"Black Box" problem is our biggest hurdle in regulated finance. We know that AI can process hundred and thousands of documents in seconds. It lacks the moral compass required for high-stakes decision-making. At our company we treat AI as a Junior Analyst, it will do the main work of processing and data aggregation, but a human always does the final sign-off. Regulators do not do audit algorithms. We use AI for speed, but have human or we keep human for their Accountability.
I work at Onyx Platform (onyxplatform.com). We are an insurance agency operations platform. We built and continuously improve our platform to streamline agency business, boost margins, and give back agency teams and agents specifically their most valuable resource: time. Our core belief is that for insurance and financial services, AI is a multiplier, not the replacement. We are building our technology around the deep understanding that AI can eliminate the repetitive operational work that keeps agents from their clients. That could be data entry, compliance reviews, or surfacing the right actions to improve agent/agency performance. But insurance is a trust-and-human-connection business. AI automation frees human experts to spend more time understanding the customer, their needs, building trust, and finding the right coverage.
AI is everywhere, in capital raising and even in daily life processes and that adds pressure on Fintech companies too. While I advise startups in capital raising, I believe automations specifically in finance is good, but the goal should be more leaned towards defensible augmentation. In areas like underwriting, fraud detection, and AML monitoring, AI is increasingly used to prioritize risk signals, surface anomalies, and reduce false positives, while trained compliance officers retain final decision authority. Humans are needed to be there, as we can only provide data to AI for objectivity while subjectivity judgement has to be the call of Human beings. Only then, we can deliver efficiency gains. I have seen this alot, that many EU and UK firms are favoring models that provide traceable decision logic over purely black-box accuracy, especially where consumer outcomes are affected. AI handles pattern recognition at scale, but escalation workflows, edge cases, and regulatory interpretation still depend on experienced professionals. The goal should not be replacement, but judgement that make things easier for the next entity
Regulatory authorities are no longer accepting companies that say, "It was the algorithm that made the decision." As a result, FinTechs are shifting away from automation's traditional 'set it and forget it' mindset and developing businesses with a tiered architecture-where an artificial intelligence (AI) solution identifies and flags high-risk transactions (e.g., fraudulent activity or loan defaults)-and the actual decision remains with humans. Human beings are still ultimately responsible for all decisions regardless of the volume of transactions and automation required to process these transactions. The use of AI to 'set it and forget it' would provide a significant lack of audit trails or records that regulatory or compliance officers require. In regulated financial markets, the conflict is not due to technology, but rather due to the reason that AI is really very complex, which means it is difficult for human experts to trace back a rejected loan or transaction to a specific non-discriminatory data point. According to the Financial Stability Board, the complicated nature of the technology used in AI creates an "opaque" condition, which makes it difficult for compliance or regulatory officers to manage risk based on their regulatory obligations. Companies are now taking steps to embed an explainability layer into their operations to allow AI technologies to enhance decision-making without a loss of professional judgement. Managing these competing forces is ultimately risk management rather than just operational efficiency; therefore, while an AI solution can process one million transactions in one second, it lacks the context necessary to understand the "why" of complex financial irregularities. Therefore, keeping human acts or checks on all AI generated decisions is essential to ensuring that the pace of change and innovation does not exceed the stability of a financial services institution.
Fintech companies can use automation for predictable tasks while relying on human expertise for more important decisions. In regulated finance, this means AI can help summarize policies, draft case notes, and prioritize alerts. However, AI should not be the sole authority on outcomes that might harm a customer. Human intervention is necessary when making adverse decisions, with a written explanation that can be defended. It is important to have strict rules for human review. Any negative decision should trigger a review process. Reviewers can label errors to improve AI over time. Tight access controls and limited data exposure help maintain security while improving consistency and efficiency.