Payment Risk And Fraud with AI: A Beginner's Guide

Explore how tokens and tokenization drive large language models, how transformer architectures with encoder and decoder components process text, and how fine-tuning tunes pre-trained models for specific tasks and domains.

Learn how prompts and prompt engineering shape AI outputs by providing clear requests, background, and a defined persona, improving accuracy and relevance in responses.

Discover AI agents that perceive, think, and act to complete tasks like assessing transaction risk and detecting payment fraud, using reinforcement, supervised, and unsupervised learning to improve.

Explore how word embeddings assign numerical codes to words, creating a word neighborhood that reveals meaning and relationships for AI and LLMs.

Explore how the transformer uses encoder and decoder components with word embedding, positional encoding, self-attention, and encoder-decoder attention to translate text quickly and contextually.

Explain how a decision tree uses nodes to split data and guide payment fraud decisions, including examples like model score above 0.9 and account age under 30, noting overfitting risks.

Explore how to use a customized GPT to build a decision tree model from historical data, generate Python code, and test it to detect bad transactions.

Explore Visa chargeback rules across fraud, authorization, processing errors, and consumer disputes, with subcategories including card present and card not present scenarios and reason code 10.4 interpretations guiding merchant responses.

Build your own visa chargeback expert GPT by configuring a lightweight, instruction-driven model that understands visa rules and chargeback eligibility timelines.

Learn how logistic regression uses maximum likelihood estimation to fit curves, assess r-squared and p-value with Maikai-Fend pseudo-r-squared, and compare to a no-prediction model via log-likelihood and chi-square.