Artificial intelligence (AI) is quietly transforming how people access credit. While most borrowers may still associate loan approval with traditional requirements like payslips and collateral, a different reality is beginning to unfold—one where algorithms, not people, assess financial trustworthiness. The rise of AI-powered lending platforms is reshaping how credit is scored. This affects how inclusion, risk and responsibility are defined in the digital economy.
For decades, financial institutions primarily evaluated borrowers based on formal employment, bank history and past loans. This left out a significant portion of the population, especially gig workers, small-scale entrepreneurs and young people without traditional banking footprints. In a country where millions remain unbanked or underbanked, the inability to present formal documentation often meant exclusion from formal credit systems. AI changes this by using alternative data, such as telco usage, e-wallet transactions, online shopping behavior and even utility bill payments, to form a picture of financial behavior and creditworthiness.
This shift is not just about data; it’s about insight. Machine learning models process patterns and trends from a variety of digital activities to predict a person’s likelihood to repay a loan. These models can update in real time, meaning that responsible financial behavior, like topping up savings regularly or paying phone bills on time, can positively influence one’s digital credit score. The potential is promising: AI can make lending more inclusive, allowing those historically left out of the system to finally access the capital they need to grow a small business or cover emergency expenses.
However, this innovation comes with caution. Not all AI credit scoring systems are transparent. Some platforms function like black boxes, providing approvals or rejections without any explanation. This lack of clarity can lead to feelings of helplessness, especially when a borrower is rejected but doesn’t understand why. Explainable AI, or XAI, aims to address this by offering users insights into the decision-making process. For example, a platform may inform a rejected applicant that their low savings rate or inconsistent income was the deciding factor, helping the individual make informed adjustments for future applications.
Beyond scoring, AI is now used to manage the entire lending experience. Some platforms offer what are called AI “co-pilots”—digital agents that help users track payments, understand loan terms and offer personalized reminders. These features can make borrowing more manageable, especially for first-time users who are unfamiliar with financial jargon or formal banking systems. Other platforms segment borrowers into risk profiles and adjust loan amounts, interest rates or payment schedules accordingly. If a user repays on time, they may be offered better terms next time. If red flags are detected, such as erratic transactions or sudden spending spikes, the system may trigger a pause or intervention.
The challenge now lies in ensuring that these systems are designed responsibly. There is a real risk that algorithmic decisions may replicate human biases or even introduce new ones if the data used for training is incomplete or skewed. For instance, a person from a rural area with limited digital activity may be penalized simply due to a lack of data, not actual financial unreliability. That’s why regulators, technologists and civil society must work together to enforce standards that prioritize fairness, transparency and user consent.
Another concern is consent fatigue and data privacy. Borrowers may unknowingly agree to share extensive personal information with lending apps in exchange for a quick loan. There needs to be a stronger emphasis on data literacy, so users fully understand how their digital footprint influences lending decisions. At the same time, fintech companies must be held accountable for how they collect, process, and secure user data.
Despite these challenges, the promise of AI in lending is immense. It can help the country move toward a more inclusive financial ecosystem, where more people have access to life-changing financial products and services. But inclusion must come with protection. Financial institutions and fintech startups must ensure that they are not just using AI to expand their market reach, but also to build trust and financial resilience among their users.
The future of lending in the Philippines won’t be decided solely by interest rates or payment terms. It will be shaped by the ethical frameworks we build around the tools we use. If we get it right, AI can help us not only score credit better but also build a more equitable financial system for all.