By Boris Bialek, VP and Global Field of CTO Industries, MongoDB
There are almost 5.6 million credit invisible people in the UK. This means they have little or no credit history, making it difficult to access financial services and products. For years, these individuals (often called the unbanked) have been excluded from the financial system by traditional credit models, which rely heavily on past credit card use and loan repayment histories. However, the financial industry is now undergoing a transformation. A new wave of alternative data-driven credit scoring methods is emerging, offering a more inclusive approach that incorporates diverse datasets, such as utility payments, rental histories, and mobile phone usage.
This shift isn’t just about providing fairer access to credit. It presents a unique opportunity for companies in the financial services sector. By embracing these innovative models, lenders can unlock new markets, reaching millions of potential customers previously left behind. In doing so, they not only expand their customer base but also gain a competitive advantage in an increasingly crowded market. Financial companies can tap into these emerging methods and find substantial benefits in doing so, including increased customer acquisition, improved risk assessment, and enhanced financial inclusion.
The power of alternative data to credit invisible individuals
The UK’s credit invisible population: students, immigrants, gig workers. These groups tend to be financially responsible but lack a conventional credit history. Irregular income poses a challenge for traditional credit scoring models, potentially labeling individuals as higher risk and leading to application denials or restrictive credit limits. By integrating alternative data, lenders can assess financial behaviours beyond the narrow scope of traditional credit models.
Consider rental payments, a consistent indicator of financial discipline for millions of renters in the UK. Historically overlooked, this data now offers lenders a clearer picture of an individual’s creditworthiness. For example, utility bills and subscription payments also provide a wealth of insights into financial stability. Lenders can use this data to assess an individual’s overall financial reliability. For instance, if someone consistently pays their electricity bill on time, it signals to lenders that the individual has a steady cash flow and is responsible with essential financial commitments. When included in credit scoring models, rental data provides lenders with a clearer picture of an individual’s financial responsibility, even in the absence of traditional credit lines such as credit cards or loans.
The nature of the credit system beast means they can be slow to changing economic conditions and landscapes, and evolving consumer behaviours. To overcome these hurdles, banks and lenders are looking to adopt AI to develop increasingly sophisticated models for scoring credit risk.
The challenge is that banks tend to have very rigid structures and processes around anything related to credit. One of the biggest hurdles is the reliance on “regular pay slips” for example. In cases where someone works in the gig or job economy, they may have a great income, but no traditional pay slips to show. This creates a mismatch between their actual financial status and the system’s understanding of their creditworthiness. The solution lies in smarter, AI-based credit parametrization. This approach considers money flow—such as income versus spending, saving behavior, and other relevant factors—rather than relying solely on the last few pay slips.
Addressing the financial inclusion gap
For lenders, incorporating alternative data reduces the risk of bad loans by providing a fuller picture of an applicant’s financial behaviour. For consumers, it means access to loans, mortgages, and financial products that were previously out of reach. This is especially significant in underserved communities where traditional credit metrics fail to capture responsible financial habits. By including alternative data in credit assessments, we can level the playing field and bring more people into the formal financial system.
Advancements in AI, ML, big data analytics and cloud computing have enabled lenders to process vast amounts of alternative data quickly and accurately. For example, Gen AI’s capability to synthesise diverse data sets addresses one of the key limitations of traditional credit scoring: the reliance on historical credit data. By creating synthetic data that mirrors actual real world financial behaviours, Gen AI models enable a more inclusive assessment of creditworthiness. This transformative shift promotes financial inclusivity, opening doors for a broader demographic to access credit opportunities.
One challenge of utilising Gen AI is the problem of hallucination, where the model may present information that is either nonsensical or outright false. There are several techniques to mitigate this risk. For example, using the Retrieval Augment Generation (RAG) approach. RAG minimises hallucinations by grounding the model’s responses in factual information from up-to-date sources, ensuring the model’s responses reflect the most current and accurate information available.
What’s next for financial services?
As these new methods become more widely adopted, we’re likely to see a broader range of tailored financial products, faster credit approvals, and increased confidence in lending. By utilising alternative data, they enable more inclusive and efficient credit scoring models, addressing the gaps that traditional banking systems often overlook. Small businesses often face barriers due to abstract or rigid banking criteria, which can make access to credit a challenge despite having solid operational performance.
However, challenges remain—particularly around ensuring the ethical use of data, maintaining privacy, and navigating evolving regulatory landscapes. In the UK, strong oversight and collaboration between financial institutions, technology providers, and regulators will be key to ensuring these innovations deliver their full potential.
The rise of alternative data is ushering in a new era of credit scoring—one that prioritises inclusion, fairness, and real-world financial behaviours. As the financial industry embraces this shift, the potential to transform lives and drive economic growth is enormous. The future of credit is not just about numbers—it’s about creating opportunities for all.
