Financial Inclusion

Aug 1, 2023

How To Empower Marginalised Communities: 3 Key Strategies To Expand Financial Inclusion

Expand financial inclusion with AI and ML algorithms to enhance alternative credit scoring, tailored financial education and digital financial services access.

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Empowering Marginalised Communities: 3 Key Strategies To Expand Financial Inclusion

With the rise of interconnectivity in today’s world, a powerful catalyst for social and economic progress has also risen: financial inclusion.

As mentioned in our previous blog, 2023 Expert Guide: The Importance of Financial Inclusion Strategies and Policies, financial inclusion refers to the provision of affordable and accessible financial services to both individuals and businesses regardless of financial status or geographic location. The emergence of financial inclusion has increased the need to go beyond basic access to financial services. The focus is on creating affordable and reliable financial products and services, enabling underserved populations to connect with formal financial systems. This will enable individuals to flourish and promote economic growth.

This is especially important for empowering those underserved, excluded or discriminated against - marginalised individuals or communities. Marginalised communities typically suffer from being denied involvement in mainstream or traditional economic, social and financial activities and therefore lack or are barred access to financial needs. They include the unbanked, gig economy workers, millennials, and individuals with thin credit files because they either did not need credit or, for instance, preferred debit over credit. Innovative strategies, including artificial intelligence (AI) and machine learning (ML), must be used to achieve financial inclusion for these communities. 

“With the world navigating through a post-COVID era, there is undoubtedly an increasing need to build and promote inclusive financial systems. This is evidently more so for individuals from marginalised communities, such as those from developing countries in Latin America and Asia-Pacific regions, who are exposed to more vulnerability and may not have the access, opportunity or tools to ensure their financial security and stability.” By Michele Tucci, MD Americas and Chief Strategy Officer of credolab

Latin America and Asia-Pacific: A Comparative Outlook

In Latin America (LATAM) and Asia-Pacific (APAC), financial inclusion has become a key topic, and various reasons contribute to the importance of promoting it. Despite low access to traditional services and the lack of a bank account, smartphone penetration is high, among other factors. This presents an increase in financial inclusion, such as through digital financial services, alternative credit scoring, microfinance, peer-to-peer (P2P) lending and financial education or literacy. 

In both LATAM and APAC, the number of unbanked people is higher. From an overview, the majority of the world’s most unbanked countries reside in these regions, especially in APAC, specifically Vietnam, Indonesia and the Philippines.

World's most unbanked countries in LATAM and APAC

In LATAM, many people are excluded from the financial ecosystem and considered unbanked due to the lack of established credit history required by traditional credit scoring models. The COVID-19 pandemic only highlighted and exacerbated the social gap in financial inclusion. 

Countries like Colombia, Brazil and Mexico have launched notable initiatives to increase financial inclusion to combat this increasing gap. For example, Colombia launched Ingreso Solidario, which promotes the use of digital financial services such as digital accounts, mobile wallets, e-wallets or mobile money. This was to counter COVID-19 restrictions and economic effects on Colombian households, and it is a non-conditional emergency transfer program assisting vulnerable households without any social program coverage. 

In APAC, about 1.4 billion people remain unbanked today, according to the World Bank, and about 70% of Southeast Asia (SEA) contributes to this number, with Vietnam, Indonesia, and the Philippines making up the majority and Thailand contributing to 63% of unbanked or underbanked adult populations. This is due to nearly 400 million adults in SEA having limited access to financial services and only 104 million being fully "banked." 

COVID-19 made a significant economic impact, especially on vulnerable and

disadvantaged groups like marginalised communities. To ensure financial inclusion perseveres, finding innovative ways to rebuild livelihoods and increase resilience among those most affected and vulnerable was imperative. To combat the impact, countries like Thailand, Indonesia and the Philippines launched notable initiatives to increase financial inclusion. For example, Thailand worked on two types of digital financial services, ensuring the continuity of the present electronic payments and paving the way for the potential use of Central Bank Digital Currency (CBDC) and cryptocurrencies.

