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72 a much more efficient way to manage capital market price and time of trade, and to reduce trade fails. But these platforms do not address the validation of loan documents and data to identify any data inconsistencies. In a single file, you could have a piece of data that says one thing, and documents that say something else entirely—and no means to reconcile the two. Data ingestion is another factor that contributes to variability because of the fact some institutions have modified their systems, so they are capable of boarding a sizable amount of data, while other institutions haven't. Even though there is more loan data available than ever before, those that are slow to adopt have no place to store it—so they stick with spreadsheets or archive the trade tape, figuring that as long as they have the data somewhere, it's okay. Most secondary market technology platforms have also failed to solve two of the biggest issues MSR traders face, the first of which is timing. Given how quickly prices change in the capital markets, spending days or weeks on MSR acquisitions can be costly and result in the loss of better opportunities. e second issue is the functional fulfillment of the loan and making sure that you bought what you thought you did. is is where things can get clumsy, given the overall lack of data quality and the high degree of variation in document ordering and naming from sellers. In the past, the spreadsheet became the great equalizer, using macros to map data and manual processes to determine if data was missing and to enter it into the buyer's systems. at approach is neither accurate nor scalable. On the origination side of the business, consumer demand for a simpler, faster, digital experience has motivated lenders to adopt increasingly higher levels of automation. It's quite a different story in the secondary market, where institutions have been slower to implement automated tools and seem to remain loyal to embedded manual practices. In fact, capabilities already exist that could enable institutions to conduct MSR trades and onboard loans in a fraction of the time it currently takes. With the increasing adoption of these new technologies, however, things are starting to change. UNDERSTANDING AI'S IMPACT One of the most exciting things happening in the secondary market today is the emergence of AI technology, as well as machine learning tools, which are a subset of AI. AI describes technologies that analyze data and make decisions based on data patterns, whereas machine learning describes technologies that learn to distinguish patterns in data from human instruction and self-learning algorithms. In truth, AI and machine learning, while often talked about, are not as commonplace as many are led to believe. ey can create the uniformity these institutions need through the data normalization they provide across sellers' loan files, making it much easier to ingest documents and data accurately. is then can lead to the use of more sophisticated applications that can significantly help servicers and investors gain greater insights on their portfolios and their trading decisions, especially when determining where risk lies and which loans to sell versus which loans to retain. AI and machine learning technology also enable MSR traders to capture a greater amount of data off loan files and then automatically run business rules around that data. is eliminates the historic "stare and compare" methods of checking data and loan quality and greatly reduces the time and overall cost of due diligence. By green-lighting the vast majority of files in which the data can be trusted, companies can focus on exception-based processing, which allows them to move loans forward much more efficiently. A particular benefit of these technologies lies in the fact that different investors look for different data elements when determining risk. ere's a huge variation among loan purchasers in terms of the data elements that they are concerned about. Certain buyers require checks on 40 different data fields, while others may want to look at 30 or 50—there's really no consistency among them. eir legal agreements all differ as well. If an investor needs to check 30 unique data elements when buying a pool of Feature By: Craig Riddell An abundance of diligence questions along with legacy systems with wildly differing customizations has led to a lack of any type of consistency or uniformity in how institutions behave in the secondary market today. As a result, an institution that sells loans of the exact same asset class to both Buyer A and Buyer B is going to have two totally different experiences. This lack of continuity really makes it cumbersome for all parties involved.