoint that embeds user queries in real time
  5. Application — Your front-end calls the endpoint and searches the vector store
import sagemaker

# Deploy embedding model as SageMaker endpoint
estimator = sagemaker.estimator.Estimator(
    image_uri="123456789012.dkr.ecr.us-east-1.amazonaws.com/embedding-model:latest",
    role=role,
    instance_count=1,
    instance_type="ml.m5.large"
)

predictor = estimator.deploy(
    initial_instance_count=1,
    instance_type="ml.m5.xlarge"
)

# Query the endpoint
import boto3
client = boto3.client("sagemaker-runtime")

response = client.invoke_endpoint(
    EndpointName=predictor.endpoint_name,
    ContentType="application/json",
    Body=json.dumps({"text": "high credit risk"})
)

embedding = json.loads(response["Body"].read())

Real-World Example: Risk Dashboard

Imagine a dashboard where analysts can:

  • Type "show me fraud cases similar to case #12345"
  • See a ranked list of similar incidents
  • Click through to see full details
  • Get an AI-generated summary of common patterns

Behind the scenes:

  1. The query gets embedded via your SageMaker endpoint
  2. A vector database returns the top matches
  3. Metadata filters narrow down results (date range, severity, etc.)
  4. Another LLM call summarizes the findings

The entire flow is powered by embeddings, and users interact with it like it's magic.

Best Practices

1. Choose the Right Model For domain-specific data (finance, healthcare), fine-tune an embedding model on your data. Generic models like all-MiniLM-L6-v2 work well out of the box, but custom training improves relevance.

2. Normalize and Store Metadata Embeddings capture semantics, but you still need filters (date, category, user ID). Store metadata alongside embeddings.

3. Monitor Drift As your data evolves, re-embed periodically. Embeddings from old models can become stale.

4. Hybrid Search Combine semantic search (embeddings) with keyword search (BM25) for best results. Tools like OpenSearch support this natively.

Conclusion

Embeddings turn messy, high-dimensional data into something you can reason about geometrically. They're not just a trick for similarity search — they're a fundamental tool for building intelligent systems that meet users where they are.

If you've already got a SageMaker pipeline running, adding embeddings is a natural next step. You go from serving predictions to serving understanding. And that's where the real value is.

Next time a user asks, "find risks like this one," you'll be ready.