Narayan Bharadwaj

Narayan Bharadwaj

SVP, Automation

With the flood of technology solutions out in the market, there is too much noise! Lenders are constantly bombarded with solutions containing sound-bytes like Artificial intelligence (AI), Intelligent Automation, Blockchain etc.

It is important for lenders to separate the signal from noise. These buzzwords are great for valuation and to get the creative juices of marketers flowing, but lenders should deconstruct the mystery behind these phrases and find out what it REALLY means for their businesses.

  • Artificial intelligence (AI) - Every other solution touts to have an "AI" component. This is a very deep topic and what is swept under the broader term of AI are really Machine Learning (ML) algorithms. The term "Machine learning", inherently implies that there is a "learning" component to the decision model upon which any solution is built. In order for a decision model to work effectively, the learning component of the model must have the ability to "look back" into a massive dataset to identify patterns, match with the incoming data from transaction systems, identify if there is a pattern and then predict the outcome if there is no defined precedent. In the world of transaction fraud management (going back to my roots), say a suspicious transaction is incoming, the model runs real-time and predicts if it could be a fraud or not if there is a matching pattern against the transaction dataset history. Amidst all the marketing hype, let's think hard about where this could be applied in the mortgage industry:
  • The most prevalent usage of machine learning algorithms is to recognize documents in OCR and document classification systems. However, even here, the promise is that these classification systems can recognize documents as they are thrown at them. This is farthest from the truth. Many of the doc classification systems do not have the capability to recognize documents previously unclassified and make a determination during run-time. Every new document needs to be fed into a model, trained with a dataset of examples and then rolled into production. This takes time and anything you hear to the contrary needs to be evaluated and tested critically with actual examples, rather than powerpoint ware!

  • The most often quoted use case for AI is that it is used in decision making. This is once again a creative spin. A majority of most lender's originations are conforming to the GSE and investor guidelines. So the lender is essentially following a set of defined, binary rules to originate a loan according to the guidelines. When you hear "AI-powered" decision management systems, where is the AI in this? Is it in credit, income, asset or collateral evaluation where lenders deal with uncertain data that AI will magically eliminate and increase your originations? Under the current structure of credit evaluation, if a borrower does not meet the underwriting criteria, which lender is using alternate credit scoring models that dynamically assess a borrower's ability to pay based on factors like utility bills and cell phone bill payments? Which investor today is accepting these as surrogate credit criteria? When these are relaxed and lenders are allowed to use alternate credit scoring models as a surrogate, then there is some merit to it (not to mention the fact that alternate credit scoring variables will also be defined by investors, so the model will still not deal with uncertainty!). But when a lender is originating to conform to a set of very clear, prescribed guidelines with a finite set of data points to evaluate those against, there is no AI involved in it!

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