It highlights how financial inclusion seeks to bridge the social and economic gap caused by various reasons, typically attributed to a lack of access to formal financial services and the need to close the financial gap in the LATAM and APAC regions. Despite this, the question remains: why is financial inclusion so important?

Importance of financial inclusion

Financial inclusion tackles key problems existing in the world today. It raises the issue of how certain barriers due to financially excluded people make it more challenging for them to access basic needs financially, such as access to credit and financial services or education.

Financial inclusion is important to foster as it helps in three key areas, which is to:

1. Reduce poverty 

Financial inclusion provides individuals with tools and increased opportunities to improve their economic status and well-being, such as accessing affordable financial services. This accessibility allows individuals to engage in better financial management, savings accumulation and income-generating investments, which helps them build assets, create sustainable livelihoods and eventually break free from an assumed never-ending cycle of poverty. 

Diving into the statistics, 42.7% of the world’s poor are from Asia, with 4.4% (the next-highest portion) in LATAM and the Caribbean

Statistics of the world’s poor

Due to extreme income inequality, poverty reduction is slowing in developing regions, which is a powerful threat to economic progress. Furthermore, the World Bank Group considers financial inclusion a key enabler in reducing (extreme) poverty and boosting shared prosperity. It, therefore, aims to eliminate extreme poverty by 2030 and raise the share of prosperity among the bottom 40% in each country.

Financial inclusion and its importance in reducing poverty

Marginalised individuals, including those living in rural areas or from low-income backgrounds, can benefit from financial inclusion by accessing the resources they need to improve their living conditions and secure a better future for themselves and their families.

2. Promote economic growth

Financial inclusion provides individuals with access to financial services enabling them to engage in capital mobility, credit access for any entrepreneurial activity and investment productivity. The ability to engage in these activities generates new businesses, jobs and innovation, increasing employment and revenue opportunities.

For example, one prominent marginalised community in Singapore is migrant workers (MWs), who have little immigration status protection. These MWs primarily hail from SEA countries with scarce and hard-to-qualify high-paying employment opportunities, like the Philippines, Vietnam, Thailand, and Indonesia. Approximately 1.4 million MWs live in Singapore, making it the largest hosting country. However, these MWs are one of the most vulnerable populations due to their lack of access to bank accounts and their vulnerability, especially economic instability, which has only been exacerbated by the pandemic. Their reduced access to banks and Singapore’s financial system is prevalently due to barriers such as:

  1. A lack of access to financial services such as remittances and secure savings and investment
  2. The inadequacy of financial services and systematically blocking MWs from participating in the normal financial system
  3. Being subjected to high loans or debt bondage by recruitment agencies and employers, or even abuse such as salary mispayment

With these barriers at play, promoting financial inclusion to indict change is imperative so that every individual can overcome challenges and economic hurdles. Some benefits of promoting financial inclusion to businesses and marginalised communities include offering a new economic potential and unlocking opportunities for individuals to harness their talents and capabilities. By putting a stop to a negative self-perpetuating cycle, financial inclusion drives individual and economic innovation, opportunity, productivity and competitiveness, contributing to an economy’s overall growth and development.

3. Increase financial services access

Financial inclusion provides access to a full range of financial services individuals need to thrive in today’s world and sustain their livelihoods: engage in bank account openings, secure money saving, access credit for education, protect against risks with insurance and leverage convenient but affordable digital payments. 

Based on data from LATAM, the pandemic contributed to an increase of 20% in bank account ownership for adults and increased digital payments, which boosted access to and usage of financial services. In 2023, according to the latest Mastercard study, The State of Financial Inclusion post-COVID-19 in Latin America and the Caribbean: New Opportunities for the Payments Ecosystem, 79% of Latin Americans currently have access to a basic financial product. However, 21% of Latin American adults still depend exclusively on cash to manage their financial lives and therefore remain outside the financial system or lack a bank account. Despite improvements to the LATAM post-pandemic, the region faces significant challenges, from tightening global financial conditions, lower global growth, persistent inflation, and increasing social tensions amid growing food and energy insecurity. 

