AI in Finance: Detecting Potential Bias in Financial Services AI Models

AI in Finance

How AI bias can disrupt and put financial services at risk?

Over the years, technology has an extensive impact on every aspect of finance, from transforming decades-old practices to reshaping core financial tasks. The development of artificial intelligence (AI) is increasingly contributing to this transition by bringing promises to streamline financial workflows. AI algorithms have the potential to detect fraud, make trading decisions, suggest banking products, and analyze loan applications, among others. 

It also presents an opportunity to transform how financial services providers allocate credit and risk and create fairer, more inclusive systems. However, the algorithms the technology use can create a potential bias and can go in the direction that foster biased credit allocation while making discrimination in lending even harder to find. This may weaken entire financial purposes. 


AI Bias in Finance

Financial service providers have long employed statistical and probability models as well as predictive analytics to foresee performance. They leverage AI and machine learning algorithms to consider data to assess creditworthiness. Despite offering immense benefits, these technologies can perpetuate biases as they work based on algorithms created by humans. 

The use of AI in this sector is also introducing new ethical drawbacks, creating unintended biases that are forcing financial services firms to reflect on the ethics of new models.

Essentially, as AI algorithms learn from data, any past partiality within an organization’s data can quickly create biased AI that creates decisions on unfair datasets. These biases in the AI system can take a number of forms. For instance, e-commerce giant Amazon, in 2014, built an internal AI tool for the selection of the most promising candidates by evaluating their job applications, particularly their CVs. However, the software quickly taught itself to prefer male candidates over female ones, penalizing CVs that involved the words ‘women’, which would often refer to women-only clubs. The software also relegated graduates from two women colleges. Afterward these issues, the company eventually lost faith in the impartiality of the system and abandoned the project.

Interpreting AI’s behavior is critical to detecting and dodging models that discriminate against or eliminate marginalized individuals or groups. But AI systems are only as good as the data we put into them. Many AI systems will continue to be trained using certain data, as subconscious bias or lack of diversity among development teams may influence how AI is trained, creating an ongoing cycle of bias and furthering biases forwards in the model.


AI Fairness in Financial Services

Certainly, the bias in AI systems can lead to undesirable outcomes, such as reputational damage, increased operating costs, service breakdown and financial loss. Thus, to curb AI biases in finance, it is significant to establish controls at the design stage and on an ongoing basis, proportionate to the scale and complexity of the system employed, and the activities carried out. This involves ensuring diversity within design and oversight teams to ease inherent societal biases.

It is also vital not to bring together fairness with bias. It should rely on the intended purpose of the AI system. So, firms must focus on establishing a precise understanding of fairness on the basis of the objectives the system has been set, and continue to monitor outcomes against this standard.

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