The responsible use of AI-based credit scoring requires ongoing efforts from both industry and regulatory bodies. Despite its promise, its implementation comes with notable caveats.
In recent times, a transformative wave has swept through the credit landscape in emerging markets, fundamentally reshaping how credit scores are calculated. Historically, credit scoring in these regions leaned heavily on factors like employment history and income, often leaving out the underbanked and those without formal employment due to strict criteria. However, new innovations are on the horizon, poised to revolutionise financial freedom in these areas.
The integration of Artificial Intelligence (AI) and Machine Learning (ML) in credit scoring has been ongoing but the process of aligning these advancements with a nation's developmental goals is just beginning. Across the board, banks and startups are delving into these technologies, intuitively grasping their capacity to amplify financial inclusion and propel economic expansion. Leading this innovation are alternative data sources, AI, and ML algorithms, along with agile cloud platforms. These technologies are being used to create credit scores for underbanked communities, allowing banks to offer improved financial solutions and expand access to individuals and communities that have traditionally been excluded from formal financial institutions. While AI holds considerable promise, its implementation is accompanied by notable caveats.
Across the board, banks and startups are delving into these technologies, intuitively grasping their capacity to amplify financial inclusion and propel economic expansion.
In emerging economies like India, data emerges as a potent force reshaping the foundations of credit scoring. Traditional systems, steeped in stringent norms, inadvertently exclude vast segments of the underbanked populace, lacking formal credit histories and access to essential banking infrastructure. However, a tide of change is underway, propelled by significant infrastructural developments, particularly the widespread proliferation of internet connectivity and smartphone usage.
The confluence of these advancements with the swift evolution of AI and alternative data sources ushers in a transformative wave. Embracing non-traditional data metrics like utility payments and network usage patterns, financial institutions could develop more inclusive credit scoring models. AI-based credit scoring represents a modern approach to evaluating an individual's creditworthiness. Unlike traditional methods that rely on factors like credit history and income, AI-based scoring incorporates a wider array of data sources, including digital footprints. It enables the evaluation of individuals without traditional credit histories by examining alternative data sources such as online transactions, social media interactions, browsing habits, or mobile app usage.
Embracing non-traditional data metrics like utility payments and network usage patterns, financial institutions could develop more inclusive credit scoring models.
In India, where a significant segment of the population lacks established credit histories, the implementation of these models could catalyse financial inclusion. Through the thoughtful use of AI and alternative data, financial institutions can empower individuals and businesses, creating avenues for loans, education, and enduring financial security. This shift has the potential to not only foster sustainable progress but also to nurture a more equitable economic landscape for all. However, as with the integration of AI in any sector, there are numerous concerns that must be addressed.
Essentially, AI can utilise alternative and unstructured data to generate a credit score for individuals. Nonetheless, associated with this capability are certain inherent problems. Certain problems are readily apparent, while others necessitate deeper contemplation, and
policymakers and key stakeholders need to be vigilant of this. Central bankers are very aware that relying solely on AI and alternative data sources may appear to be an overly simplistic solution, disregarding various peripheral factors that also demand attention. From the outset, RBI Governor Shaktikanta Das has emphasised the importance of caution among banks, NBFCs, and fintech firms in relying on pre-set algorithms for credit assessment. He stresses that these algorithms must be robust, regularly tested, and periodically re-evaluated to maintain their effectiveness. The RBI is encouraging financial institutions to recalibrate their algorithms, which utilise AI and increasingly rely on ML tools. This recalibration is crucial to adapt to the changing dynamics of the financial ecosystem and to incorporate new insights about sectors, consumer segments, and global trends. Governor Das highlighted the need to avoid excessive reliance on these algorithms and to ensure their accuracy and relevance in the evolving financial landscape.
Efforts should be concerted to align new and emerging technologies with the world's most pressing challenges. While there is hope and promise in this endeavour, it's crucial to maintain an awareness of the broader context and potential risks. Understanding why such models need to undergo regular testing, as well as why AI should be integrated with caution and vigilance, is essential. For instance, AI enables the evaluation of individuals without traditional credit histories by analysing alternative data sources such as online transactions, social media interactions, browsing habits, or mobile app usage. However, there is a question of whether AI is making accurate choices in this assessment. This raises the question of whether a human element should be introduced to ensure accuracy and assess subjective situations. In AI and ML, a “black box” refers to algorithms that are difficult to interpret. This opacity raises concerns about accountability, fairness, and bias. For instance, if an AI system denies someone a loan or a job, understanding the decision-making process becomes crucial, yet this can be challenging with a black box model.
AI enables the evaluation of individuals without traditional credit histories by analysing alternative data sources such as online transactions, social media interactions, browsing habits, or mobile app usage.
Data privacy emerges as a crucial concern in this context. Alternate data, encompassing all non-traditional financial data points, prompts a fundamental question: why should citizens be compelled to disclose their private information? As countries worldwide implement stringent data protection guidelines for their citizens, the use of personal data for advancing financial inclusion raises ethical quandaries. It poses a broader philosophical question: should the pursuit of development and enhanced public services supersede the sanctity of data privacy?
It would be imprudent to miss the forest for the trees. Attention needs to be directed towards the benefits, foremost among them being the manifold advantages of financial inclusion. There also exists the potential for the development of models that focus on payment behaviour, particularly regarding utility bills, loans, and credit lines. These models, when implemented, often provide delayed indicators of a potential default event as it becomes more apparent. There are several conceivable innovations. Perhaps, it could be taken a step further by integrating blockchain technology into the mix. Such a move might enhance transparency within the system.
The future of AI-based credit scoring appears marked by continuous advancements and increased adoption throughout the financial sector. As ML algorithms grow more sophisticated and robust, and alternative data sources become more readily accessible, AI-based credit scoring models are anticipated to become even more accurate and comprehensive in evaluating credit risk. There is some evidence for digital footprints to enhance access to credit for individuals who currently lack access to formal financial services. This could lead to greater financial inclusion and reduced inequality.
As ML algorithms grow more sophisticated and robust, and alternative data sources become more readily accessible, AI-based credit scoring models are anticipated to become even more accurate and comprehensive in evaluating credit risk.
A significant driving force behind the continued development and adoption of these models will be their potential to create a more inclusive credit evaluation system, one that takes into account individuals lacking a traditional credit history. Nonetheless, challenges will arise that necessitate attention. Issues concerning transparency, bias, and data privacy and security will remain paramount as these models become more widespread. Responsible and ethical use of AI-based credit scoring will require ongoing efforts from both industry and regulatory bodies. Likewise, advancements in related technologies, such as blockchain, may offer opportunities to enhance the transparency and security of AI-based credit scoring, albeit with their own set of complexities.
Sauradeep Bag is an Associate Fellow at the Observer Research Foundation
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Sauradeep Bag is Associate Fellow at ORF. Sauradeep has worked in several roles in the startup ecosystem and in international development with the United Nations Capital ...
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