The progress of financial inclusion has shown positive results, and it is only a matter of time and effort needed to overcome these challenges and continue increasing access to financial services. By promoting financial inclusion, marginalised individuals' well-being and overall quality of life can be improved when they are given the necessary tools to ensure future planning, mitigate any financial shocks and even have the opportunity to partake in the formal economy. Moreover, financial inclusion contributes to consumer protection and financial resilience by reducing reliance on informal and often predatory financial services, adding another layer of security and stability to marginalised communities. 

Therefore, expanding financial inclusion is important as it helps reduce poverty, promotes economic growth and increases access to financial services, especially for marginalised individuals and communities. By addressing barriers to financial inclusion, societies can create more inclusive and equitable economic systems that benefit individuals, businesses, and entire communities. 

Expanding financial inclusion 

In recent years, innovative strategies leveraging AI and ML have emerged as powerful tools to expand financial inclusion and empower marginalised communities. 

The importance of privacy and data protection

To build the foundation of these strategies, it must be highlighted that data-driven solutions play a crucial role in supporting them. It addresses the problem of financial inclusion by providing valuable insights and enhancing informed decision-making processes. Data can be leveraged in multiple ways, such as identifying underserved areas, understanding barriers and needs, assessing financial behaviour and preferences, using monitoring and evaluation frameworks, assessing risk and credit scoring, offering personalised financial recommendations and using predictive analytics. 

“It's important to note that data-driven solutions must prioritise privacy and data security. Safeguards should be in place to protect the confidentiality and security of individuals' data while ensuring compliance with relevant data protection regulations. By leveraging the power of data and analytics, stakeholders can gain deeper insights into the barriers, needs, and preferences of underserved populations. This knowledge can inform the development and implementation of effective financial inclusion strategies, leading to more targeted interventions and improved access to financial services for all.”
By Michele Tucci, MD Americas and Chief Strategy Officer of credolab

These data-driven solutions can be successfully implemented and welcomed by businesses and end-customers' trust and confidence. By doing so, strategies using data-driven solutions can reassure concerns about sharing personal and financial information, especially for marginalised individuals. Other important factors behind privacy and data protection include:

  • Ensure individuals retain informed consent and hold control over personal information: In financial inclusion initiatives, individuals should have the right to choose whether to participate or share their information based on their understanding of how their data will be used.
  • Mitigate risks of data breaches, identity theft, and unauthorised access to individuals' financial information: In financial inclusion, data-driven solutions often require sensitive personal and financial data collection and analysis. With proper and robust privacy practices, these risks can be mitigated, and individuals are protected from potential harm.
  • Reassure businesses and individuals with adherence to regulatory compliance:  These regulations ensure that individuals' privacy rights are respected, and data is handled securely. Regulatory compliance is essential to avoid legal consequences and demonstrates a commitment to ethical and responsible data practices.
  • Prevent the risk of perpetuating bias or discrimination during the decision-making process: Using AI and ML algorithms and models, data-driven solutions analyse and make decisions based on historical or alternative, which makes AI bias a cause of concern. However, providing individuals with privacy and data protection ensures financial inclusion initiatives do not exacerbate or reinforce existing inequalities. 
  • Build long-term sustainability and inclusivity by maintaining individuals' trust and ensuring the responsible use of their data: Investing in privacy and data protection can pave the way for long-term success in promoting financial inclusion. These measures include transparent data practices, clear data governance frameworks, and robust security measures to protect individuals' information over the data lifecycle.

Key innovative strategies in how AI and ML can help

This article segment will explore three key innovative strategies to help banks and financial and non-financial institutions expand financial inclusion, specifically for marginalised communities. We will also demonstrate how AI and ML can be leveraged to expand financial inclusion. 

1. Enhance alternative credit scoring

One of the primary challenges marginalised communities face is the lack of traditional credit histories, making it difficult to access financial services via traditional channels. To overcome this, it is imperative to develop credit scoring models using enhanced alternative data combined with AI and ML algorithms in three main ways.

Utilise advanced analytics techniques and ML algorithms to analyse alternative data and identify patterns and correlations that can help predict creditworthiness for individuals with limited or no traditional credit history.

