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Bias isn't the only problem with credit scores--and no, AI can't help

MIT Technology Review

But in the biggest ever study of real-world mortgage data, economists Laura Blattner at Stanford University and Scott Nelson at the University of Chicago show that differences in mortgage approval between minority and majority groups is not just down to bias, but to the fact that minority and low-income groups have less data in their credit histories. This means that when this data is used to calculate a credit score and this credit score used to make a prediction on loan default, then that prediction will be less precise. It is this lack of precision that leads to inequality, not just bias. The implications are stark: fairer algorithms won't fix the problem. "It's a really striking result," says Ashesh Rambachan, who studies machine learning and economics at Harvard University, but was not involved in the study.


AI Can Make Bank Loans More Fair

#artificialintelligence

As banks increasingly deploy artificial intelligence tools to make credit decisions, they are having to revisit an unwelcome fact about the practice of lending: Historically, it has been riddled with biases against protected characteristics, such as race, gender, and sexual orientation. Such biases are evident in institutions' choices in terms of who gets credit and on what terms. In this context, relying on algorithms to make credit decisions instead of deferring to human judgment seems like an obvious fix. What machines lack in warmth, they surely make up for in objectivity, right? Sadly, what's true in theory has not been borne out in practice.


Crowd, Lending, Machine, and Bias

arXiv.org Artificial Intelligence

Big data and machine learning (ML) algorithms are key drivers of many fintech innovations. While it may be obvious that replacing humans with machine would increase efficiency, it is not clear whether and where machines can make better decisions than humans. We answer this question in the context of crowd lending, where decisions are traditionally made by a crowd of investors. Using data from Prosper.com, we show that a reasonably sophisticated ML algorithm predicts listing default probability more accurately than crowd investors. The dominance of the machine over the crowd is more pronounced for highly risky listings. We then use the machine to make investment decisions, and find that the machine benefits not only the lenders but also the borrowers. When machine prediction is used to select loans, it leads to a higher rate of return for investors and more funding opportunities for borrowers with few alternative funding options. We also find suggestive evidence that the machine is biased in gender and race even when it does not use gender and race information as input. We propose a general and effective "debasing" method that can be applied to any prediction focused ML applications, and demonstrate its use in our context. We show that the debiased ML algorithm, which suffers from lower prediction accuracy, still leads to better investment decisions compared with the crowd. These results indicate that ML can help crowd lending platforms better fulfill the promise of providing access to financial resources to otherwise underserved individuals and ensure fairness in the allocation of these resources.


Investigating bankruptcy prediction models in the presence of extreme class imbalance and multiple stages of economy

arXiv.org Machine Learning

In the area of credit risk analytics, current Bankruptcy Prediction Models (BPMs) struggle with (a) the availability of comprehensive and real-world data sets and (b) the presence of extreme class imbalance in the data (i.e., very few samples for the minority class) that degrades the performance of the prediction model. Moreover, little research has compared the relative performance of well-known BPM's on public datasets addressing the class imbalance problem. In this work, we apply eight classes of well-known BPMs, as suggested by a review of decades of literature, on a new public dataset named Freddie Mac Single-Family Loan-Level Dataset with resampling (i.e., adding synthetic minority samples) of the minority class to tackle class imbalance. Additionally, we apply some recent AI techniques (e.g., tree-based ensemble techniques) that demonstrate potentially better results on models trained with resampled data. In addition, from the analysis of 19 years (1999-2017) of data, we discover that models behave differently when presented with sudden changes in the economy (e.g., a global financial crisis) resulting in abrupt fluctuations in the national default rate. In summary, this study should aid practitioners/researchers in determining the appropriate model with respect to data that contains a class imbalance and various economic stages.


Credit denial in the age of AI

#artificialintelligence

Banks have been in the business of deciding who is eligible for credit for centuries. But in the age of artificial intelligence (AI), machine learning (ML), and big data, digital technologies have the potential to transform credit allocation in positive as well as negative directions. Given the mix of possible societal ramifications, policymakers must consider what practices are and are not permissible and what legal and regulatory structures are necessary to protect consumers against unfair or discriminatory lending practices. In this paper, I review the history of credit and the risks of discriminatory practices. I discuss how AI alters the dynamics of credit denials and what policymakers and banking officials can do to safeguard consumer lending.


Ai bankability: 10 ways artificial intelligence is transforming banking

#artificialintelligence

With plenty of post-recession anti-banking sentiment still lingering, it's common to see fintech and traditional banks framed in oppositional terms. There's some truth to that, especially with disruption-minded digital-only banks, but technological innovations have transformed banking of all stripes -- and nowhere is that clearer than with artificial intelligence. AI has impacted every banking "office" -- front, middle and back. That means even if you know nothing about the way your financial institution uses, say, complex machine learning to fend off money launderers or sift through mountains of data for fraud-related anomalies, you've probably at least interacted with its customer service chatbot, which runs on AI. Read on to learn how else AI is transforming the way banks operate, from investment assistance and consumer lending to credit scoring, smart contracts and more.


AI bankability: 10 ways artificial intelligence is transforming banking

#artificialintelligence

With plenty of post-recession anti-banking sentiment still lingering, it's common to see fintech and traditional banks framed in oppositional terms. There's some truth to that, especially with disruption-minded digital-only banks, but technological innovations have transformed banking of all stripes -- and nowhere is that clearer than with artificial intelligence. AI has impacted every banking "office" -- front, middle and back. That means even if you know nothing about the way your financial institution uses, say, complex machine learning to fend off money launderers or sift through mountains of data for fraud-related anomalies, you've probably at least interacted with its customer service chatbot, which runs on AI. Read on to learn how else AI is transforming the way banks operate, from investment assistance and consumer lending to credit scoring, smart contracts and more.


AI and the bottom line: 15 examples of artificial intelligence in finance

#artificialintelligence

Artificial intelligence has given the world of banking and the financial industry as a whole a way to meet the demands of customers who want smarter, more convenient, safer ways to access, spend, save and invest their money. We've put together a rundown of how AI is being used in finance and the companies leading the way. A recent study found 77% of consumers preferred paying with a debit or credit card compared to only 12% who favored cash. But easier payment options isn't the only reason the availability of credit is important to consumers. Having good credit aids in receiving favorable financing options, landing jobs and renting an apartment, to name a few examples.


Small and midsize banks can't shy away from AI

#artificialintelligence

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.


Deep Generative Models for Reject Inference in Credit Scoring

arXiv.org Machine Learning

Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. In this research, we use deep generative models to develop two new semi-supervised Bayesian models for reject inference in credit scoring, in which we model the data generating process to be dependent on a Gaussian mixture. The goal is to improve the classification accuracy in credit scoring models by adding reject applications. Our proposed models infer the unknown creditworthiness of the rejected applications by exact enumeration of the two possible outcomes of the loan (default or non-default). The efficient stochastic gradient optimization technique used in deep generative models makes our models suitable for large data sets. Finally, the experiments in this research show that our proposed models perform better than classical and alternative machine learning models for reject inference in credit scoring.