{"id":6747,"date":"2025-11-19T12:43:55","date_gmt":"2025-11-19T12:43:55","guid":{"rendered":"https:\/\/fintech.global\/globalregtechsummit\/?p=6747"},"modified":"2025-11-19T12:43:56","modified_gmt":"2025-11-19T12:43:56","slug":"is-transparency-the-final-barrier-to-true-ai-compliance","status":"publish","type":"post","link":"https:\/\/fintech.global\/globalregtechsummit\/is-transparency-the-final-barrier-to-true-ai-compliance\/","title":{"rendered":"Is transparency the final barrier to true AI compliance?"},"content":{"rendered":"

As AI becomes deeply embedded in compliance operations, one challenge continues to loom large: transparency. While machine-driven monitoring and decision-making promise speed and accuracy, many of these systems still operate as opaque black boxes \u2014 a problem for regulators and firms that must justify every outcome. The question now is whether greater transparency, powered by explainable AI, is the final hurdle standing between today\u2019s automated tools and true, regulator-ready AI compliance. <\/strong><\/p>

For Areg Nzsdejan, CEO and founder of Cardamon<\/a>, the conversation around AI in compliance often swings between two polls \u2013 efficiency and accountability.<\/p>

\u201cEveryone agrees automation can transform regulatory work, but few talk about what happens when an algorithm makes a decision that affects a client, a transaction, or even a regulator\u2019s interpretation,\u201d said Nzsdejan. \u201cThat\u2019s where explainable AI comes in \u2013 and it may be the bridge between automation and genuine trust in compliance.\u201d<\/p>

For the Cardamon CEO, explainable AI brings visibility into how compliance decisions are made, not just what the outcome is. Instead of a simple black box that outputs \u2018approved\u2019 or flagged\u2019, such a technology enables compliance teams to trace the lgoci behind each result \u2013 the data that was used, the reasoning behind the final call and the thresholds triggered.<\/p>

He said, \u201cThis kind of transparency is something we\u2019ve embedded across Cardamon\u2019s regulatory intelligence engine. When the system maps an obligation or calculates residual risk, users can view the underlying rationale. It\u2019s not just automation for speed\u2019s sake; it\u2019s automation you can stand behind, backed by traceable evidence and context.\u201d<\/p>

Currently under GDPR, individuals have the right to understand how automated decisions affect them. Nzsdejan outlined that explainable AI turns that principle into practice. \u201cIt can provide human-readable explanations of why a particular outcome occurred, what factors influenced it most, and what could have changed the result,\u201d he said.<\/p>

Nzsdejan went on, \u201cIn compliance, this matters deeply. Regulators and internal teams alike need to know that automated systems aren\u2019t making arbitrary calls. By surfacing the reasoning behind each output, explainable AI transforms the \u201cright to explanation\u201d from a legal checkbox into a living, operational safeguard. At Cardamon, that\u2019s reflected in how we design audit trails and decision logs \u2013 clear, interpretable, and regulator-ready.\u201d<\/p>

For the Cardamon CEO, explainability also reduces one of the biggest operational pains in compliance: the audit burden.\u201d Instead of pulling scattered spreadsheets and recreating rationale months after the fact, explainable AI keeps a running record of what the model did and why,\u201d he said.<\/p>

In addition, he stressed that when a regulator asks why an alert was raised or an obligation was mapped, compliance teams can now provide a transparent, structured answer. This, he added, was about not about faster audits \u2013 but creating a compliance culture where every automated action is inherently defensible.<\/p>

A key challenge that remains, despite this, is balancing transparency with intellectual property. Here, Nzsdejan detailed that firms need to show how their systems make decisions without exposing their algorithms or data pipelines.<\/p>

\u201cExplainable AI supports this through layered disclosure \u2013 offering meaningful logic and key factors while abstracting sensitive details. It\u2019s the balance between openness and security, and it\u2019s one every compliance technology provider needs to get right,\u201d he stated.<\/p>

Explainable AI isn\u2019t just a future concept \u2013 it\u2019s becoming, in the view of Nzsdejan, a practical necessity.<\/p>

\u201cIt helps firms justify automated decisions, meet data protection obligations, and build credibility with regulators. At Cardamon, we think about it less as a feature and more as a principle: if an AI system can\u2019t explain itself, it shouldn\u2019t be making regulatory calls. The future of compliance will be defined not just by how fast automation moves, but by how clearly it can show its work,\u201d he exclaimed.<\/p>