Here are some ways AI and ML can help:

1. Utilise alternative data

Using AI and ML allows analysts to evaluate alternative data from different sources, integrate them and identify patterns related to the probability of anyone missing a payment. This provides a modern understanding of creditworthiness with a more in-depth view of a borrower’s credit profile and ensures a more holistic credit risk assessment. Using alternative data arises from the need to serve thin-file customers, forming the majority of under- and unbanked potential borrowers in emerging markets.

Alternative data refers to information on behaviours, habits, interests and transactions carried out by a person and obtained from non-traditional sources such as social networks, satellites, sensors, financial reports, geolocation, rental payment, and more. Data can also be categorised into different types: transactional data, telco data, psychometric data, and behavioural data, with the latter being a focus when using credolab.

For example, credolab’s risk solutions complement traditional data with behavioural data, insights and scores from one privacy-consented rich data source. This approach uses smartphone and web behavioural metadata to assess risky applicants and their probability of defaulting in real-time. Furthermore, credolab is the only alternative credit scoring company that uses only privacy-consented, permissioned, depersonalised and anonymised metadata

By combining alternative data with traditional data, new and powerful insights can be gained to improve business projections and financial inclusion. For example, according to McKinsey, retailers that benefit from data analytics in their organisations could increase their operating margins by more than 60%, with the most interesting insights coming from the combination of transactional (billing over time), navigation (on mobile devices) and customer service (returns) data.‍ By considering these alternative data points, financial institutions gain valuable insights into an individual's financial behaviour and repayment capacity, enabling a more comprehensive assessment of creditworthiness. Furthermore, using AI and ML algorithms leveraging alternative data in credit decisions improves existing credit models and increases the probability of financially excluded individuals receiving credit at fair terms.

2. Enable pattern recognition

AI and ML algorithms to excel in identifying behavioural patterns and trends within data. By leveraging these capabilities, financial institutions can make more accurate credit assessments. ML algorithms can identify patterns associated with positive credit behaviour, allowing lenders to extend credit to individuals from marginalised communities even in the absence of extensive credit history.

Credolab, a leading provider of ML-based credit scoring solutions, uses advanced ML algorithms to detect micro-behavioural patterns from over 70,000 permissioned and privacy-consented data points, transforming them into 10 million possible signals. By utilising non-traditional data sources and extracting granular insights about customer behaviour, credolab's ML algorithms offer a comprehensive look into an individual's behaviour, moving away from the sole reliance using traditional credit histories. 

In this way, financial institutions can assess creditworthiness more inclusively and equitably. This allows them to extend credit even to individuals with limited credit histories who demonstrate positive repayment behaviour from marginalised communities.

3. Conduct continuous learning

ML algorithms to enhance alternative credit analysis by continuously learning, allowing models to improve and obtain more accurate behaviour patterns over time. Continuous Machine Learning (CML) monitors and retains models with updated data. CML aims to mimic a human's ability to acquire and fine-tune information and help with constant improvements continuously. CML's most basic application is in circumstances where the data distributions remain constant, but the data is continuous.

ML algorithms, such as those utilised by credolab, can adapt and learn from new data, leading to the evolution of credit scoring models that are more inclusive and accurate over time. These algorithms analyse patterns, trends, and correlations (or lack thereof) within the data to comprehensively understand an individual's creditworthiness. Using smartphone and web metadata, credolab's ML algorithms can enhance alternative credit scoring and constantly adapt to the unique circumstances of marginalised communities. They also expand financial inclusion by incorporating more people into the credit assessment process.

In conclusion, AI and ML algorithms use alternative data, pattern recognition, and continuous learning capabilities to enhance the ability of any financial institution to properly and confidently score even marginalised communities. These AI and ML algorithms enable more accurate and inclusive credit assessments by analysing new data and adapting credit scoring models. Credolab's approach of leveraging smartphone and web metadata can expand financial inclusion to a broader range of individuals, promoting a more inclusive and equitable financial ecosystem.

