A rich value proposition made easy and simple As in any business, time is money. With machine learning, non-credit worthy applicants can be more quickly eliminated from consideration with no human intervention. Weeding out less credit-worthy loan applicants earlier means no costly and time-consuming queries to third party credit rating firms for these applicants. The technology also gives lenders greater assurance of the credit worthiness of those to whom credit is extended, owing to the far more extensive array of datasets analyzed to reach a credit approval status. The cloud-basedmachine learning as-a-servicemodel for modernizing and streamlining credit risk determination is easy. Literally nothingmore is needed other than a continuous stream of data, fed into the system by way of very user-friendly interfaces that any business analyst can use. Plus the security of customer data is virtually guaranteed because all personal information sent to the machine learning model is either encrypted or left out. The machine doesn’t care who the data is from, rather only what information is actually in the data. According to ESG’s Rouda, “Most services have set up data migration or loading functions to work with common sources, so it shouldn’t be too much work to import data to cloud services. That said, good data governance is important in any setting, as bad or badly managed data will train the model to be inaccurate.” This is simplicity that rivals the instant cake mixes marketing under the Betty Crocker brand in the 1950s by General Mills. All you had to do was add water and an egg and reap the output from the oven an hour later. So it is with machine learning for credit risk determination when served up in an as-a-service model. Just add data, as much as you have, and the rest is pure business benefit.
“Most services have set up data migration or loading functions to work with common sources, so it shouldn’t be too much work to import data to cloud services.” – NIK ROUDA,
SENIOR ANALYST AND MACHINE LEARNING SPECIAL I ST AT ESG
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