A crucial aspect<\/strong><\/p>

According to South African RegTech firm RelyComply<\/a>, considering the ever-increasing need of AI to keep up with even some base-level compliance shifts, many businesses have been investing to hone design and development expertise that can curb its poorer connotations for unethical practice, hallucinations and bias.<\/p>

The firm added, \u201cNow, XAI is becoming a crucial aspect in the regulatory field: transparency around how models are used, how they arrive at their generated outcomes, and how their capabilities are easily understood.\u201d<\/p>

This, the company claims, marks a stark difference to the black box algorithms that were only explainable unto themselves. \u201cThat alone cannot appease heavy AI restrictions that are tightening as our understanding of its discriminatory or harmful biases grows. Hereby, technical knowledge from data scientists and RegTech partners is paramount to not just ensure AI\u2019s real-time data processing and segmentation is implemented, but made highly explainable and traceable for auditory purposes,\u201d the business added.<\/p>

XAI is not the only missing link for justifying the technology\u2019s usage in financial crime investigation and detection, as it cannot work without the human element \u2013 accountability that a compliance function\u2019s AML controls are carried out safely, securely and without risk of disclosing personal information, the enterprise added. XAI, it went on, can only be trained according to specific requirements through comprehensive model training at the hands of experts.<\/p>

Inside a stricter regulatory framework, firms that are able to maintain XAI from the very beginning for their automations can then get a competitive advantage.<\/p>

Development time, the firm said, can be met realistically by processing only the data intended for AML usage. Mapping a model\u2019s level of explainability against risk factors sets up XAI in a safe way, and as a working baseline to be improved over time through regimented testing.<\/p>

RelyComply concluded, \u201cXAI is only a start to making our AML systems greater, and a way to protect the integrity of data usage that should benefit institutions, regulators and customers while AI\u2019s technical capabilities for anti-fincrime grows.\u201d<\/p>

The core component of transparency<\/strong><\/p>

Explainability is a core component of transparency, which feeds directly into trustworthy AI. This is the view of Supradeep Appikonda, COO and co-founder of RegTech 4CRisk.ai<\/a>.<\/p>

Appikonda begins by emphasising how explainability strengthens day-to-day trust in AI-driven compliance work. As he puts it: \u201cExplainability builds trust by showing the user the steps, sources and assumptions used to generate a response. Users can verify, collaborate with others on results, and revise accordingly. This is \u2018Human in the Loop\u2019 \u2013 where feedback is vital to ensure AI-generated results are reliable, accurate and build trust. Reviews from other team members can be accelerated, since SMEs can see structured evidence for why something was mapped, and defend it.\u201d<\/p>

He adds that this clarity doesn\u2019t just help users \u2014 it reassures leadership too. \u201cExplainability also increases stakeholder confidence; for example, compliance officers can defend and justify outputs to other stakeholders and regulators. For instance, in a compliance mapping scenario, explainable AI can show why a specific policy or control was mapped to a regulation, and how strongly it was matched \u2014 whether the requirement was fully met, partially supported, or only contextually related.\u201d<\/p>

For him, the impact is as much about risk reduction as it is about efficiency. \u201cOverall, explainability reduces human bias and error by grounding each match in transparent reasoning rather than opaque judgment. It also improves model governance because the same explanations can be logged, versioned, and audited later. Without explainability, AI deployments risk rejection and ultimately, failure.\u201d<\/p>

Appikonda points out that the regulatory stakes are even higher in Europe, where GDPR\u2019s requirements make explainability non-negotiable. \u201cSince GDPR Recital 71 and Article 22 grant individuals the right to request human intervention, a review of the decision, and an explanation, in plain language, of the rationale behind an AI outcome or decisions that produce legal or significant effects.<\/p>

\u201cThis means how an algorithm reached its decision \u2014 not by revealing AI model code, but by describing the main factors and rationale behind the outcome. Explainability is table stakes for companies that must be able to provide \u2018meaningful information\u2019 about the \u2018logic involved\u2019 and the significant factors and outcomes of the processing. That means context, sources, steps and assumptions, and more if required to clarify AI reasoning.\u201d<\/p>

This is why he frames explainable AI as central to compliance, not just a supporting feature. \u201cExplainable AI helps meet GDPR\u2019s right-to-explanation obligation by generating clear, interpretable explanations that regulators and affected individuals can understand and trust.\u201d<\/p>

