One of the main challenges of using machine learning for loan default prediction is that of securing a large, representative, and high-quality dataset for model training. A machine learning model can only learn patterns from the data it is trained on, so it is critical to have a dataset that accurately reflects the full variety of factors that could influence loan repayment behavior. Acquiring comprehensive historical data on past borrowers, their loan characteristics, and accurate repayment outcomes can be difficult, costly, and may still not capture every relevant variable. Missing or incomplete data can reduce model performance.
The loan market is constantly changing over time as economic conditions, lending practices, and borrower demographics shift. A model trained on older historical data may not generalize as well to new loan applications. Frequent re-training with recent and expanding datasets helps address this issue but also requires significant data collection efforts on an ongoing basis. Keeping models up-to-date is an operational challenge.
There are also risks of bias in the training data influencing model outcomes. If certain borrower groups are underrepresented or misrepresented in the historical data, it can disadvantage them during the loan application process through model inferences. Detecting and mitigating bias requires careful data auditing and monitoring of model performance on different demographic segments.
Another concern is that machine learning models are essentially black boxes – they find patterns in data but do not explicitly encode business rules or domain expertise about lending into their structure. There is a lack of transparency into exactly how a model arrives at its predictions that administrators and regulators may find undesirable. Efforts to explain model predictions can help but are limited.
Relatedly, it can be difficult to verify that models are compliant with evolving laws and industry best practices related to fair lending since their internal workings are opaque. Any discriminatory or unethical outcomes may not be easily detectable. Regular model monitoring and auditing is needed but not foolproof.
Machine learning also assumes the future will closely resemble the past, but loan default risk depends on macroeconomic conditions which can change abruptly during downturns in ways not seen in prior training data. This exposes models to unexpected concept drift that reduces their reliability unless rapidly re-trained. Ensuring robustness to concept drift is challenging.
There are also technical issues around developing reliable thresholds for classifying applicants as likely to default or not based on a machine learning model’s continuous risk score predictions. Small differences in scores near any threshold could incorrectly categorize some applicants. Setting thresholds requires testing against real-world outcomes.
Another technical challenge is ensuring predictions remain stable and consistent for any given applicant and do not fluctuate substantially with small changes to initial application details or as more application data becomes available. Significant instability could undermine trust in model assessments.
More fundamentally, accurately predicting loan defaults remains quite difficult using any method since real-world financial stressors and behaviors are complex, context-specific and sometimes unpredictable. There are also incentive issues around applicants potentially gaming a fully transparent predictive system to appear lower risk than reality. Machine learning may only be able to improve traditionally high default rates by a modest amount.
When used decisively without any human judgment also, machine learning risk assessments could potentially deny access to formal credit for valid subprime borrowers and push them to much riskier informal alternatives. A balanced, responsible use of automated evaluations along with specialist reviews may be optimal to maximize financial inclusion benefits while controlling defaults.
While machine learning models avoid requiring manual encoding of lending expertise, their assessments are still just formalizing empirical patterns within specific dataset limitations. There are intangible moral, social and cultural factors surrounding credit and debt which no technology can fully comprehend. Completely automating lending decisions without appropriate human oversight also raises ethical concerns around accountability and bias. Prudently integrating machine-guided decisions with traditional credit analysis may be preferable.
Machine learning shows promise to help better evaluate loan default risk at scale but its applications must be done judiciously with a recognition of its limitations to avoid harm. Significant challenges remain around securing quality data, addressing bias, regulatory compliance, robustness to changing conditions, setting accurate thresholds, ensuring stable predictions, and maintaining the right balance between man and machine in consequential financial matters. Careful development and governance processes are necessary to realize its full potential benefits while minimization any downsides.