Sydney, Australia | 14 March 2024

By Charles Guan, CTO & Co-Founder

Financial inclusion remains a significant challenge not only in developing countries but also within our own communities. A poignant example is a personal story about my niece, a university student with a stable job, who faced unexpected hurdles when she applied for an NBN plan. Despite her stable income and occupation, she was denied due to her lack of credit history. This incident serves as a stark reminder of how traditional methods of assessing creditworthiness can disproportionately affect those new to credit.

The Impact of Credit History on Young Adults and Small Businesses

Her story isn’t unique. Many individuals, especially young adults just starting their careers or small businesses seeking capital for growth, encounter similar obstacles. The reliance solely on credit history to gauge creditworthiness can inadvertently exclude many deserving candidates from accessing essential services or capital. This practice stifles not only individual opportunities but also broader economic growth and job creation.

However, the landscape of financial assessments is poised for a transformation, thanks to advancements in technology such as artificial intelligence (AI), machine learning, and open banking. These technologies offer the promise of a more inclusive financial system that evaluates potential borrowers based on a wider range of data points, including consistent income and the ability to service loans—factors often overlooked in traditional credit scoring models.

The Role of AI and Machine Learning in Credit Scoring

AI and machine learning, in particular, have the capability to analyse vast amounts of data quickly and accurately. By integrating these technologies, financial institutions can implement more nuanced and comprehensive credit scoring models. These models can consider a person’s overall financial behaviour rather than just their credit history. For instance, consistent payments for utilities, rent, and even subscriptions can be indicative of financial responsibility.

Open banking further enhances this by allowing the secure sharing of financial data between banks and third-party providers with the consumer’s consent. This enables a richer, real-time view of a person’s financial health. Together, these technologies can create a more dynamic and fair credit assessment process, reducing biases against those new to credit or with minimal credit history.

The Future of Financial Inclusion

The integration of autonomous AI agents can also streamline the data processing and pattern recognition required for these advanced analyses. This not only makes the process more cost-effective and scalable but also helps financial institutions to quickly adapt to changing financial behaviours and trends. As a result, a broader swath of society can gain access to financial products and services, fostering a more inclusive financial ecosystem.

The conversation about gathering insights on leveraging technology to support those new to credit is both timely and necessary. As more financial institutions recognise the limitations of traditional credit scoring and embrace technological innovations, a shift towards a more equitable financial system is expected.

In conclusion, by harnessing the power of AI, machine learning, and open banking, we can redefine financial inclusion, making it more reflective of an individual’s true financial capabilities. This shift not only benefits those currently excluded but also enriches the entire financial ecosystem by bringing diverse participants into the fold, ultimately creating a more resilient and dynamic economy.


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