Auditors, he notes, particularly benefit from this level of transparency. \u201cExplainable AI helps auditors by being precise and specific on how outputs are derived and providing evidence and context to back up ratings and conclusions. For example, AI can show the policy and procedure that is violated with transactions and provide some details on the severity and consequences. The auditor, however, will always be needed to provide oversight and judgment since AI may not be able see beyond a specific set of transactions. This kind of analysis by the auditor is particularly necessary when a finding is logged, and the business, third parties or regulators need to dive more deeply into potential consequences, such as fines or MOUs.\u201d<\/p>

Appikonda stresses that the technology used under the hood matters just as much as the explanations produced. \u201cOne fundamental risk to consider: RegTech tools leveraging private, secure, small language models that are trained on a current, accurate and specialized risk and compliance corpus will be more trusted and, by design, minimize both bias and hallucinations.<\/p>

\u201cThose that rely on large language models are less likely to be accurate, especially when mining for risks and vulnerabilities that are outdated or only arise in a specific context that is not covered by the LLM. In addition, human oversight and judgment are critical to catching subtle bias and will never be automated fully.\u201d<\/p>

Appikonda finished, \u201c There are limits to explainable AI when it comes to protecting proprietary models and algorithms.  The core principle is that transparency should be sufficient for the purpose \u2013 for example, a regulatory audit, or a user appeal, but not excessive enough to compromise trade secrets.  That means specific algorithms, how data is fine-tuned, or the training process, are confidential trade secrets and as such, should be disclosed only to regulators under protected circumstances.\u201d<\/p>

Critical enabler<\/strong><\/p>

As regulated industries accelerate AI adoption, explainability has emerged as a critical enabler of trust, transparency, and compliance, claims Chris Reed, head of product and technology at Wordwatch<\/a>.<\/p>

 <\/strong>He said,\u201d Explainable AI helps uncover how automated systems reach decisions, addressing mounting pressure from regulators like GDPR\u2019s \u201cright to explanation\u201d. Transparency is not optional, instead, it\u2019s key to ensuring compliance, mitigating model bias, and supporting regulatory audits.\u201d<\/p>

Reed added the equally important is understanding the end-to-end flow of data into and through AI systems. Without visibility into what data is captured, how its processed, and where it resides. Reed states, organisations are risking breaches of data lineage, retention and access obligations. \u201cMapping data flows is the bedrock of defensible AI governance,\u201d he said.<\/p>

Furthermore, the Wordwatch tech head stated that deploying small language models on-prem with no access requirement to interact with public-facing services further strengthens regulatory posture.<\/p>

He explained, \u201cThese models allow businesses to leverage AI while keeping sensitive communications and interaction data within their secure infrastructure, eliminating exposure to third-party clouds and reducing the likelihood of data leaks. On-prem AI models also ease regulator concerns over cross-border data transfers and uncontrolled inference risks.\u201d<\/p>

Through the combination of XAI with robust data governance and secure architectures, Reed stated that organisations can confidently modernise their compliance frameworks, reducing audit friction and balancing transparency with operational efficiency.<\/p>

A balancing act<\/strong><\/p>

For Baran Ozkan, CEO of Flagright<\/a>, explainability turns an automated decision from a black box into an auditable story.<\/p>

\u201cWhen a model can show which signals mattered, how they combined, and what alternatives would have changed the outcome, regulators and customers can see that the result was reasoned, not arbitrary,\u201d he remarked.<\/p>

That transparency, Ozkan claims, supports core duties under modern privacy laws, including the need to inform people about automated decisions, offer a meaningful way to contest them, and prove human oversight where required. It also shortens audits. \u201cIf every alert carries a reason code, feature attributions, the data lineage behind those features, and a clear control that was triggered, examiners spend less time chasing spreadsheets and more time validating outcomes,\u201d detailed Ozkan.<\/p>

However, for the Flagright head, the hardest part is balancing openness with the protection of proprietary models.<\/p>

\u201cThe practical approach is layered disclosure. Firms keep weights and architecture private, while exposing regulator\u2011grade artifacts such as reason codes, surrogate explanations that are faithful within a defined window, counterfactual examples, and signed decision logs. That gives supervisors what they need to test fairness and consistency without forcing full model handover,\u201d he stressed.<\/p>

The Flagright founder finished by stating his company designs for explainability by default, with every score shipping with human\u2011readable rationales, immutable evidence, and a simulator that shows how different facts would have changed the decision.<\/p>

He concluded, \u201cThe goal is simple: speed for operations, clarity for auditors, and recourse for customers.\u201d<\/p>

Keep up with all the latest RegTech news here<\/a><\/p>","protected":false},"excerpt":{"rendered":"

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