AI Fundamentals for Banking Infrastructure
A Two-Week Primer with Layman Terms and Java Examples
Week 1 – Core Concepts
DAY 1 – DEEP LEARNING Deep Learning is a type of AI that mimics how the human brain learns. Instead of writing rules like “if transaction > £10,000, flag it,” you feed the model thousands of examples and let it figure out what fraud looks like. It uses neural networks—layers of interconnected nodes that learn patterns from data.
In banking, Deep Learning is great for detecting fraud, predicting loan defaults, or analyzing customer behavior. It works best with large datasets and unstructured data like text or images.
Java Example Imagine a fraud detection model
double[] transaction = {1200, 3, 0}; // amount, location code, merchant type
NeuralNet fraudDetector = new NeuralNet();
boolean isFraud = fraudDetector.predict(transaction);
DAY 2 – FOUNDATION MODELS Foundation models are large AI models trained on massive datasets—like the entire internet. They’re general-purpose and can do many tasks: summarizing, translating, answering questions, and more. You don’t build them from scratch—you reuse them and fine-tune if needed.
In banking, you can use foundation models to summarize KYC documents, extract key terms from contracts, or answer customer queries.
Java Example Calling a foundation model to summarize a KYC document:
String kycText = "Customer provided passport and utility bill...";
String summary = foundationModel.summarize(kycText);
DAY 3 – ATTENTION Attention is a mechanism inside AI models that helps them focus on the most important parts of the input. Just like when you read a sentence and focus on keywords, AI uses attention to weigh which words matter more.
In banking, attention helps AI models highlight key phrases in loan documents or contracts—like interest rates or repayment terms.
Java Example Simulating attention weights:
String[] words = {"The", "interest", "rate", "is", "5%"};double[] weights = {0.1, 0.3, 0.4, 0.1, 0.1}; // Focus on "interest" and "rate"
DAY 4 – TRANSFORMERS Transformers are the architecture behind most modern AI models. They process all input at once (not word-by-word) and use attention to understand relationships between words. This makes them fast, scalable, and very accurate.
In banking, transformers power tools like ChatGPT, Bedrock, and Llama—used for summarization, document parsing, and customer support.
Java Example Using a transformer model to process a document:
TransformerModel model = new TransformerModel();String response = model.process("Summarize this credit card agreement");
DAY 5 – CONTEXT LENGTH Context length is how much information an AI model can “remember” at once. A short context means it forgets earlier parts of the input. A long context means it can understand full documents or long conversations.
In banking, this matters when processing long regulatory documents or multi-step workflows. Longer context = fewer mistakes and better accuracy.
Java Example Handling long input:
String[] tokens = tokenize(document);if (tokens.length <= model.getContextLimit()) {model.process(tokens);} else {chunkAndProcess(tokens);}
DAY 6 – PROMPTING Prompting is how you give instructions to an AI model. A good prompt is clear, specific, and includes context. Think of it like giving a task to a smart assistant—what you say determines the result.
In banking, prompting helps you get accurate summaries, explanations, or answers from AI. You can ask it to rewrite infra docs, extract risks, or explain flows.
Java Example Prompting the model:
String prompt = "Summarize this infra doc in 3 bullets:\n" + document;String result = model.generate(prompt);
DAY 7 – REVIEW & REFLECT Review all the concepts from Days 1–6. Reflect on how they apply to your team’s work. Write a short summary like:
“AI helps banking by automating fraud detection, summarizing documents, and understanding long texts using models like Transformers and Foundation Models. Prompting and context length are key to getting accurate results.”
Week 2 – Practical Tools & Applications
Day 8 – Fine-Tuning
Fine-tuning is the process of taking a general-purpose AI model and training it on your company’s specific data. Think of it like hiring a smart intern who knows general banking concepts, and then teaching them your internal policies, tone, and workflows.
Instead of building a model from scratch, you start with a pre-trained one (like GPT or Titan), and feed it examples from your domain—KYC documents, infrastructure notes, compliance reports. The model adjusts its internal parameters to better reflect your data.
Why it matters in banking:
Improves accuracy for internal tasks
Reduces hallucinations (wrong answers)
Speaks your team’s language (e.g. “PAN”, “CTC”, “SIT”)
Example scenario: You want the AI to summarize KYC documents using your internal format. After fine-tuning, it knows to highlight ID type, address proof, and declaration status.