2. Personalised Financial Education and Guidance:

Marginalised communities often need more financial literacy and education, hindering their ability to make informed financial decisions. To counter this, it is imperative to provide personalised financial education and guidance using AI-powered tools.

Here are some ways AI and ML can help:

1. Tailored content

Businesses can analyse individual preferences, financial behaviour and learning patterns more easily and accurately using AI and ML algorithms to help them create and deliver tailored content and recommendations, empowering marginalised communities with personalised financial education and knowledge. 

Using data-driven solutions powered by AI and ML algorithms, such as those offered by credolab’s solutions, can help to analyse transactional and behavioural data to identify patterns and provide insights into the financial behaviour and preferences of underserved populations and marginalised communities. AI and ML algorithms also help categorise individuals into specific segments based on their current financial needs and goals, which only enhances the delivery of personalised financial education and guidance by addressing each category's specific needs and challenges. 

Furthermore, AI and ML algorithms continuously learn and adapt from new data providing real-time insights which are used to ensure each piece of tailored content and information remains timely and relevant. This creates a feedback data-driven approach, which only helps to refine any recommendations and keeps the information provided in line with evolving and changing needs, financial and economic status and personal circumstances.

2. Behavioural nudges

AI and ML algorithms can analyse transaction histories and financial behaviour to provide actionable insights encouraging positive financial habits and decision-making. 

Leveraging credolab’s ML algorithms can help unlock valuable insights without compromising data privacy and protection. These insights can be used to develop a deeper understanding of customers to enrich existing customer segmentations and even build personas to recommend highly tailored behavioural nudges and personal reminders.

Personalised reminders can be sent based on analysing individuals’ transactional data and financial goals to help maintain a good financial management record, from bill payments to saving targets. Behavioural nudges can be sent based on analysing individual spending patterns and offering alternative spending options or tailored budgeting suggestions. With these two kinds of actionable insights, individuals and even communities, especially marginalised communities, can be put on a path to making more accurate, informed and responsible financial decisions.

As individuals respond to the nudges and their financial behaviour changes, the algorithms gather feedback to refine the nudge strategies. With ML algorithms continuously learning and adapting from the new data, the insights provided will be kept up-to-date and increasingly personalised, relevant, effective, and impactful. 

In conclusion, these AI and ML algorithms provide more accurate and relevant actionable insights than traditional segmentation methods based on obsolete and static data. With credolab's approach of leveraging smartphone and web metadata, the resulting insights and recommendations can guide individuals to develop healthier financial behaviours and achieve their financial goals, empowering marginalised communities with personalised financial education and knowledge. 

3. Accessible and inclusive digital financial services

Marginalised communities often need help accessing traditional financial services due to limited physical infrastructure or lack of identification documents. To counter this, it is imperative to develop accessible and inclusive digital financial services tailored to the needs of marginalised communities.

Here are some ways AI and ML can help:

1. Digital financial solutions

Develop digital financial solutions that provide user-friendly interfaces, simplified account setups, and easy-to-use intuitive features for financial transactions and basic banking services.

Mobile and web banking onboarding journeys can be optimised using insights derived from AI and ML algorithms, like those offered by credolab. The analysis of behavioural data collected while a customer interacts with a web page, a registration, or an application form provides very granular insights about points of friction and hesitation during the onboarding. By identifying potential drop-off points at the onboarding stage, financial institutions can deliver improved experiences, personalised features and tailored interfaces. The most advanced digital banking solutions leverage AI algorithms and advanced analytics to offer intuitive user interfaces that adapt to individual preferences and behaviours, ensuring a seamless experience for users. ML algorithms can be leveraged to streamline the account setup process for genuine customers by, for example, automating identity verification and requesting more information from customers that display either anomalous or outright suspicious behaviours. Alternative data sources, such as those derived from analysing keystroke patterns and cadence, UI interactions, gestures and mouse movements, can help simplify (for good customers) or complicate (for bad actors) the onboarding process.

Furthermore, the same algorithms help enhance digital banking security by continuously learning from individuals’ behaviour and usage patterns. This can even help to detect and prevent fraudulent activities such as Account Takeover attempts in real-time, ensuring a secure environment for accessible and inclusive digital financial services.

