Credit Without Borders: AI and Financial Inclusion in Bangladesh

Credit Without Borders: Harnessing AI to
Empower Bangladesh's Unbanked Populations
Overview:
In Bangladesh, a significant portion of the population remains outside the formal banking
system, with limited access to essential financial services like credit. This gap not only
hinders individual economic advancement but also stymies the broader economic
development of the country. Traditional banking infrastructures often fail to reach these
unbanked individuals due to various barriers including geographical constraints, lack of
formal financial records, and socioeconomic factors. As a result, a substantial number of
people are deprived of opportunities for personal and business growth that credit access
could facilitate.
Enter artificial intelligence (AI), a technology with the power to revolutionize how credit
scoring is approached for populations without formal banking histories. AI can analyze
alternative data sources such as mobile phone usage, utility bill payments, and other
digital footprints to assess creditworthiness in ways that traditional banking systems
cannot. This innovative approach is not only more inclusive but also offers a potentially
more accurate and less biased assessment of an individual's ability to manage credit.
The integration of AI into financial services holds the promise of democratizing credit
access by developing models that inclusively cater to the unbanked. This could pave the
way for a financial revolution in Bangladesh, where the benefits of economic growth and
stability are more equitably distributed across all strata of society. Thus, the potential of
AI to transform the landscape of financial inclusion is not just innovative but could be
profoundly transformative, offering new pathways to economic empowerment for the
unbanked population.

Understanding the Unbanked in Bangladesh
In Bangladesh, the unbanked population represents a significant segment of the society that has
minimal or no access to formal banking services. This exclusion from the traditional financial
system impacts millions, influencing their economic opportunities and broader societal growth.
Understanding who these unbanked individuals are and the factors contributing to their exclusion
is crucial for addressing these disparities.
Demographics of the Unbanked
The demographics of the unbanked in Bangladesh are diverse, encompassing rural farmers,
small business owners, women, and low-income families. Many of these individuals lack the
necessary documentation such as proof of identity or address, which are typically required to
open bank accounts. Others live in remote areas where banks do not have branches or ATMs,
rendering access to financial services impractical.
Socio-Economic and Cultural Factors
Several socio-economic and cultural factors play a critical role in the banking exclusion of certain
populations. For instance, there is a significant literacy gap in rural areas, which complicates
understanding of banking procedures and the perceived benefits of having a bank account.
Additionally, cultural norms, particularly those affecting women, can restrict mobility and
discourage or forbid them from interacting with predominantly male banking staff. Economic
barriers, such as minimum balance requirements and maintenance fees, also prevent low-income
individuals from accessing banking services.
Logistical Factors
Logistical challenges further exacerbate the issue. The physical inaccessibility of banking facilities
in rural or underdeveloped areas discourages regular interaction with banks. Moreover, the lack
of reliable internet access in many parts of the country limits the potential reach of digital banking
solutions that could otherwise bridge the gap between the unbanked and financial institutions.
Impact of Being Unbanked
Being unbanked has profound implications for individuals and communities. Without access to
credit, savings accounts, or insurance, unbanked individuals have a harder time investing in
education, starting and growing businesses, and securing their financial future against
unexpected economic shocks. This lack of financial inclusion stunts personal economic growth
and contributes to the persistent cycle of poverty.
Understanding these factors is the first step toward crafting effective interventions that can bring
more of Bangladesh’s unbanked population into the fold of financial services, leveraging tools like
AI to tailor solutions that meet their unique needs and circumstances.

