Why Trust Needs an Upgrade: Rethinking Credit and Compliance in the Age of Automation

Trust has always been the quiet force behind trade.

Before formal financial systems took shape, trade credit often depended on deeply personal trust—or painful compromises. Families would mortgage their land or jewellery just to secure working capital. Business was conducted on the strength of one’s word, reputation, and community ties, with no real safety nets if things went wrong. Credit risk assessment was entirely based on personal relationships and local knowledge.

Fast forward to today. When buyers and sellers are separated by borders, currencies, and unfamiliar legal systems, trust remains the glue that holds everything together. It's what drives a business based in Coimbatore to send a consignment to Rotterdam, based on little more than paperwork and a promise. Modern trade credit systems must navigate complex international regulations while conducting thorough credit risk assessment across multiple jurisdictions.

Traditionally, this trust was formalized through instruments like letters of credit, bank guarantees, and compliance checks - paper-heavy processes anchored in human judgment and institutional reputation. These traditional trade credit instruments required extensive manual credit risk assessment procedures. But trade credit today moves faster, further, and at a scale these legacy tools were never built for.

A single trade finance deal can involve more than 100 pages of documentation, passed between 20+ parties, triggering over 5,000 data field interactions. Yet, according to the 2024 FIT Alliance Global Survey, more than half of respondents (50.8%) still rely on paper bills of lading. Paper makes processes sluggish, introduces errors, and leaves room for fraud. This inefficiency directly impacts trade credit availability and complicates credit risk assessment for financial institutions.

Meanwhile, the pressure on the broader financial system to improve transparency is increasing. In 2023 alone, $3.1 trillion in illicit money moved through global financial channels, of which $346.7 billion was tied to fraud and $11.5 billion was linked to terrorism financing (2024–25 Financial Crime and Compliance Report). Under this pressure, old ways of building trust in business are showing cracks.

This brings us to a key question: Can technology, not just speed up trade, but make it more trustworthy?

From Institutions to Infrastructure: A Shift in Trust

In the traditional model, we placed trust in known entities: a bank’s name, a stamped document, a familiar process. In a digital-first world, trust shifts from people and paperwork to systems and code.

Blockchain offers one of the clearest examples of this shift. With smart contracts, tamper-proof audit trails, and immutable ledgers, blockchain replaces intermediaries with protocols. Combined with new legal frameworks like the UK’s Electronic Trade Documents Act or MLETR-based laws in Singapore and the UAE, trust is becoming programmable—hardcoded into the infrastructure itself.

A good example is the Vayana Debt Platform (VDP), which uses blockchain to digitize and tokenize B2B trade finance. VDP enables digital settlement using stablecoins and CBDCs, and is laying the groundwork for tokenizing assets like trade documents, bonds, and equity—helping make B2B trade credit faster, safer, and more transparent.

Next comes compliance—and here, regulatory technology (RegTech) is leading the transformation. Tasks like KYC, AML checks, and sanctions screening are becoming continuous and automated, replacing static, one-time reviews. For example, the Rubix Early Warning System (EWS) monitors changes in a company’s legal status, ownership, or financial health in real time and alerts businesses about the changes in their counterparty's risk.

The Trust Paradox

However, here’s the twist: The more we automate trust, the more we have to trust the automation.

AI systems aren’t flawless, and can be biased, especially in markets where data is limited or outdated. Who is responsible when a business is incorrectly flagged by an algorithm?

This “black box” risk makes regulators nervous for good reason. To that, if you add inconsistent digital standards, fragmented laws, and restrictions on cross-border data sharing, the trust in automation starts wavering.

Therefore, to be trustworthy, an automated system must be:

• Transparent – Can it explain its decisions?

• Consistent – Does it behave reliably across different situations?

• Auditable – Can we trace what it did, and why?

• Data-Reliant – Is it using current, complete, and relevant information?

• Governed – Who is held accountable if something goes wrong?

When these elements are built into systems, automation strengthens trust. Machines can spot red flags that humans miss, rules get enforced more consistently, and decisions become explainable, not arbitrary.

Building a New Trust Architecture

Clearly, the future of trust lies in hybrid systems, where people and machines work together.
While automation can handle volume, speed, and complex pattern detection, humans can step in when judgment is needed or when the situation isn't black and white. This approach is particularly valuable for trade credit decisions, where nuanced understanding of business relationships and market conditions remains crucial. Advanced credit risk assessment tools can process vast amounts of data, but human expertise ensures contextual understanding of trade credit relationships. This will help develop digital trust with better tools, better design, governance, and accountability.

This means banks and lenders can invest in explainable AI and strong data pipelines for more accurate credit risk assessment, regulators can build flexible, forward-looking rules that support innovation without compromising stability, and MSMEs can become digitally visible to access this new world of trade credit finance. Modern credit risk assessment methodologies must evolve to support the growing demand for accessible trade credit solutions.

The verdict? Trust cannot be automated fully, but it can be built into the systems we now rely on. We may no longer need to trust a signature on a stamped document, but we do need to trust the algorithms and digital contracts that are reshaping trade.

So, let's modify the question: Are the systems we rely on worthy of our trust?