Java-style snippet:
FineTuner tuner = new FineTuner(baseModel);
tuner.train("kyc-docs.json");
Model customModel = tuner.getModel();
Day 9 – Embeddings
Embeddings are how AI understands meaning. They convert words, phrases, or documents into numerical vectors—like fingerprints that capture semantic meaning.
This lets AI find similar content even if the wording is different. For example, “failed payment” and “transaction error” might look different to humans, but embeddings show they’re related.
Why it matters in banking:
Improves search across tickets, logs, and docs
Enables semantic matching (not just keyword)
Powers recommendation systems and clustering
Example scenario: You search for “payment issue” and the AI finds logs with “transaction timeout” or “card declined”—even though those exact words weren’t used.
Java-style snippet:
EmbeddingEngine engine = new EmbeddingEngine();
double[] vector = engine.embed("failed payment");
List<String> results = engine.search(vector);
Day 10 – RAG (Retrieval-Augmented Generation)
RAG combines two things:
Retrieval – finding the most relevant document or passage
Generation – using AI to answer your question based on that document
This is powerful because it grounds the AI’s response in real data, reducing errors and improving trust.
Why it matters in banking:
Answers are backed by actual documents
Reduces hallucinations
Great for querying Confluence, PDFs, or policy docs
Example scenario: You ask “What’s the fraud escalation process?” and the AI pulls the correct section from your compliance doc, then summarizes it.
Java-style snippet:
String query = "What is the fraud escalation policy?";
String doc = searchEngine.findRelevantDoc(query);
String answer = aiModel.generateAnswer(doc, query);
Day 11 – Tokenization
Tokenization is the process of breaking text into smaller pieces (tokens) before feeding it to an AI model. These tokens can be words, subwords, or even characters.
It’s like slicing a sentence into digestible chunks so the model can process it efficiently.
Why it matters in banking:
Helps models handle long documents
Affects context length and memory
Important for performance and accuracy
Example scenario: Before summarizing a 10-page regulatory document, you tokenize it to ensure the model can process it chunk by chunk.
Java-style snippet:
Tokenizer tokenizer = new Tokenizer();
List<String> tokens = tokenizer.tokenize("Banking infrastructure is evolving.");
Day 12 – Amazon Bedrock Basics
Amazon Bedrock is a cloud service that gives you access to powerful foundation models (like Anthropic Claude, Meta Llama, and Amazon Titan) via API. You don’t need to manage servers or GPUs—just send a request and get a result.
It’s ideal for teams that want to use AI without worrying about infrastructure.
Why it matters in banking:
Easy integration with Spring Boot or microservices
Scalable and secure
Supports summarization, classification, Q&A, and more
Example scenario: Your backend service sends a document to Bedrock and gets back a 3-bullet summary for stakeholder review.
Java-style snippet:
RestTemplate rest = new RestTemplate();
String response = rest.postForObject("https://bedrock.aws/summarize", document, String.class);
Day 13 – AI Use Cases in Banking
Here are three practical ways AI is already helping banking teams:
Chatbots AI-powered bots handle customer queries like balance checks, card blocking, and loan status—24/7.
Regulatory Summarization AI can read long compliance documents and extract key points, saving hours of manual review.
Anomaly Detection AI learns transaction patterns and flags suspicious behavior—like duplicate payments or unusual locations.
Java-style snippet (Chatbot): Chatbot bot = new Chatbot(model); String reply = bot.respond("How do I block my card?");
Day 14 – Final Glossary & Summary
Here’s a recap of the 10 key terms you’ve learned:
Deep Learning: AI that learns patterns from data
Foundation Models: Pre-trained models for many tasks
Attention: AI’s focus mechanism
Transformer: Architecture behind modern AI
Context Length: How much info AI can remember
Prompting: Giving clear instructions to AI
Fine-Tuning: Teaching AI your company’s language
Embeddings: Turning text into meaningful numbers
RAG: Search + AI-generated answers
Tokenization: Breaking text into chunks
Final Summary:
Generative AI is transforming banking infrastructure. It helps automate document handling, detect fraud, improve customer service, and accelerate compliance. With tools like Bedrock, embeddings, and fine-tuning, you can build smarter, faster, and more secure systems—without over-engineering.
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