Overcoming Vocational Irony as AI Role Grows

A sure sign that a topic has progressed beyond trending and is now “trended” is when its acronym has firmly replaced the actual name.  Artificial intelligence, the theory and development of computer systems able to perform tasks that normally require human intelligence, such as speech recognition or decision-making, has been discussed by techies since at least the early 1970s.  However, its journey from something few understood to wide recognition of the verbal shortcut “AI” has happened in only a few short years. Now that it is mainstream there is a lot of discussion about how best to manage it and get the most from it.

Loretta Ibanez, Mortgage Innovation Director Single-Family Strategic Delivery for Freddie Mac, focuses on how companies use big data in their internal operations in a recent article in the company’s Perspectives blog.  She uses the concept of “vocational irony” as a starting point; the cobblers children has no shoes, the accountant who goes broke, and the technology company that provides cutting edge financial models for their customers maintains internal machinery firmly rooted in the 20th century.”

Management consulting firm McKinsey & Company recently published a study that looked into big data and the building influence of artificial intelligence and Ibanez said one point stood out to her:  "While investments in analytics are booming, many companies aren't seeing the ROI they expected. They struggle to move from employing analytics in a few successful use cases to scaling it across the enterprise, embedding it in organizational culture and everyday decision-making."  How, she asks, do companies ensure that big data and AI, which are transforming nearly every industry in the world, improve their own internal operational processes?

Freddie Mac’s venture into this brave new world started with the introduction of Loan Advisor Suite in 2016. This allowed the company’s customers, i.e. banks and mortgage lenders, access to Freddie’s risk assessment tools.  Loan Product Advisor, the Suite’s automated underwriting system, handles hundreds of thousands of single-family loan files and appraisals each month. It can assess borrowers who lack credit scores and is expanding use of its automated collateral evaluation (ACE) system to eliminate the requirement of an appraisal for many loans.  While these are ways to look at the mortgage experience differently and to make smarter decisions, she says, harkening back to the cobbler analogy, we have to make sure “that we aren't walking around barefoot while we accomplish our mission.”

In the days before companies could embed AI and machine learning into their internal operation processes, they had to make tradeoff decisions among time, money, workforce and – importantly – their computing power. Inevitably they put improving the customer experience at the top of the list. But now, with cheaper, scalable cloud computing available, it makes sense to apply it to internal operations in order to get more done, faster, and at lower cost. She says, “Not only can the cobbler's children have shoes, they can have the best shoes in town.”

To demonstrate how far artificial intelligence has come, Ibanez demonstrates how AI can be used for AI. Her company recently partnered with machine-learning company DataRobot to help its own data scientists find better methods for modeling historical data and are already finding ways to use machine learning algorithms in fast and powerful ways.

The old method of improving a predictive model would involve:

• Hand-coding programs to analyze the data.
• Using human experience to narrow the field of possible predictive model designs
• Manually run and re-run simulations and algorithms.
• Continuously adjust until a strong, predictive model was achieved.

This cycle typically took many, many months as humans waded through the thousands of available algorithms and data sets that have been introduced, using their best judgement to pick the correct data set and the correct algorithm.

AI provides the ability to rapidly analyze and experiment with any structured data. It doesn’t even need to know anything about the data to select the most likely algorithms to obtain the predictive information needed from it.  Using unsupervised machine learning it selects dozens of algorithms to run on the data, scanning and profiling the results and creating a “leaderboard” that identifies the best algorithms to use. It can even evaluate combinations of algorithms.  

That, the author says, is using artificial intelligence (algorithm) for artificial intelligence (algorithm selection).  It helps Freddie Mac improve its models faster and experiment with non-traditional data and will ultimately help Fannie Mae’s customers improve the loan origination process.

She concludes that, at a time when we're all drowning in email and data, we need to filter out the noise, and decide and focus on what is important. There are many instances of data-centric activities that require a person to apply judgment and make a decision; approving an invoice, flagging a suspicious transaction or identifying suspicious activity on an internal network.

“AI and machine-learning can help our employees make faster, better decisions on what might be an outlier, what we need to pay attention to, and what might be our next best step.” She urges that this is the time to do it, making it a part of Freddie Mac’s transformation into a better company helping to build a better housing finance system.