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64 synonymously, but, in fact, they are not exactly alike. While one is a subset of the other, it is important to understand the distinctions. Generally speaking, AI is used to describe any technology capable of applying knowledge to solve complex problems and find the optimal solutions related to a specific task or a range of tasks. Machine learning, on the other hand, is more of a subset of AI. It is a system that is "trained" to complete a task on its own based on large datasets, human instruction and self- learning algorithms that can recognize patterns. Rather than a focus on decision-making, it learns from sample training sets to maximize performance and increase the accuracy of the results from what it is trained to do. For example, machine learning can be applied to the classification of loan file documents with a focus on speed and accurate identification. AI, on the other hand, might look across data sets and loan file documents to find the optimal answer for a credit decision. Both technologies have huge potential for use in the financial industry, and banks, insurers, and lenders are already spending big on each. According to PwC, in 2017 more than half of financial services firms said they had made a substantial investment in AI, while two-thirds said they planned to do so within the next three years. More recently, Fannie Mae's Lender Sentiment Survey reported familiarity with AI and machine learning technology was 63 percent, with 27 percent of firms indicating it is currently deployed. CHALLENGES FOR THE MORTGAGE INDUSTRY Because of the sheer volume of data involved in lending and the complexity of mortgage transactions, the mortgage industry has the opportunity to use AI and machine learning differently than other industries, with broad use cases across the mortgage process. In the exploration of these tools, there appears to be just as much focus on the borrower experience as with improving overall operational efficiency, with anomaly detection automation being the highest priority, according to Fannie Mae. When processing mortgage documents, lenders and servicers need to make decisions with a high degree of specificity, increasing the need to extract and verify more data from loan files and documentation. With hundreds of data points and thousands of documents, manual practices involving spreadsheets and off-line tasks are no longer practical or safe; in fact, they never really were to begin with. In recent years, optical character recognition (OCR) technology has been used to "grab" data from paper documents. eoretically, this means humans no longer need to look at documents to get data off of them. However, OCR is essentially just a data picker—and an imperfect one at that. Its use in data extraction has limitations, capturing only a portion of data found in structured documents using a template approach with a heavy reliance on the data in question being in generally the same location on the document every single time. Human operators are still then required to verify and validate the data. Today, machine learning has begun to augment OCR technology, improving both the transparency and auditability of loan file details. OCR becomes the starting point, creating big blocks of data that represent the input into machine learning models. ese models are "trained" to recognize patterns through textual analysis that can classify both structured and unstructured documentation. From these documents, data is more efficiently and accurately extracted and structured. is "purified data" can then power rules- based logic to evaluate loan quality, identify defects, and clear conditions. Taking the next step, more sophisticated AI applications can also consume this data to further automate decision making from origination to servicing. AI and machine learning work together to power applications that can guide borrowers through the lending process and support them after close by identifying borrowers who may need help making payments or identify when refinancing or a home equity loan may be a good idea for a borrower. The mortgage industry, with its byzantine regulations and myriad processes, could stand to gain the most by adopting AI and machine learning. It's already starting to happen—and there are benefits for every segment of the mortgage lifecycle, including for default servicing.