Since 2016 I have been developing COREDO as a partner for entrepreneurs for whom technology, finance and law form a single growth ecosystem. During this time the COREDO team has implemented dozens of projects in the EU, the UK, Singapore, Dubai, the Czech Republic, Slovakia, Cyprus and Estonia, registering legal entities, obtaining financial licences and building AML frameworks. Today I see a key challenge for those implementing algorithmic recommendations: legal liability for AI errors in finance is distributed among several participants and jurisdictions, and the rules are changing faster than IT teams’ roadmaps.
Regulatory map: what’s changing
AI regulation‑advisors in the EU has become systemic: The European AI Act, MiFID II, DORA and ESMA/EBA guidance letters shape requirements for explainability, operational resilience and model risk management. In practice this means: any platform with automated investment recommendations falls under the “high‑risk” test; it needs model documentation, decision logs, model validation procedures and human‑in‑the‑loop for critical actions. COREDO’s practice confirms: where a client has implemented explainability and logging in advance, the risk of regulatory claims is significantly lower.
United Kingdom follows the principle «same risk, same regulation» through the FCA, emphasizing conflict of interest management, bias tests and documentation of model assumptions. In Estonia and Cyprus regulators apply MiFID II and, in places, local clarifications for robo‑advice. In the Czech Republic and Slovakia central banks focus on operational risk and DORA approaches. COREDO’s team adapts licensing packages and internal policies taking these nuances into account so that the registration of AI financial advisors proceeds without legal gaps.
Who is responsible for the AI advisor’s decision
The commercial liability of the AI solution provider is contract‑based: warranties of operability, caps on losses, exclusion of indirect damages and indemnity for IP claims and data breach. Product liability (product liability), however, can arise outside the contract if a software defect is proven. In contracts we record the allocation: manufacturer’s fault vs user’s fault in an AI error linked to zones of control, data, parameters, environment, updates.
The human‑in‑the‑loop (human‑in‑the‑loop) and the legal consequences come down to the question: whose action triggered the loss. If the interface explicitly required a human to confirm the investment advice, and the confirmation was given without verification, liability shifts to the person who made the decision. Where the system executes the advice automatically, the regulator expects enhanced measures of explainability, alerting and risk limits.
The rights and duties of depositaries regarding AI advice in funds (UCITS/AIFMD) remain classic: safekeeping of assets and oversight of compliance with the investment mandate. If AI leads to deviations from limits, the depositary must signal and block the breach, otherwise joint liability with the manager arises.
Contractual architecture: risks upfront
Contractual liability when implementing an AI adviser is not a single clause, but a system. I consider four blocks to be fundamental: limitation of liability and contract disclaimers for AI (liability cap, exclusion of indirect/ consequential damages, warranty disclaimers), a contract for AI customization and risk allocation (transfer of liability for changes), vendor management and legal liability of contractors (flow‑down of obligations), as well as SLAs and KPIs for AI services.
In SLAs we include metrics not only for uptime but also model performance: tracking error, drawdown thresholds, training details (data freshness SLAs), explainability latency and time for human review. COREDO’s practice confirms: such KPIs help demonstrate Due Diligence to the regulator and structure incident-response procedures.
Contracts for AI customization and risk allocation take into account the use of open‑source and pretrained models (transfer learning). If an open‑source component causes a licensing conflict or a vulnerability, the vendor must provide indemnity and an obligation for prompt remediation. For clients with an international footprint we add a prohibition on unauthorized transfer learning on client data and specify rights to model artifacts.
Automation of AML and compliance
Liability for AML violations in AI recommendations most often arises in automated KYC, transaction monitoring and sanctions screening workflows. EU regulators rely on AMLD frameworks, in Asia: on comparable acts and central bank guidance; in some African markets, less formalized, but local risks are high due to poor-quality lists and limited data sources. The COREDO team builds data quality controls and escalation processes so that garbage-in garbage-out does not become the cause of a fine.
Model risk management: documentation
Model validation (model validation) and related legal protection go hand in hand. We build three lines of defense: development with unit‑ and integration‑tests, independent validation (backtesting, stress, calibration) and a model committee audit. Model risk metrics include VAR tests, evaluation of performance drift and probability calibration for credit and market models. Such a framework provides causation (causation) in your favor when forensic ML is required.
regulatory requirements for AI explainability (explainability) vary, but the trend is fixed: document features, limitations, applicability and counterfactual explanations (counterfactual analysis). In investment recommendations local regulators require a clear rationale, even if the internal model is a complex ensemble. A solution developed at COREDO records the decision path and confidence score, which reduces disputes about foreseeability and the limits of liability for unforeseen advice.
Technical auditability: logging, an audit trail and decision replication are part of our mandatory setup. We recommend immutable logs, versioning of models and datasets, artifact hashing and time-stamping. This creates evidentiary proof of actions during an incident and helps distinguish a software defect from incorrect data interpretation.
Testing for adversarial attacks and legal security obligations come to the forefront: data poisoning, prompt injection in generative components and bypasses of restrictions. We combine ISO 27001 requirements, role‑based access control, separation of duties (Dev/ML/SOC) and signed approvals for deployment. Our experience at COREDO has shown: formal change‑management logs often resolve a dispute about blame long before court.
Data governance covers provenance, lineage, consent and retention, including confidentiality and cross-border transfer of personal data (GDPR‑like regimes). For open banking and API connections to AI advisors, PSD2/OB framework restrictions apply: customer consent, channel security and clear allocation of responsibility between the TPP, the bank and the platform.
Legal consequences of incidents
Direct damage and lost profits from errors of an AI adviser are assessed using damages methodologies that take into account VAR, drawdown, tracking error and the market environment. The rigor of the evidentiary base requires establishing causation: without forensic ML and counterfactual analysis, showing that the algorithm specifically caused the loss is difficult. We prepare clients for this in advance: model cards, data versions and replication of experiments.