Traditional Credit Scoring Methods
Traditional credit scoring methods have long served as the backbone of financial decision-making,
assessing the creditworthiness of individuals based on a range of financial data. However, these
systems often fail to accommodate those without a formal banking history, such as the unbanked
populations in Bangladesh and globally.
Overview of Traditional Credit Scoring Systems
Traditional credit scoring in Bangladesh, as in most parts of the world, relies heavily on historical
financial data such as loan repayment histories, credit card usage, and existing account
performances. Globally, these scores are often calculated by credit bureaus that consolidate
financial behavior into a numerical score, influencing an individual's ability to borrow money and
at what interest rates.
Limitations of Traditional Systems
The primary limitation of traditional credit scoring is its dependency on formal financial records,
which excludes a significant portion of the population that either does not use banks or lacks a
sufficient financial history. In Bangladesh, where many people operate in a cash-based economy
and rarely use formal banking products, traditional credit scores are not just inadequate but often
irrelevant.
● Data Availability: In regions with significant unbanked populations, there is a stark
absence of the data needed for traditional scoring models.
● Exclusion from Credit: Without a credit history, individuals are often either denied access
to loans or charged prohibitively high interest rates.
● Bias and Inequality: These systems can inadvertently favor certain demographics —
typically urban over rural, and middle and upper classes over lower-income groups.
Comparison with Other Countries
In contrast to Bangladesh, countries like the United States and the United Kingdom have more
developed credit reporting systems with broader data integration, including rent payments, utility
bills, and even subscriptions, providing a more comprehensive view of a person's financial
behavior. Emerging markets such as Kenya have innovated with mobile money transaction data
to score credit, vastly increasing financial inclusion.
In India, similar challenges are addressed by incorporating alternative data into credit scores,
such as telecommunications and utility bill payments, which has helped to slightly widen the credit
net. However, these methods are still in their infancy and are not universally applied, indicating a
global gap in effectively serving unbanked or underbanked populations.
Traditional credit scoring methods are evidently not sufficient to meet the needs of all population
segments, particularly the financially invisible groups in Bangladesh. The comparison with credit

systems in other countries highlights significant gaps and inefficiencies that could be mitigated by
adopting more inclusive data practices and leveraging emerging technologies like AI to fill these
gaps. This transition is essential not only for enhancing financial inclusion but also for driving
equitable economic growth.
Introduction to AI in Credit Scoring
The advent of artificial intelligence (AI) and machine learning (ML) is transforming numerous
industries, including finance. In the context of credit scoring, these technologies offer revolutionary
approaches that promise to expand financial inclusion significantly.
Basics of AI and Machine Learning
AI involves creating algorithms and systems that can perform tasks which typically require human
intelligence. These tasks include decision-making, problem-solving, and pattern recognition.
Machine learning, a subset of AI, focuses on developing algorithms that allow computers to learn
and adapt through experience. ML models improve their accuracy over time by processing large
sets of data and identifying patterns that would be invisible or inaccessible to human analysts.
Application in Credit Scoring
AI and ML are particularly well-suited to enhancing credit scoring systems:
● Accuracy: AI models can process complex datasets and discern nuances in financial
behavior that traditional scoring methods might overlook. This capability allows for a more
precise assessment of an individual’s creditworthiness.
● Scalability: AI systems can quickly analyze data from millions of users simultaneously,
making it feasible to assess large populations efficiently, which is crucial in densely
populated countries like Bangladesh.
● Unbiased Assessment: While traditional credit scoring can perpetuate biases present in
historical data, AI systems can be designed to minimize these biases, providing a fairer
evaluation of creditworthiness across different demographics.
Global Examples of AI in Credit Scoring
● United States: Companies like Zest AI offer tools that use thousands of data points to
determine creditworthiness more accurately than traditional models. This approach not
only predicts risk but also explains the data influencing decisions, increasing transparency.
● China: Ant Financial’s Zhima Credit utilizes AI to score people based on both traditional
and non-traditional data, including online purchase behavior and social networking habits.
This method has opened credit markets to millions who previously had no access to
banking services.

● Africa: In Kenya, startups like Tala are using AI to analyze mobile phone data to provide
credit scores for users, many of whom do not have a formal credit history. This model has
enabled broader access to microloans for small business owners and individuals.
These examples underscore the transformative potential of AI in redefining the landscape of credit
scoring. By leveraging AI, financial institutions can not only reach a broader audience but also
make more informed lending decisions that can drive economic growth and enhance societal well-
being. This section emphasizes the global shift towards AI in finance, illustrating its profound
impact on traditional systems and the new possibilities it creates for financial inclusivity.
Innovative AI Approaches in Bangladesh
As Bangladesh seeks to enhance financial inclusion for its unbanked population, adopting
innovative AI models and approaches can be a game-changer. These technologies are especially
crucial in contexts where traditional data points used for credit scoring are sparse or non-existent.
Adapting AI Models for Sparse Data
AI's versatility allows it to operate effectively even with limited data, making it ideal for
environments like Bangladesh where many individuals lack extensive formal financial records.
Machine learning models, such as decision trees, neural networks, and ensemble methods, can
extrapolate insights from non-traditional data sources such as mobile phone usage, utility bill
payments, and even social media activity.
● Predictive Analytics: By analyzing patterns in mobile phone data, such as call frequency
and top-up amounts, AI can predict an individual's financial stability and propensity to
repay loans.
● Natural Language Processing (NLP): AI can process and analyze text from digital
transactions and social media to gauge consumer behavior and preferences, offering
deeper insights into a person's financial habits.
Case Studies from Other Developing Countries
● India: Startups like CreditVidya use AI to harness alternative data for credit scoring,
helping lenders assess the creditworthiness of millions of individuals who were previously
invisible to traditional credit systems.
● Africa: Companies like JUMO and Aella Credit use AI to analyze mobile wallet
transactions and other digital footprints to offer credit and insurance products tailored to
the needs of sub-Saharan Africa’s unbanked populations.
These examples demonstrate that AI can be successfully implemented in similar markets with
challenges akin to those faced by Bangladesh.

