Over the past couple of years, the global fintech industry has witnessed a wave of revolutions stemming from the increased leverage of artificial intelligence & machine learning, particularly in its aim of increasing inclusivity for women.
However, as history has taught us, without careful planning & oversight, even the most advanced of technologies can fall into the trap of the same preconceived biases, which hinders equitable financial access for consumers from every walk of life.
Thus, in today’s blog post, we will take a closer look at how we can intently take steps towards ensuring that AI based finance works for women & consumers from every walk of life.
Without further ado, let’s get started.
Table of Contents
- Understanding the Bias
- Labelling the Bias
- Double-Checking
- Constantly Adapting
- On-Ground Application
- Conclusion
- References
Understanding the Bias
One of the first & most important aspects we need to understand is the bias that exists & the predominant factors where it has stemmed from.
At its essence, technology of any format is de facto neutral; however, the humans who are sharing key decision verticals with the technology are not.
For instance, artificial intelligence-based recruitment software is, by design, free of any biases; however, most recruiters are of the opinion that an employment gap stretching more than 6 months has a severe negative impact on the overall potential of the employee, & thus the same is programmed within the system such that employees with gaps can be automatically removed.
Similar to the above example, in the world of finance as well, several key stakeholders harbour predetermined biases about women borrowers, with one of the most affluent of them being they cannot ensure stability in income.
Since women are also perceived to be the primary caregivers in a family, it is assumed right from the get go that they will need to terminate their employment when the needs of the family arise, thus failing to have a stable income. To ingrain this ideology in the lending value chain, often key stakeholders require women to co-apply with a male borrower (either their husband or father) such that they can be approved for the loan.
Along with this, there have been several instances throughout history where stakeholders have not invested enough time to properly evaluate the biases driving their decisions & left them on essential autopilot, rendering the entire lending value chain biased.
However, although the present situation might look bad on the surface, there are gladly significant interventions you can orchestrate in your individual capacity to ensure that these biases are properly mitigated. Some of them are shared below.
Labelling the Bias
One of the first & most important steps several technology developers are initiating is labelling the bias right at the pre-processing stage.
Here, instead of developing the existing software architecture, stakeholders are leveraging advanced artificial intelligence & machine learning algorithms to re-assess the data, identify & label the existing biases & in certain situations, make use of a gender lens to ensure systematic biases towards women can be effectively mitigated.
Taking all these factors into careful consideration, a final model is intentionally crafted such that preconceived biases can be effectively thwarted.
Double-Checking
Crafting an intentional model which is free of biases is only the first step of the process & thus, several stakeholders are taking it one step further & leveraging data scientists & developers to audit every algorithm before transferring them to development such that even the minutest ones can be effectively removed from the system & borrowers can be insured from their effects.
Constantly Adapting
As a last stage, developers have established a routine to continuously evaluate their past developments, leverage the learnings acquired from them & consistently update their models such that both existing & future biases can be dealt with.
On-Ground Application
While it is easy to discuss the impact of this technology & its subsequent development model on paper, it is important to acknowledge the several practical steps lenders around the world are taking to ensure AI based finance actually works for women.
- Meeting Personalized Goals – Instead of selling standard financial products, few lending institutions are going the extra mile to actually understand the exact goals of their women consumers & then design technological interventions to support reaching that goal.
- Creating Personas – While it might appear that goals of every borrower are unique, in reality, when orchestrated at scale, it can be easily understood that consumers can be best served when they are effectively categorized into different & carefully crafted personas. Thus, as a next step of the process, these financial institutions develop women specific personas & further leverage technology to both continuously refine & scale them.
- Credit Appraisal Models – As a third step, instead of relying on standard credit scoring & underwriting models, these financial institutions develop custom credit appraisal models which take into account a variety of different factors to estimate the actual creditworthiness of the borrower over & above standard credit scores supplied by credit rating agencies.
- Managing Biases – In an effort to continuously ensure that their credit models are free of biases, financial institutions continually revisit sources of data which cater to underserved borrowers, followed by careful analysis of previously rejected borrowers to ensure proper identification of biases & finally removing variables which might potentially give rise to biases in the future.
- Product Flexibility – Last but not least, institutions are making it a priority to offer product flexibility at the heart of all their offerings. By offering solutions which are context-specific to the borrower’s current situation & further offering interventions such as flexible repayment & savings based goal achievement models, financial institutions are going the extra mile to effectively serve the underserved market.
Conclusion
Identifying & removing biases from a credit assessment model which has been designed to work for the masses is no easy feat, however with intentional design & careful consideration, even the most difficult of challenges can be seamlessly overcome & such is the task of ensuring AI based finance works for women.
Thank you for reading & I will see you in the next one.