Public reporting and disclosure of AI use to investors are gradually becoming a market standard. In several COREDO projects we prepared sections of AI ethics policy where we documented good faith, absence of discrimination and explainability — this reduced reputational risk in incidents.
Insurance and financial guarantees
risk insurance of AI errors (AI liability insurance) complements professional indemnity and cyber insurance (cyber). Insurers look at the maturity of model risk governance, the presence of human‑in‑the‑loop, logs and regular validations. I advise drafting insurance clauses with requirements for notification, the right of recourse and coordination of dispute resolution.
Allocation of responsibility in COREDO cases
Practical case: liability in the event of an incorrect liquidity forecast. A platform in the EU issued a rebalancing recommendation without taking local clearing windows into account; a temporary liquidity shortfall occurred. The COREDO team conducted forensic ML, proved model drift due to an outdated feed and initiated a review of the SLA with the data provider. Responsibility was split: the feed provider compensated direct losses up to the cap, the asset manager assumed operational costs and revised the human override.
AML case: an automated KYC missed a client’s sanctions indicator in Asia. During the root cause analysis we identified data poisoning; an external database had applied the wrong tag. The solution developed at COREDO included immutable logs and alert corridors, so the regulator assessed the due diligence positively. Compensation was limited to administrative measures, and the data vendor accepted indemnity for the error.
Model drift in a new market: scaling to Dubai led to an increase in suitability errors. We insisted on a staged rollout, a control period with human-in-the-loop and limits on automatic execution. After three weeks the metrics stabilized; this illustrates the cost-benefit analysis of implementing human-in-the-loop to reduce liability.
Registration of an AI advisor and Licensing: in Singapore the client obtained a license with COREDO’s support, embedding algorithm transparency rules, vendor audits and explainability procedures. In the EU a similar service is structured under MiFID II with a focus on suitability and DORA controls; for Estonia we prepared local policies and reports for the FSA.
From idea to sustainable practice
Due diligence when implementing AI:
- Regulatory map: AI Act, MiFID II, DORA, GDPR‑like regimes, MAS, SFC.
- Assessment of legal risks of using AI for asset management: licenses, limits of automation, open banking/APIs.
- Vendor due diligence: certificates, SOC reports, incident history, bias policy.
- Contractual architecture: caps, indemnities, warranty disclaimers, arbitration clauses, choice of law.
Design of corporate AI governance:
- Model committee, independent validation, periodic review, model cards.
- Logging, versioning, immutable audit trail, blockchain‑stamps.
- Access control: RBAC, segregation of duties, role of SOC/DevOps.
- AI ethics policies, conflict of interest management and public disclosure.
Contract templates and negotiation position:
- SLA and KPIs: uptime, drift, explainability, latency, human review.
- Contractual mechanisms: transfer of liability and vendor indemnification, flow‑down to subcontractors.
- Limitation of liability: caps, exclusion of lost profits, carve‑outs for intent and data breaches.
- International agreements and choice of jurisdiction; arbitration clauses and force majeure in case of AI service failures.
ROI and reducing litigation risks:
- Metrics of error impact: VAR, drawdown, tracking error in risk team’s KPIs.
- Continuous validation, drift monitoring and explainability as savings on future claims.
- Human‑in‑the‑loop at critical thresholds: cost‑benefit compared to liability exposure.
- Insurance solutions: proper alignment of professional indemnity, cyber and AI liability.
Specific issues people forget
Responsibility for bias and discrimination in AI advice is not only an ethical concern but also a legal risk. Regulators expect bias tests, data adjustments and documentation of fairness metrics. In one project the COREDO team implemented regular bias audits as part of the SLA with the vendor.
Liability when using open models (open‑source) in an advisor: a high‑risk area. The legal frameworks of product liability applicable to AI-powered financial software are increasingly debated in the EU; a prudent strategy is to clearly separate “as-is” components and your integration guarantee.
The impact of local Asian legislation on cross-border AI solutions manifests in data localization requirements, periodic audits, and additional consents. Here COREDO helps choose a group policy structure that withstands both GDPR-like regimes and Asian rules.
The role of the corporate lawyer
Technical auditability and tools for Forensic ML constitute a pre‑prepared platform for defense. We recommend assembling a set of assumptions, versions, test cases, and counterfactual scenarios suitable for legally admissible examinations of models. This approach makes it possible not only to win disputes, but also to learn from incidents.
What to do today: checklist
- Conduct a gap‑analysis against the AI Act, MiFID II, DORA, MAS/SFC and local AML acts.
- Formalize model risk governance: committee, validation, drift monitoring, explainability.
- Re-check contracts: caps, indemnities, warranty disclaimers, SLAs for model metrics, arbitration and choice of law.
- Configure immutable logs, role‑based access control, segregation of duties and incident-response procedures with notifications.
- Review insurance coverage: AI liability insurance, professional indemnity and cyber with coordinated terms.
- Update public disclosures on AI use so customer expectations align with reality.
Conclusions
Intelligent advisors are transforming the financial industry, but with opportunities come legal and operational obligations. Platform liability for algorithmic recommendations, the management company’s liability for automated advice, and contractual liability when implementing an AI consultant are manageable categories of risk if the process and contract architecture are set up correctly.
If you are preparing to enter new markets in the EU, the UK, Singapore, Dubai, Cyprus, Estonia, the Czech Republic or Slovakia, or building a financial AI service with international liability: let’s discuss a practical roadmap. I am responsible for ensuring that every line of code and every contract clause work towards your resilience and predictability of outcomes, and COREDO’s practice confirms: it is achievable.