Sentiment analysis emerged as a field in the 1990s, and NLP-powered sentiment analysis emerged as an innovative call center technology at that time, allowing machines to determine how a customer call was going in near real-time. Combined with automatic speech recognition (ASR), traditional call recording solutions became more intelligent with NLP, enabling meaning to be derived from text generated from speech.
The Miracle of What Happened Next
In 1998 the convolutional neural network (CNN) was invented, and in 2009 the first graphics processing unit (GPU) was used for the purpose of deep learning. This major technological leap was actually enabled by video games, as GPUs has previously only been used to render high quality video game imagery. Then in 2017, the groundbreaking paper “Attention is All You Need” was published by eight computer scientists working at Google, which introduced the transformer architecture and the mechanism of self-attention.
Combined with the massive gains in accelerated computing, this gave us mass availability of generative AI (genAI) in 2022 with the release of ChatGPT 3.0.
GenAI is a type of artificial intelligence that creates new content, such as text, images, or code, by learning patterns from existing data.
Penetration of Generative AI in Mortgage Today
While confusion persists around the distinction between AI and genAI, successful applications like operator chatbots leveraging retrieval-augmented generation have emerged. AI-assisted code generation has become routine, and large language models (LLMs) are being used in sentiment analysis and customer interaction analysis. GenAI-based tools are gaining traction in OCR, ICR, and marketing content generation, though adoption remains cautious, particularly for customer-facing roles. Mixed policy approaches reflect this hesitance, with some enterprises fully embracing tools like ChatGPT while others impose outright bans, highlighting cultural and educational barriers.
Despite these advancements, confusion about genAI's use cases, often conflated with traditional AI or expert systems, further complicates adoption. Discussions about AI guardrails are becoming more prevalent, reflecting the need for clearer guidance and responsible use. However, truly innovative applications of genAI in industries like mortgage remain scarce, likely due to restrictive policies, a chilling effect from regulatory fears, and insufficient investment in organizational research and development (R&D) labs. This underscores the need for more targeted exploration and education to unlock genAI’s potential while addressing these challenges.
