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48 HOW AI WILL ENHANCE THE MORTGAGE SERVICING INDUSTRY Artificial Intelligence (AI) will one day touch every single industry and level of our lives. e interaction and dependence on AI will be so nat- ural that we will have to stop and think what life would be like without it. It will be cheap, accessi- ble, and widely circulated. ere will be different levels of sophistication for AI models, ranging from simple and primitive ("if this, then perform this action") to highly complex, individualized free-thinking, self-replicating automatons. is article presents a brief, historical perspective on the evolution of AI over the last several decades, the introduction and development of AI in our financial industry, and, finally, a word of caution. A LOOK BACK AT FINANCE AND AI While AI continues to become part of the conversation, and to greater levels of degree, this was not always the case. It was around the 1980s when AI began to gain prominence in the fi- nance world. e model created (Expert System) was used to predict market trends and provide customized financial plans; this kicked off the acceptance of AI in finance, which was primarily used in financial analysis, market analysis, curren- cy exchange, and bank management. e 1990s introduced fraud detection and advanced levels of transaction reviews. e early 2000s introduced the assistance in underwriting decisions on the credit application of corporate loans. e 2010s used the model to enable smaller enterprises to conduct professional data security analysis as a result of capital constraints. Until this time, AI revolved around analyzing a massive amount of numerical data, hence the perfect fit for the finance industry. However, it was not until Natural Language Processing (NLP) arrived around mid-2015 as a method to enhance the AI model with text. No longer would AI dependence be limited to numeric or quantifiable data alone. Instead, a massive amount of text would now be involved with improved models. According to NVIDIA, one of the world's leading AI programming and chip manufacturers, there are even more advanced models such as BERT (Bidirectional Encoder Representations From Transformers) which can understand words in context. For example, it could tell a difference between a city's riverbank and the River City Bank. HOW AI WORKS Simply put, AI is a process of prediction and errors of prediction. It requires big data technol- ogy and a series of highly complex algorithms using linear algebra, probability, and statistics. e AI engineer would use programming languages like Python, R, Java, or C++ to build the AI model. One type of AI model is "Inference and Train." You tell the model there is a cat in a pic- ture and to find the cat. e model, having access to all information via the internet, will compare billions of data points within milliseconds, logging the many, many errors in trying to find the answer until eventual success. at's inference. Training is the process of giving it an input and having the expected output. AI engineers now can also use what's called backpropagation, which takes the errors and divides up those across layers to back calculate the correct answer to more precisely provide a more accurate output response. us, when you put a new thing in, it gives you a better answer. While some companies (and banks) with the capital may invest in their own departments to Legal Industry Update By: Daniel C. Chilton