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BNPL in Credit Reports: How Could This Data Impact FICO Scores?

FICO

Our two-part blog series will discuss our initial insights into how this emerging BNPL credit product could impact the FICO ® Score and its predictive power. This blog is co-authored with Senior Director of FICO Scores and Predictive Analytics, Suna Hafizogullari. Stay tuned! by Ethan Dornhelm.

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How to Extract Key-Value Pairs Using Deep Learning

Nanonets

This tutorial blog examines some of the use cases of key-value pair extractions, the traditional and current approaches to solving the task, and a sample implementation with code. Even in the business world, invoices use this structure: items purchased are the keys, with prices as their corresponding values.

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Credit Card Fraud: It’s Still a Thing (and as Big as Ever)

FICO

Last year I blogged a lot about how payments and fraud patterns have changed. In this blog post I’ll do some quick level-setting with 2019 card fraud numbers, touch on what that looked like for 2020, and then share a round-up of several of the latest credit card fraud trends.

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Unsupervised Learning Part 2: The AML Connection

FICO

In my first blog about unsupervised learning , I took a deep dive into this machine learning technique, which draws inferences in the absence of outcomes. Springboarding from that blog, today’s post covers three categories of unsupervised learning that FICO has investigated, refined and put into our anti-money laundering (AML) solutions.

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A Complete LlamaIndex Guide

Nanonets

Whether you have data stored in APIs, databases, or in PDFs, LlamaIndex makes it easy to bring that data into conversation with these smart machines. The 'data connectors' are the diligent gatherers, fetching your data from wherever it resides, be it APIs, PDFs, or databases.

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Chat with PDFs using ChatGPT & OpenAI GPT API - A Detailed Tutorial

Nanonets

In this blog post, we explore Language Learning Models (LLMs) and their astounding ability to chat with PDF files. LangChain is an innovative technology that functions as a bridge -  linking large language models (LLMs) with practical applications like Python programming, PDFs, CSV files, or databases.

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How to build your own Zendesk Answer Bot with LLMs

Nanonets

This blog post will guide you through building and deploying your own Zendesk Auto Responder using LLMs and implementing RAG-based workflows in GenAI to streamline the process. In simple terms, RAG enables the AI to pull in relevant information from a database or other sources to support the generation of more accurate and informed responses.

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