Algorithmic Bias in Finance
In recent years, both traditional banks and the fintech industry have increasingly turned to advanced algorithms and machine learning techniques to make more precise and profitable decisions regarding creditworthiness for loan applications. The application of AI technology in finance aims to lower costs, expand economic opportunities, enhance financial inclusion, and improve market functionality. These algorithms play a crucial role in various financial processes, from credit scoring models, product design, marketing, and portfolio monitoring to underwriting, ID verification, fraud detection, and collection.
Algorithms have emerged as a solution to combat racial biases, both implicit and explicit, present in traditional face-to-face lending and digital financial services. A study by the Haas School of Business found that fintech algorithms discriminated 40 percent less on average than human loan officers in loan pricing. Furthermore, algorithms showed no discrimination in accepting and rejecting loans. Well-developed algorithms are designed to maximize benefits, provide accurate decisions, counter human biases, and enhance the fairness, speed, and accuracy of disbursing loans to those previously denied access.
Despite the increasing digitization and automation of financial services, the human element of influence or bias has not been entirely eliminated. According to a UC Berkeley study, data sources like the Home Mortgage Disclosure Act (HDMA) can be merged with other large datasets to provide detailed insights on interest rates, loan terms, property location, and borrower credit with race and ethnicity information. The analysis revealed significant overcharging of minority borrowers by both traditional and digital lenders.
Fintech algorithmic models exhibited data biases, leading to higher interest rates for minorities based on profiling related to shopping behavior and geography.
In recent years, lenders have started considering nontraditional data unrelated to creditworthiness, sourced from internet search histories, shopping patterns, social media activity, and other consumer-related inputs. While this approach has the potential to improve financial inclusion, it also poses risks of unfair or discriminatory lending decisions if not appropriately implemented and monitored.
Impact of Algorithmic Bias
Algorithms act as gatekeepers to socio-economic opportunities across multiple sectors, influencing decisions related to affordable credit, housing, car purchases, business investments, healthcare access, employment, government programs, resources, and education. Algorithmic bias is concerning due to its potential to amplify or reinforce entrenched social biases and discriminations in society.
Algorithmic bias creates unjust and discriminatory decisions that favor certain groups over others, contributing to social injustice and inequality. Instances of algorithmic biases on social media networks have been observed, where algorithms create filter bubbles that show content aligning with users’ preferences, potentially leading to less tolerance for opposing views and societal polarization.
In societies with persistent gender inequalities, algorithmic biases may accentuate existing biases, further marginalizing and underrepresenting women. Vulnerable populations, including minorities, low-income groups, micro and small businesses, are most affected by algorithmic biases in the finance industry. Many of these groups struggle to access credit, making financial inclusion crucial for narrowing global inequality gaps.
Achieving algorithmic fairness is essential for progressing toward an inclusive society. To address and pursue fairness, it is crucial to identify, study, and understand how, where, and why biases are present in algorithms.
