Home Slider The Role of AI in Shaping Credit Scoring in Emerging Markets

The Role of AI in Shaping Credit Scoring in Emerging Markets

by internationalbanker

By Rajat Dayal, Co-founder and CEO, Yabx





In recent years, a transformative wave has been sweeping the financial industry, reshaping the credit landscape in emerging markets. Credit scoring in these regions has historically relied heavily on factors such as employment history and income, with the underbanked and those without formal employment finding themselves excluded due to rigid criteria. New innovations are set to be the turning point for financial freedom in these regions.

At the forefront of this innovation drive are alternative data, artificial intelligence/machine learning (AI/ML) algorithms and agile cloud platforms employed to build credit scores for underbanked communities. Utilising new technologies allows banks to offer better financial solutions and increase access to individuals and communities traditionally excluded from formal financial institutions.

The challenge of financial inclusion

Whilst the conventional credit-scoring system proves to be effective throughout many developed markets, it often leaves behind vast segments of the global population. Most of the adult populace in emerging markets rarely have access to financial institutions as they cannot produce evidence of their ability to repay a loan.

This leads to the underbanked, who may lack formal credit histories, required documents, access to traditional banking infrastructure and sometimes smartphones, facing significant hurdles in securing essential financial services. Underbanked individuals and businesses in emerging markets face challenges that often extend beyond financial limitations. Reduced access to education, healthcare and employment opportunities can compound the intricacies of credit assessments. These individuals also often grapple with the absence of formal documentation, hindering their ability to prove financial stability and perpetuating the cycle of economic disparity.

In response to these challenges, technology can have transformative impacts by exploiting alternative scoring models, enabling the provision of life-changing financial solutions to communities worldwide. Individuals are empowered to pursue diverse objectives, such as furthering their educations, breaking the high entry barriers to smartphone ownership and securing surplus cash during business emergencies. Financial solutions contribute to breaking the cycle of economic disparity, ultimately fostering sustainable development and inclusive economic growth on a global scale.

The power of alternative data sources

By utilising alternative data sources and leveraging non-traditional information to build comprehensive customer profiles, banks can expand the pool of eligible borrowers in underbanked areas. Yabx’s approach has seen particular success in Africa, with its work in Uganda leading to an increase in the eligible customer base from 100,000 to an impressive four million, with more than six million loans disbursed in just 12 months.

This massive boost was made possible through a non-traditional approach to data sources. Unlike traditional credit scores that rely heavily on credit histories, Yabx’s approach incorporates a range of data points, such as utility payments, network-usage patterns, wallet behaviours, etc. This holistic assessment provides a more accurate and inclusive representation of an individual’s creditworthiness, opening previously closed doors.

Organisations can also generate predictive views of consumers’ financial habits—for instance, by monitoring their responses to missed calls to paint a picture of their reliability or past usage of mobile wallets to predict future inactivity. In many cases, a credit bureau is also integrated, providing additional data that contributes to credit decisions. Moreover, a borrower’s good behaviour related to repayments creates further perks, such as gaining an increased credit limit. The widespread information gleaned from active borrowers delivers additional data that can be used to enhance scoring models further.

The artificial intelligence age

Recent years have seen AI-driven recommendation engines move to the forefront of the financial landscape, playing a pivotal role across the sector. At Yabx, we utilise machine learning, scoring, risk decisioning and policy engines, building thousands of datasets to help us better understand the needs of those in underbanked communities. These engines leverage customer-behaviour patterns to provide tailored suggestions for financial products and services, focusing on loan products that align with the unique needs of each consumer. This bespoke process significantly increases the likelihood of successful loan applications, offering a more personalised and user-friendly experience.

Additionally, the integration of AI has transformed the way financial institutions approach customer segmentation and personalisation. This level of personalisation is particularly advantageous for microbusiness owners, who may have been historically underserved. The reduction of traditional financial bureaucracy makes borrowing more accessible, fostering an environment in which entrepreneurs can access needed capital without navigating layers of administrative hurdles.

Artificial intelligence and machine learning in scoring

Machine learning, a subset of artificial intelligence, plays an essential role in the credit-risk assessment process. By continuously learning from vast datasets, machine-learning algorithms can identify patterns and trends that traditional scoring models may miss. This adaptive approach not only enhances the accuracy of credit assessments but also allows for real-time adjustments, ensuring that the scoring system remains dynamic and responsive to evolving economic conditions.

Additionally, artificial intelligence can bolster an organisation’s personalisation capabilities. By moving beyond the one-size-fits-all approach of traditional credit scoring and tailoring assessments based on individual behaviours and financial patterns, organisations can improve the accuracy of their credit decisions and enhance the overall user experience, empowering future borrowers.

The future of risk evaluation in emerging markets

The foremost way to determine the success of inclusive finance in the lending space is to examine the performances of its portfolios. The unique selling point of fintechs (financial-technology firms), such as Yabx, is how they manage risks in risky markets—the role of alternative scoring in risk evaluation and how the “minds” of such platforms are trained to segregate good borrowers from bad accurately. Credit-limit and collection models are personalised in a way that not only promotes repayment but also guarantees it. Traditional credit-scoring models, often rooted in legacy systems and financial bureaucracy, can struggle to adapt to these markets’ unique challenges and dynamics. When looking to the future of risk evaluation and scoring, banks should ensure they are employing alternative methods to bridge the financial-inclusion gap.

The future of risk evaluation in emerging markets is undoubtedly intertwined with technological advancements and innovative approaches to data analysis. Yabx’s journey provides valuable lessons for the financial industry, highlighting the need to embrace change and explore new avenues for assessing creditworthiness.

Collaborations and partnerships are keys to success.

Collaborations and partnerships within the financial sector are pivotal in advancing the innovative landscape of AI-driven credit scoring. In this dynamic ecosystem, the fusion of expertise from fintech firms, traditional financial institutions and customer-merchant networks works together to extend beyond conventional boundaries. By fostering partnerships, these parties can collectively address the unique challenges underbanked communities face in emerging markets. Collaboration cultivates a shared understanding of diverse consumer needs and facilitates the development of holistic solutions.

Another benefit is that partnerships enable efficient data exchange, ensuring more comprehensive assessments of creditworthiness. Through collaborative efforts, the financial sector can harness the collective power of varied perspectives and resources, propelling the evolution of inclusive credit-scoring practices that transcend individual organisation’s capacities.

Where these cooperative frameworks are possible, the financial industry stands poised to deliver transformative impacts, empowering communities and contributing to a more accessible and equitable global financial landscape.

Shaping an inclusive financial future

Overall, the advancements in scoring are transforming the financial landscape for underbanked communities in emerging markets. As we navigate the evolving credit-assessment landscape, the financial sector cannot overlook the value of artificial intelligence in credit scoring. The journey towards financial inclusion is an ongoing one. With a strong community of financial pioneers leading the way, the prospect of a more inclusive, accessible and responsible global financial ecosystem is becoming increasingly promising.


Related Articles

Leave a Comment

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.