Invisible risks that are becoming Fintech's biggest liabilities

The success of businesses in the Fintech sector depends heavily on their staying ahead of the tech curve. But given technology’s accelerating rate of change, today’s Fintech leaders may feel more preoccupied, more vulnerable, and less certain than ever.

Technological acceleration¹ is outpacing visibility, exposing Fintechs to risks where oversight is stretched to the breaking point – across third-party dependencies, amidst the tangle of regulations governing their sector, and even in the “black box” of AI’s inner workings. 

These blind spots rarely announce themselves until something fails. And when that happens, traditional insurance coverage may fall short, especially when claims stem from new technology-driven incidents. 

So how can Fintechs build resilience when the most consequential risks are also the hardest to detect?

Dependence on third-party and cloud infrastructure

Fintechs often depend on a complex web of third-party vendors, cloud services, and Application Programming Interfaces (APIs). Each additional dependency makes it more difficult for Fintechs to exercise real-time oversight – a vulnerability that threat actors use to their advantage, attacking areas in the network periphery where visibility is limited.² 

APIs, the connective tissue of this ecosystem, are a prime target. According to an Indusface report, API attacks surged by 104% in H1 2025,³ with APIs experiencing 388% more Distributed Denial-of-Service (DDoS) attacks than websites. 

Beyond the obvious impact of breaches on their vendors’ operations, these attacks on the periphery can also lead to professional indemnity claims on the Fintech as well, should they face allegations of breaches in their professional duty of care. 

In this complex environment, Fintechs need professional indemnity and cyber insurance that are designed around their specific vendor dependencies and risk profile. If full visibility can’t be guaranteed, insurance needs to account for risks that sit beyond Fintechs’ direct control.

The AI "black box" dilemma

Many Artificial Intelligence (AI) and Machine Learning (ML) models function as a “black box”⁴ where users can see inputs and outputs, but they’re blind to the system’s decision-making process. AI outputs are thus difficult to verify, slowing corrective action and allowing bias, privacy risks, and security weaknesses to persist. 

As Fintechs lean harder on AI⁵ to take on workloads in a diverse range of functions⁶ – from credit risk assessment to algorithmic trading and portfolio management – they become more exposed to discrimination claims if the AI system can be shown to reflect unwanted biases. 

 A 2024 Singapore-based test of four major large language models (LLMs)⁷ found that incidents of bias could be triggered with surprising ease, often by using just a single prompt. Gender bias accounted for 26.1% of total successful exploits, with race/religious/ethnicity bias (at 22.8%) and geographical/national identity bias (at 22.6%) coming up a close second and third.

It’s easy to see how these hidden biases can be rapidly amplified through a Fintech operating AI systems at scale – with significant legal, financial, and compliance exposure in the balance.

Supervisory and regulatory lag

The cross-sectoral, cross-border nature of modern Fintech ecosystems creates regulatory fragmentation across markets, which in turn elevates risk for Fintechs themselves. 

Regulatory frameworks evolve at different speeds across jurisdictions, with varying degrees of maturity. On the flip side, Fintechs that don’t keep up with new regulations will find the cost of their negligence adding up fast: failing to conform with Singapore’s Personal Data Protection Act (PDPA) risks financial penalties⁸ of up to 10% of a company’s annual turnover in Singapore.

In one of the biggest cases of 2025, the Monetary Authority of Singapore (MAS) imposed a total of S$960,000⁹ in composition penalties on five licensed Major Payment Institutions (MPIs) for breaching anti-money laundering and anti-terrorism financing regulations.

In this exacting regulatory climate, Fintechs need to treat compliance as an ongoing obligation, not a one-time exercise. They must stay ahead of current requirements while remaining adaptable to future legislative developments. 

This includes regular reviews of insurance coverage to ensure alignment with current and anticipated regulatory risk. It is helpful to work with a reliable broker who will go the extra mile to review policy wording minutely to identify subtle differences that might create gaps in coverage later on.

Creating a plan to mitigate blind spot risks

Many Fintechs’ risk mitigation efforts focus on technical controls, but they could be further augmented via structured risk transfer through insurance. 

Fintechs should use insurance as part of a multi-layered risk management strategy tailored to their industry’s unique realities. This strategy might include a number of insurance products, each designed to address separate aspects of risk. 

• Professional Indemnity (PI) Insurance: This policy covers claims arising when a Fintech’s professional services cause client loss, including situations where AI-driven outputs might lead to incorrect decisions or financial harm. PI can help defray costs associated with regulatory actions and litigation, including defence costs and damages or settlements resulting from the outcome.
• Directors & Officers (D&O) Insurance: This policy protects a company's board members and officers from personal liability in lawsuits related to their management decisions. For Fintechs, this is critical for claims arising from failure to comply with regulations or possible infractions resulting from AI usage, just to name a few. It is especially important for funding the significant legal defence costs during investigations by regulators and litigation by third parties.
• Commercial Crime Insurance: This policy protects the company against monetary losses arising from theft and fraud committed by its own employees, and when they collude with outsiders, or are duped by social engineering attacks. Given the impossibility of perfect oversight over Fintech employees and their privileged access to sensitive financial data, this coverage is a key safeguard against balance sheet losses.
• Cyber Insurance: Today’s cyber insurance products provide coverage for both first-party and third-party losses – typically covering your liability to a client for a cyber incident originating from your systems, or that of your vendors (third-party liability), while also covering your own direct losses (first-party liability), such as the costs of business interruption, forensic investigations, and ransom payments. 

The rapid evolution of the Fintech landscape has created powerful new capabilities, while generating dangerous operational blind spots that can endanger companies without a game plan. As traditional insurance policies may struggle to keep pace with these complex, fast-moving threats, building a strategic, multi-layered insurance framework remains an essential tool for building resilience. 

In the race to achieve unicorn status, Fintechs must remember that scale creates value – but resilience preserves it. 

¹ Our World in Data, Technology over the long run: zoom out to see how dramatically the world can change within a lifetime
² CrowdStrike, 2025 Global Threat Report
³ Indusface, State of Application Security – H1 2025
⁴ IBM, What Is Black Box AI and How Does It Work? 
⁵ Fintech Singapore, AI Reaches Widespread Adoption in Finance, Yet Full Integration Still Lags
⁶ IBM, AI in Fintech​​​​​​​
⁷ Campaign Asia, Nearly 70% of bias incidents in AI LLMs occur in regional languages: IMDA study
⁸ DLA Piper, Data protection laws in Singapore
⁹ Fintech Singapore, MAS Fines Five Payment Firms S$960,000 Over AML/CFT Control Failures