However, NAMs (likeallGAMs), are not causal models.

According to Grand View Research, the global artificial intelligence (AI) market was worth an estimated $62 billion in 2020 but could grow 40% annually through 2028. If you don't yet have AI stocks in your long-term portfolio, it might be time to start thinking about it. The market's recent sell-off of technology and high-growth companies has created a great buying opportunity for bold and patient investors. Here are three top AI stocks building moats around their algorithms and whose stocks are attractively priced today. Technology can change at a blistering pace, and nobody can know for sure that the winners of today will still hold their crown tomorrow, a year from now, or a decade from now.

Artificial intelligence (AI) is rapidly becoming more prevalent in today's society, reaching into industries that many investors never would have thought. Deere & Company, for example, is bringing artificial intelligence and machine learning to tractors with a fully autonomous tractor that can plow, harvest, and plant crops without a driver. With Deere, among other companies, it is clear that artificial intelligence is making its way into nearly every part of our world, and there are three companies you can invest in today that I think could be the best companies to capitalize on it. Upstart Holdings (NASDAQ:UPST) is bringing AI to a very old market: loan determinations. For decades, Fair Isaac has ruled the loan determination space with its FICO score, but it is a flawed system.

Dave Girouard, the chief executive of the AI lending platform Upstart Holdings Inc. UPST, -2.51% in Silicon Valley, understood the worry. "The concern that the use of AI in credit decisioning could replicate or even amplify human bias is well-founded," he said in his testimony at the hearing. But Girouard, who co-founded Upstart in 2012, also said he had created the San Mateo, Calif.-based company to broaden access to affordable credit through "modern technology and data science." And he took aim at the shortcomings he sees in traditional credit scoring. The FICO score, introduced in 1989, has become "the default way banks judge a loan applicant," Girouard said in his testimony.

Artificial Intelligence (AI)-driven lending practices are gaining visibility and credibility. AI tools used with machine learning can analyze more data for a more accurate answer to loan requests. Lenders using new AI systems can evaluate bank account balances calculated with purchase history, social media habits, and utility payments to determine a person's creditworthiness. Those without established credit can benefit greatly from AI lenders. New startup lenders are using AI to approve personal loans for people with a short or non-existing credit history who have a reliable income and a high earning potential.

We've been led to believe that A.I. is going to solve all of our problems - economically, socially, environmentally. It stretches credulity that it can do that when all it does is find patterns in numbers. But what it is capable of - in that limited role - is dangerous. Nevertheless, A.I.'s inevitability, predicted by industry, academics, and industry analysts, goes without question. That the issues of bias, exclusion, and disinformation are social problems that cannot be addressed with pattern-matching and curve fitting, and cannot satisfactorily be dealt with by technology is a strength, not a weakness of the inevitability narrative.

Fear, uncertainty and doubt are tainting the banking industry's views of artificial intelligence. There's so much noise about AI, it's reminiscent of irrational fears about electricity or even the microwave -- it's going to take away our jobs, is more dangerous than nuclear weapons and will have a negative impact on our cities. From my perspective, it's important to be cautious when we evaluate new technologies, but I'm an optimist at heart. I believe in the power of technology to create value and transform lives. The individuals who are responsible for AI have the capacity to create guardrails and ensure that these new approaches to data science do not have a negative impact.

Machine learning has the potential to democratize access to credit. It can expand the pool of people qualified to obtain credit -- most notably low- and moderate-income (LMI) borrowers -- and decrease the cost of that credit. It also can increase access to credit and reduce systemic risk by allowing different banks to analyze different factors, and thereby generate different results in a way that the existing, FICO-based system discourages. The greatest current obstacle to this development is pressure from the banking regulators to continue adhering to the status quo system, lest machine learning produce an unfortunate outcome. Perversely, that system already contains the very flaws that regulators have expressed about machine learning.

This paper proposes a two-stage scoring approach to help lenders decide their fund allocations in the peer-to-peer (P2P) lending market. The existing scoring approaches focus on only either probability of default (PD) prediction, known as credit scoring, or profitability prediction, known as profit scoring, to identify the best loans for investment. Credit scoring fails to deliver the main need of lenders on how much profit they may obtain through their investment. On the other hand, profit scoring can satisfy that need by predicting the investment profitability. However, profit scoring completely ignores the class imbalance problem where most of the past loans are non-default. Consequently, ignorance of the class imbalance problem significantly affects the accuracy of profitability prediction. Our proposed two-stage scoring approach is an integration of credit scoring and profit scoring to address the above challenges. More specifically, stage 1 is designed as credit scoring to identify non-default loans while the imbalanced nature of loan status is considered in PD prediction. The loans identified as non-default are then moved to stage 2 for prediction of profitability, measured by internal rate of return. Wide and deep learning is used to build the predictive models in both stages to achieve both memorization and generalization. Extensive numerical studies are conducted based on real-world data to verify the effectiveness of the proposed approach. The numerical studies indicate our two-stage scoring approach outperforms the existing credit scoring and profit scoring approaches.

How are advances in artificial intelligence and machine learning changing credit risk assessment? That's a topic I'll be exploring in three presentations at FICO World 2018, April 16-19 in Miami Beach. These three presentations have a common thread: How FICO applies artificial intelligence and machine learning not as a "silver bullet" but carefully balanced with human domain expertise in the formulation of problems and models. FICO Scores are among the most scrutinized models in the world, so would you expect modern machine learning -- with its challenges around explainability -- to bring great benefits to FICO Score R&D and production? On Tuesday, April 17, 1:30-2:30, my colleague Ethan Dornhelm and I will show that machine learning offers tremendous efficiencies for research "in the lab". But in order to bring these efficiencies to the field in the form of a new international FICO Score, we benefitted from blending the latest machine learning algorithms with FICO's own explainable and palatable Scorecard development technology.