Potential Partnerships and Roles of Stakeholders
To effectively implement AI in credit scoring, a collaborative approach involving various
stakeholders is essential:
● NGOs: Non-governmental organizations can facilitate data collection and user education,
helping unbanked individuals understand the benefits of participating in AI-driven financial
services.
● Tech Startups: Local and international tech companies can develop and tailor AI solutions
to fit the specific needs of the Bangladeshi market. These firms can also ensure that
solutions are scalable and secure.
● Government: The government plays a critical role in creating a supportive regulatory
framework that encourages the ethical use of AI in financial services. Moreover, public
agencies can partner with tech companies to ensure that innovations reach a broad
audience.
For Bangladesh, the integration of AI into credit scoring systems presents an opportunity to
drastically improve financial inclusion rates. By learning from global successes and fostering a
cooperative ecosystem involving tech innovators, NGOs, and government bodies, Bangladesh
can pave the way for a more financially inclusive future. This approach not only benefits those
currently outside the formal banking system but also stimulates broader economic growth and
stability.
Challenges and Ethical Considerations
Implementing AI in credit scoring, while promising, introduces several challenges and ethical
considerations that must be addressed to ensure its success and sustainability in Bangladesh.
These issues revolve around data privacy, the ethics of automated decisions, regulatory
compliance, bias mitigation, and maintaining transparency.
Data Privacy Concerns
One of the most pressing challenges is protecting the privacy of individuals whose data is being
used. In Bangladesh, where data protection regulations may still be developing, ensuring that
personal information is handled securely and with consent is crucial. This involves:
● Establishing robust data protection laws that align with international standards.
● Ensuring that data collected for credit scoring is encrypted and securely stored.
● Implementing strict access controls and audit trails to prevent unauthorized data access.
Ethical Implications of Automated Decisions
AI systems make decisions that can significantly impact individuals' financial lives. Ethical
considerations must include:

● Ensuring that AI decisions do not result in unfair exclusions or discriminatory practices.
● Providing mechanisms for recourse and appeal for individuals negatively affected by AI
decisions.
● Designing AI systems that are explainable, so users and regulators can understand how
decisions are made.
Regulatory Hurdles
The regulatory environment for AI and financial services in Bangladesh may need updates to
accommodate new technologies:
● Regulators must understand AI technology to create effective guidelines and standards.
● There must be clear regulations governing the use of alternative data in credit scoring.
● Ongoing dialogue between AI developers, users, and regulatory bodies is essential to
ensure that AI solutions are both innovative and compliant.
Mitigating Biases in AI Models
AI models can inadvertently perpetuate or amplify existing biases if not carefully designed:
● Training AI models on diverse data sets that represent all segments of the population
fairly.
● Continuously monitoring and updating AI algorithms to identify and correct biases.
● Employing techniques like fairness-aware modeling to actively reduce bias in AI decisions.
Importance of Maintaining Transparency
Transparency is critical in building trust and accountability in AI systems:
● Developing clear explanations of how AI models function and how they make decisions.
● Making these explanations accessible to all stakeholders, including customers, regulators,
and partners.
● Engaging independent audits of AI systems to verify their integrity and fairness.
While AI offers transformative potential for credit scoring in Bangladesh, navigating the ethical
and regulatory landscape is crucial. Addressing these challenges through comprehensive
policies, continuous oversight, and stakeholder engagement will be key to harnessing AI's
capabilities responsibly and effectively. By prioritizing ethical considerations and transparency,
Bangladesh can set a benchmark for innovation in financial services that not only enhances
economic inclusion but also safeguards individual rights and promotes trust.