2. Biometric identification

Advanced analytics that leverages AI and ML algorithms enable biometric identification to create accessible and inclusive digital financial services. By leveraging these algorithms, biometric authentication methods can ensure secure identity validation and reduce reliance on traditional identification practices, such as using a selfie without liveliness detection or basic verification tools. 

The security and efficiency of biometric identification processes can be enhanced using AI and ML algorithms, like those offered by credolab’s solutions based on behavioural biometrics analytics. Biometric authentication, which refers to verifying the authenticity of a person, where it compares the applicant's individual characteristics to their biometric "template", compares an individual's unique physical or behavioural traits and characteristics, such as fingerprints or face IDs, the way one types or interacts with the web page or mobile onboarding to ensure the validation and authenticity of an individual’s identity. Implementing biometric verification on top of authentication can reduce risks, even account takeover fraud, by verifying users' identities using their biometric data to detect behaviour, device, browser and typing anomalies. This enhances security while providing a seamless user experience, increasing conversion rates and reducing overall risks.

For instance, credolab's solutions enable financial institutions to identify data familiarity and data manipulation as a way to discriminate between genuine and fraudulent applicants on mobile and web, resulting in improved approval rates and reduced fraud costs. Another example of solutions incorporating behavioural biometrics at the identification, verification and authentication stages is credolab’s solution in collaboration with TransUnion. In one seamless and frictionless digital onboarding process, TransUnion and credolab leverage the latest AI and ML algorithms applied to traditional and alternative data sources to proactively enable lenders and non-financial institutions to any borrowers' risk and fraud levels.

Expanding financial inclusion to marginalised communities requires innovative strategies that address their unique challenges and leverage the latest AI and ML technologies to stay ahead of fraudsters while letting genuine individuals through. By enhancing alternative credit scoring, providing personalised financial education, and developing accessible digital financial services, we can empower marginalised communities to access and utilise financial services effectively and with fair terms. Through these efforts, the financial gap can thus be bridged to create a more inclusive and equitable financial system for all.


This article delves into the crucial role of financial inclusion in empowering marginalised communities and fostering economic growth. It highlights the need for innovative approaches, such as advanced technologies like AI and ML algorithms, to bridge the gap between underserved populations and formal financial systems. 

Several initiatives are underway to improve financial inclusion in regions like LATAM and APAC, including digital financial services, alternative credit scoring, microfinance, and financial education. Furthermore, the COVID-19 pandemic has highlighted the significance of inclusive financial systems for vulnerable communities. 

The article also dives briefly into the importance of protecting privacy and data in data-driven solutions for financial inclusion to build the foundation for financial inclusion strategies. The focus then moves to explore three key innovative strategies and how using AI and ML can help in: 

  1. Enhancing alternative credit scoring using AI and ML, 
  2. Providing personalised financial education and guidance
  3. Creating accessible and inclusive digital financial services

These strategies’ ultimate goal was to expand financial inclusion, which helps reduce poverty, foster economic growth, and improve access to financial services for marginalised individuals and communities.


  • Alternative data: The set of information on behaviours, habits, interests and transactions carried out by a person and obtained from non-traditional sources.
  • Biometric authentication: Verifying the authenticity of a person, where it compares the applicant's individual characteristics to their biometric "template”.
  • Credit history: A record of an individual's borrowing and repayment behaviour, often used by lenders to assess creditworthiness.
  • Digital financial services: Financial services that are accessed through digital channels, such as mobile phones or the Internet.
  • Financial literacy: The knowledge and skills needed to make informed and effective financial decisions.
  • Financial technology (Fintech): The use of technology to provide financial services, often using innovative approaches to reach underserved populations.
  • Microfinance: Providing financial services such as credit and savings to low-income individuals or small businesses.
  • Mobile banking: Using mobile devices to access financial services, such as banking and payments.
  • Unbanked: People who do not have access to financial services.
  • Underbanked: People with limited access, often in the form of informal financial services (e.g. illegal loans, relatives/family member loans, etc.).

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