Article | “Is Your Firm Ready for Machine Learning?”

Source: deBanked | 2018 Author: Cheryl Winokur Munk Artificial intelligence such as machine learning has the potential to dramatically shift the alternative lending and funding landscape.

But humans still have a lot to learn about this budding field.

Across the industry, firms are at different points in terms of machine learning adoption.

Some firms have begun to implement machine learning within underwriting in an attempt to curb fraud, get more complex insights into risk, make sounder funding decisions and achieve lower loss rates.

Others are still in the R&D and planning stage, quietly laying the groundwork for future implementation across multiple areas of their business, including fraud prevention, underwriting, lead generation and collections.

“It’s entirely critical to the success of our business,” says Paul Gu, co-founder and head of product at Upstart, a consumer lending platform that uses machine learning extensively in its operations.

“Done right, it completely changes the possibilities in terms of how accurate underwriting and verification are,” he says.

“It’s entirely critical to the success of our business,” says Paul Gu, co-founder and head of product at Upstart, a consumer lending platform that uses machine learning extensively in its operations.

“Done right, it completely changes the possibilities in terms of how accurate underwriting and verification are,” he says.

While there’s no absolute right way to implement machine learning within a lender’s or funder’s business, there are many data-related, regulatory and business-specific factors to consider.

Because things can go very wrong from a business or regulatory perspective—or both—if machine learning is not implemented properly, firms need to be especially careful.

Here are a few pointers that can help lead to a successful machine learning implementation: Using machine learning, funders can predict better the likelihood of default versus a rule-based model that looks at factors such as the size of the business, the size of the loan and how old the business is, for example, says Eden Amirav, co-founder and chief executive of Lending Express, a firm that relies heavily on AI to match borrowers and funders.

Machine learning takes hundreds and hundreds of parameters into account which you would never look at with a rule-based model and searches for connections.

“You can find much more complex insights using these multiple data points.

It’s not something a person can do,” Amirav says.

He contends that machine learning will optimize the number of small businesses that will have access to funding because it allows funders to be more precise in their risk analyses.

This will open doors for some merchants who were previously turned down based on less precise models, he predicts.

To help in this effort, Lending Express recently launched a new dashboard that uses AI-driven technology to help convert business loan candidates that have been previously turned down into viable applicants.

The new LendingScore™ algorithm gives businesses detailed information about how they can improve different funding factors to help them unlock new funding opportunities, Amirav says.

Lenders and funders always have to be thinking about what’s next when it comes to artificial intelligence, even if they aren’t quite ready to implement it.

While using machine learning for underwriting is currently the primary focus for many firms, there are many other possible use cases for the alternative lenders and funders, according to industry participants.

Lead generation and renewals are two areas that are ripe for machine learning technology, according to Paul Sitruk, chief risk officer and chief technology officer at 6th Avenue Capital, a small business funder.

He predicts that it is only a matter of time before firms are using machine learning in these areas and others.

“It can be applied to several areas within our existing processes,” he says.

Collection is another area where machine learning could make the process more efficient for firms.

Machines can work out, based on real-life patterns, which types of customers might benefit from call reminders and which will be a waste of time for lenders, says Sandeep Bhandari, chief strategy and chief risk officer at Affirm, which uses advanced analytics to make credit decisions.

“There are different business problems that can be solved through machine learning.

Lenders sometimes get too fixated on just the approve/decline problem,” he says.

“Most underwriters don’t have enough data to effectively incorporate AI, deep learning, or machine learning tools,” says Taariq Lewis, chief executive of Aquila, a small business funder.

He notes that effective research comes from the use of very large datasets that won’t fit in an excel spreadsheet for testing various hypotheses.

Problems, however, can occur when there’s too much complexity in the models and the results become too hard to understand in actionable business terms.

For example, firms may use models that analyze seasonal lender performance without understanding the input assumptions, like weather impact, on certain geographies.

This may lead to final results that do not make sense or are unexpected, he says.

“There’s a lot of noise in the data.

There are spurious correlations.

They make meaningful conclusions hard to get and hard to use,” he says.

The more precise firms can be with the data, the more predictive a machine learning model can be, says Bhandari of Affirm.

So, for example, instead of looking at credit utilization ratios generally, the model might be more predictive if it includes the utilization rate over recent months in conjunction with debt balance.

It’s critical to include as targeted and complete data as possible.

“That’s where some of our competitive advantages come in,” Bhandari says.

Underwriters also have to pay particularly close attention that overfitting doesn’t occur.

This happens when machines can perfectly predict data in your data set, but they don’t necessarily reflect real world patterns, says Gu of Upstart.

Keeping close tabs on the computer-driven models over time is also important.

The model isn’t going to perform the same all along because the competitive environment changes, as do consumer preferences and behaviors.

“You have to monitor what’s going well and what’s not going well all the time,” Bhandari says.

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