Access to credit is an instrumental foundation of modern business. For companies and individuals to be active participants in our global economy, it is crucial that they have equal access to credit – emphasis on the word equal. It is common knowledge that good credit must be earned, which in turn means that those with bad credit earned that as well, right? This is not always the case. In some countries, unequal access to credit has been a longstanding issue. While there are legal protections in place to avoid discrimination regarding credit, the nature of lending does not always ensure this. Fortunately, developments in AI and RPA may be changing this.
Traditionally, credit lending and loan applications have been processed manually by humans. Have you heard the phrase – „if it’s not broken, don’t fix it?“ Well, the traditional credit system is broken, and it’s time for an upgrade. With humans in charge of lending, it opens the door for a significant amount of risk. As with any human process, it faces the potential for human error, whether intentional or otherwise. One of these risks includes bias, which has been a prominent issue in the distribution of credit.
As much as we work to reduce it, bias is still prevalent in modern business. Whether based upon economic status, education level, or location, individuals are being locked out of the credit system due to discrimination. Approximately 26 million Americans are “credit invisible,” which means that they have no credit history, and as a result are unable to join the current credit scoring system. Even for those who are able to access and develop their credit score, millions are set at a disadvantage based upon bias.
In terms of credit lending, human bias often leads to unequal access to credit, prohibiting certain individuals from accessing the credit they deserve. As these individuals are disadvantaged from the start, a cycle of inequality is created – making it difficult for certain groups to obtain and improve their credit scores.
While there are laws in place to prohibit such discrimination, bias can slip through the cracks. Just because credit lending is done in compliance with these laws does not mean that inequality is not involved. As a result, firms are looking for alternative lending processes that will eliminate bias from credit scoring. Fortunately, there is a solution in the form of digitization.
AI and RPA
In recent years, more and more companies are looking to digitize their operations. Digitization often provides businesses with faster and more efficient processes, alleviating humans of repetitive and tedious tasks – in addition to eliminating the risk of human error. Digitization, particularly AI and RPA, can also provide businesses with more accurate information, thus improving decision-making. By analyzing historical data, computers can make faster and more accurate decisions, which can be applied to the credit scoring process.
One of the greatest benefits of transitioning from human to digital credit lending is eliminating any possibility for human error. Allowing a computer to perform credit checks and distribution ensures that there will be no bias involved in the decision-making process. The computer will utilize historical data and analyze relevant information to make a fair decision based strictly on factors such as income, assets, and credit history. With this data, the computer will determine an individual’s creditworthiness.
By automating the crediting process, companies can improve the equality of their credit scoring process. Through digitization, businesses and consumers are ensured that intentional biases are not considered during the scoring process, translating to more equal access to credit. Still, this process is not yet perfected and still undergoing testing. While developers can check their system algorithms for any sign of intentional biases, there may still be errors in the credit scoring process. While AI and RPA systems can automate credit scoring and eliminate the risk of human error, they are only as efficient and equal as the historical data they are fed. As there is a pattern of credit inequality in the historical data, a computer could recognize this and mistakenly continue the cycle of bias.
The current legal and regulatory structure in the US that allows for this financial discrimination is outdated, having been set in the 1960s and 1970s, when the society looked very different. For discrimination in the credit scoring process to be truly eliminated, current laws and regulations must be improved, in addition to business’ credit models as a whole.
Until this occurs, digitization is our best bet for an equal distribution of credit. While AI and RPA-enabled credit scoring may not entirely eliminate bias in the decision-making process, they have the potential to reduce it significantly. That being said, utilizing automated credit models may come with some trade-offs.
Implications of Digitized Credit Decisions
Due to the nature of automation, your computers can only operate as efficiently and equally as they are programmed to. If there is bias present in the historical data consumed by a computer, it is inevitable that some bias will be output by the system. If not adequately controlled, AI-enabled technology may perform unintentional proxy discrimination. This means that if a computer is programmed to analyze data with the explicit instruction to avoid specific attributes, the computer will do so. But, it will find a proxy for these features, thus perpetuating a cycle of discrimination.
For example, the computer may substitute one identifying factor for another, such as geographical location, income, or education and begin to discriminate based upon this. While bias will no longer be an intentional consideration, the credit scoring process will not be entirely equal. This error can be challenging to identify if personnel are not explicitly looking for it, so it is critical to be mindful of the potential of a proxy.
In addition to the potential of discrimination proxy, computers may also face the risk of programming bias. If the human programming the computer or application inadvertently teaches or programs with bias, the system will continue to make the same systems as humans, but with expedited processing speed.
As with any solution, it comes with trade-offs. AI and RPA, if carefully monitored, can be instrumental in reducing bias in the credit decision-making process. Even so, for it to be effective, credit lenders have two options; scrub their historical data to eliminate bias patterns or design a new model from scratch. Fortunately, AI technology can be programmed to address these issues.
While programming bias is more challenging to avoid, machine-learning software can create decision-making models that eliminate the consideration of bias while simultaneously identifying and avoiding proxy discrimination.
With AI and RPA-enabled computers, companies can minimize the trade-offs of reducing bias in their credit scoring processes. For the sake of lenders and beneficiaries, it should be a priority to equalize the distribution of credit to consumers. Ensuring that credit is scored and lent depending only on relevant factors such as income, assets, and credit history should be the standard. With improved equality in the credit decision process, lenders can create a more inclusive and fair financial model, allowing businesses and consumers to thrive.
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