The Road Ahead
The future of AI in enhancing financial inclusion in Bangladesh is bright, with numerous
opportunities for growth and innovation. As AI continues to evolve, its integration into financial
services can significantly transform the landscape of banking and credit accessibility. Here's a
look at the prospective developments, ongoing projects, and the synergistic efforts of the
government and private sectors.
Future Prospects of AI in Financial Inclusion
AI's potential to transform financial services in Bangladesh is vast. By utilizing AI, financial
institutions can provide more personalized, efficient, and accessible services. This includes:
● Automated credit scoring systems that can reach a larger population.
● Personalized banking advice and financial management tools for low-income households.
● Enhanced fraud detection systems that protect both the consumer and the provider.
Ongoing Projects and Research
Several initiatives are already underway, showcasing the commitment to leveraging AI for
financial inclusion:
● Pilot projects utilizing AI to provide microloans based on non-traditional data.
● Research collaborations between universities and tech companies to explore new
applications of AI in predicting financial behavior.
● Development of AI tools that can interpret local languages and dialects to increase
accessibility for all Bangladeshi citizens.
Initiatives by Government and Private Sector
The government and private sectors are increasingly recognizing the importance of AI in financial
services:
● The government's digital Bangladesh initiative includes provisions for the development
and integration of AI technologies in various sectors including finance.
● Private sector investments are fueling the growth of fintech startups that specialize in AI-
driven financial services, aiming to bridge the gap between traditional banking systems
and the unbanked.
Moving Forward
To capitalize on AI's potential to revolutionize financial inclusion, concerted efforts from all
stakeholders are essential. The call to action includes:

● Encouraging further investment in AI technologies to develop solutions tailored to the
needs of the unbanked.
● Promoting partnerships between fintech companies, traditional banks, and non-
governmental organizations to facilitate knowledge sharing and innovation.
● Supporting policy frameworks that foster an environment conducive to the ethical use of
AI in financial services.
Investing in AI-driven solutions offers a path toward more inclusive, equitable financial services in
Bangladesh. As stakeholders from various sectors come together to push the boundaries of what
AI can achieve, the focus must remain on creating a financially inclusive ecosystem that benefits
every citizen. By embracing these technologies, Bangladesh can ensure a future where financial
services are accessible to all, thereby driving national growth and development.
How AI is Transforming Credit Scoring and Empowering the Financially
Excluded
In the expansive world of financial services, one of the most significant barriers to economic
growth and personal financial empowerment is access to credit. Traditional credit scoring
systems, while useful, often exclude a large portion of the global population. These systems
typically require a substantial history of borrowing behavior, leaving those new to banking or with
minimal credit history at a stark disadvantage. This is where artificial intelligence (AI) comes into
play, revolutionizing the way financial institutions assess creditworthiness.
The Rise of AI-Based Credit Scoring
AI-based credit scoring is emerging as a formidable and relevant solution that extends beyond
traditional methodologies. Unlike conventional credit scoring that relies heavily on past financial
behavior and static variables, AI introduces a dynamic approach by incorporating a multitude of
real-time data points.
Data Collection and Analysis: AI systems don't just rely on credit history. They can analyze
diverse data sources such as bank transactions, bill payments, and even patterns in mobile phone
usage. This extensive data collection facilitates a more holistic view of an individual's financial
behavior.
Machine Learning at the Core: The essence of AI-based credit scoring lies in machine learning
models. These models are adept at identifying patterns and correlations in large datasets, making
it possible to assess a borrower's likelihood of repaying a loan with greater accuracy.
Predictive Analytics: Once trained, these models can perform predictive analytics on new credit
applications, generating scores that reflect an applicant’s creditworthiness more
comprehensively.
Adaptability and Continuous Learning: AI models continually learn and improve from new data.
This feature ensures that AI credit scoring remains relevant, adapting to changing financial
behaviors and market conditions.

