AI and alternative data hold the key to financial inclusion

A guest editorial by Dmitry Borodin, head of decision analytics at Creditinfo

Despite global efforts to expand access to finance, underserved demographics still face barriers. Almost 1.5 billion people in emerging economies don’t have access to formal savings and credit, and this socioeconomic gap particularly affects women and people under 25. For example, in West Africa, only 27% of contract borrowers are women, while in Kenya, just 0.4% of 20 – 24-year-olds secured formal loans exceeding $200 in 2023.

These financial barriers can negatively impact people’s lives. Without a solid credit history, many individuals will struggle to access housing, pay for utilities, or even attain a mobile phone plan.

Alternative data and artificial intelligence (AI) have the potential to break these barriers down. By integrating the two, institutions can work towards creating a more inclusive financial ecosystem – one that evaluates credit risk using a wider range of factors beyond traditional credit history.

Defining alternative data

Traditional credit scoring models often rely on a narrow set of data, which leaves millions of people unable to access loans. Alternative data, however, uses sources such as mobile app usage, social media activity, and digital transaction histories to analyse an individual’s financial behaviour.

Mobile transaction data, for example, can indicate an individual’s repayment capacity by providing banks and lenders with valuable information about their income and cash flow. In addition, social media activity, such as comments and instant messaging records, can help detect fraudulent behaviour, contributing to overall credit risk assessment. Some institutions even use satellite imagery to evaluate property or agricultural land as assets that may serve as collateral for loans.

It’s important to highlight that financial organisations need structured and secure data sets to achieve the best results from each of these methods. By integrating alternative data sources into their credit risk decision making process, financial institutions can establish more accurate and fair credit profiles. This means that individuals with little to no traditional credit history have a greater chance of accessing financial services.

AI as a tool for credit scoring

Sophisticated AI and machine learning systems are essential for harnessing large volumes of data. Credit risk modelers can implement these advanced AI-driven models to discover patterns that traditional analysis might not pick up on. This is especially useful when it comes to unstructured data, which doesn’t have a predefined format.

AI-powered tools also offer personalised finance tips, helping individuals, particularly young people, make informed decisions about their finances and build financial stability. Additionally, with AI and alternative data, financial products can be customised to better suit individuals’ needs. For example, tailored loans with flexible repayment plans or personalised insurance policies.

AI needs human oversight

AI is a powerful asset, but its true impact depends on how it’s used. For example, AI-driven credit scoring can lead to biases in decision-making. If models are trained on historical data containing biases, they may unintentionally reinforce existing disparities, favouring certain groups based on race, gender, or socioeconomic status. This can lead to discriminatory outcomes and hinder efforts to promote financial inclusion. However, with a little human oversight, this can be overcome.

Many see AI as a replacement for human decision-making. However, the best approach is a balanced one. Combining the efficiency of AI and machine learning with practical judgment from people will lead to the most reliable results. While AI excels at automation and pattern detection, it should be seen as a helpful tool for enhancing and streamlining financial processes and complementing human expertise, rather than replacing it. Intervention from people is essential for interpreting results, correcting biases, and ensuring fair and accurate decision-making.

Ensuring both data privacy and security is equally critical. AI-powered credit scoring relies on diverse personal and sensitive data, making it vital to protect against breaches and misuse. Additionally, the accuracy and quality of data used to train AI models play a significant role in their effectiveness. Poor-quality data can lead to flawed predictions, further perpetuating biases.

Conclusion

A strong and inclusive financial system is crucial for building a resilient economy, especially during rising inflation and global economic uncertainty. To safeguard economic stability, countries must establish solid financial foundations to encourage greater participation in local economies and foster sustainable and inclusive growth.

AI and alternative data play a vital role in this, but their value depends on secure collection, ethical usage, and responsible sharing. By integrating advanced AI, machine learning models, and well-defined regulatory frameworks, financial institutions can reap the benefits of alternative data in widening access to financial services.

Image: Creditinfo

Guest Editorial This article was produced specially for Fintech Intel by an expert guest contributor.