Addressing Challenges: Transparency and Bias Mitigation
Despite its advantages, AI credit scoring is not without challenges. The complexity of machine
learning models can create a "black box" scenario where decision-making processes are not
transparent. To address this, new tools and techniques have been developed to enhance the
interpretability of AI decisions.
Visual Tools for Interpretation: Visualizations such as the SHAP (SHapley Additive
exPlanations) value plot help illustrate how different features impact model predictions. These
tools make it easier for analysts to understand and explain the factors driving credit decisions.
Combining AI with Traditional Scoring: Some institutions are integrating AI with traditional
scoring methods, creating hybrid models that harness the predictive power of AI while maintaining
the clarity and familiarity of traditional scorecards. This approach helps balance innovation with
reliability.
The Broader Impact of AI on Financial Inclusion
AI’s ability to consider a broader range of data points allows it to generate credit scores for
individuals who previously may not have had access to credit. This capability is crucial for
promoting financial inclusion, particularly for underbanked populations who can now gain access
to financial products suited to their needs.
Moreover, the efficiency and adaptability of AI systems mean that financial institutions can
process applications faster and keep pace with evolving economic landscapes, ultimately
extending credit to more deserving borrowers.
The Future is Here
AI is setting the stage for a financial revolution where more individuals can access credit based
on a comprehensive and fair assessment of their creditworthiness. As AI technology continues to
evolve and become more accessible, its integration into credit scoring is expected to expand,
promising a future where financial services are more inclusive and equitable. This advancement
is not just about leveraging new technology—it's about opening doors to financial empowerment
and growth for millions around the world.
AI-based credit scoring is not just a technological advancement; it is a tool for social change,
enabling more people to participate in the financial system and improve their economic status. As
we look to the future, the continued refinement and adoption of AI in credit scoring will be key to
building more inclusive financial environments worldwide.

The Mindprint
The transformative potential of artificial intelligence (AI) in revolutionizing credit scoring systems
is poised to significantly enhance economic empowerment and growth in Bangladesh. By
integrating AI into financial services, we can bridge the gap between the traditional banking sector
and the unbanked populations, facilitating access to credit for millions who have previously been
marginalized. This shift not only promises to improve individual financial stability but also
catalyzes broader economic development by enabling more people to invest in businesses,
education, and personal growth.
AI's role extends beyond merely automating processes—it reshapes how financial inclusion is
perceived and implemented. As these technologies become more sophisticated, they open up
unprecedented opportunities for innovation in credit assessment, risk management, and customer
service, ensuring that financial products are tailored to the needs of all segments of society.
As we stand on the brink of this new era, it is imperative for stakeholders across sectors—
government, private industry, and civil society—to embrace and invest in AI-driven solutions. The
commitment to fostering an inclusive financial environment through technological innovation will
be key to building a resilient and thriving economy.
Let this be a call to action: Innovation is not just about creating new tools but about transforming
lives. By championing AI in financial services, Bangladesh can lead the way in achieving financial
inclusivity, setting a global benchmark for how technology can empower economic participation
for everyone.
References
Academic Papers:
● "Financial Inclusion and Poverty Alleviation: The Contribution of Digital Finance in
Bangladesh" by Mahmud, A. & Muyeed, A. (Journal of Economic Development, 2020).
● "The Impact of Artificial Intelligence on Financial Services in Emerging Markets" by Singh,
S. & Sharma, P. (Emerging Markets Review, 2019).
● "Using Machine Learning to Extend Credit to Informal Sector Participants" by Osei-Bonsu,
K. & Tan, Q. (Journal of Financial Innovation, 2021).
Books and Reports:
● "Banking on Equality: Women, Work and Employment in the Banking Sector in
Bangladesh" by Kabeer, N., & Huq, L. (Routledge, 2018).
● World Bank Report: "Global Findex Database 2021: Measuring Financial Inclusion and
the Fintech Revolution".
● "Artificial Intelligence in Financial Services" by EY Global Services (2022).

Online Resources and Databases:
● Bangladesh Bank - Reports on Financial Inclusion and Innovation.
● Access to Information (a2i) Programme data on digital financial services in Bangladesh.
● International Monetary Fund (IMF) Working Papers on AI and Credit Access.
Case Studies:
● "JUMO’s AI Model: Pioneering Credit Scoring for the Unbanked in Africa" – A case study
by Harvard Business Review.
● "CreditVidya: Leveraging Alternative Data for Credit Scoring in India" – Published by the
Centre for Financial Inclusion.
Relevant Legislation and Policy Documents:
● "Digital Bangladesh Vision 2021: National ICT Policy".
● "The Personal Data Protection Act of Bangladesh (Draft)".
● Bangladesh Telecommunication Regulatory Commission (BTRC) guidelines on data
usage and privacy.
Industry Reports and Articles:
● McKinsey Global Institute Report: "Artificial Intelligence and its Impact on Emerging
Economies".
● "AI and the Future of Banking" by Forbes.
● "How AI is Driving Financial Inclusion in Developing Countries" by TechCrunch.
Author: MM Ehsan Nizamee (Ehsan Tanim) — CEO, Finager Fintech
More insights: www.ehsantanim.finagerfintech.com
+8801909009009

Credit Without Borders: AI and Financial Inclusion in Bangladesh

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