AI Agent Reliability Scoring for Singapore Financial Services

DBS, OCBC, UOB, and MAS-licensed fintechs need agent reliability scoring that satisfies supervisory expectations — not just internal metrics. A technical breakdown of all 12 dimensions.

TL;DR

• Singapore financial institutions are deploying AI agents across customer service, fraud detection, credit decisioning, KYC, and wealth management — and MAS supervisory expectations for the reliability of these agents are increasingly specific.
• Internal reliability metrics (uptime, accuracy rate, latency) are necessary but not sufficient for MAS supervision — they do not measure behavioral compliance, scope adherence, or safety under adversarial conditions.
• Armalo's 12-dimension composite trust score covers the full reliability surface: accuracy (14%), reliability (13%), safety (11%), self-audit/Metacal™ (9%), security (8%), bond (8%), latency (8%), scope honesty (7%), cost efficiency (7%), model compliance (5%), runtime compliance (5%), and harness stability (5%).
• The dimension weightings are calibrated to reflect the relative consequence of each failure mode in regulated financial services contexts.
• MAS-supervised entities should map each of the 12 dimensions to specific conduct and technology risk obligations before using the score as a compliance evidence artifact.

Why This Matters In Practice

Singapore's major financial institutions — DBS Bank, OCBC, UOB, Grab Financial Group, MariBank, Trust Bank — are among the most advanced AI adopters in the Asia-Pacific region. DBS has publicly committed to having more than 1,000 AI models in production. OCBC has deployed AI for credit risk, fraud, and customer service. UOB's TMRW digital bank runs AI at the core of its customer experience.

Beyond the banks, MAS-licensed payment service providers, capital markets intermediaries, and insurance companies are deploying AI agents for functions ranging from customer onboarding to claims processing to investment recommendation.

The reliability expectations MAS brings to these deployments are different from what an internal engineering team typically measures. MAS Technology Risk Management (TRM) Guidelines require: that AI systems operate within defined parameters, that material deviations are detected and remediated promptly, that technology risk appetite is explicitly set and measured, and that senior management has adequate oversight of AI system reliability.

"Adequate oversight" in MAS language means: quantitative evidence, not qualitative assurance. A reliability report that says "our fraud detection agent achieved 97.3% accuracy on our internal test set" does not meet MAS evidence standards because it does not address: adversarial robustness, scope honesty, behavioral drift, or the specific failure modes that create regulatory exposure in a financial services context.

This is the gap that multi-dimensional reliability scoring is designed to close.

Direct Definition

AI agent reliability scoring for Singapore financial services is the process of quantifying an agent's trustworthiness across all dimensions that matter for MAS supervision — including behavioral compliance, adversarial robustness, scope adherence, and safety — using independently verifiable evaluation methods that produce evidence credible to Singapore's supervisory expectations.

The key distinction from conventional ML model evaluation: reliability scoring covers behavioral compliance and regulatory alignment, not just predictive performance. An agent that is highly accurate but that can be manipulated into producing outputs outside its authorized scope is not reliable in the MAS sense of the term.

The 12 Dimensions: A Technical Breakdown for Financial Services

1. Accuracy (14% weight)

Accuracy measures how often the agent produces outputs that are factually correct, contextually appropriate, and aligned with the intended task objective. For financial services agents, accuracy has a narrow sense (is the information correct?) and a broader sense (did the agent's response serve the customer's legitimate need?).

For MAS-regulated contexts, accuracy evaluation must cover: hallucination rate (does the agent produce fabricated information about products, rates, or regulatory requirements?), task completion rate (does the agent successfully complete authorized tasks without unnecessary escalation or failure?), and contextual appropriateness (does the agent's response match the customer's stated need?).

Accuracy has the highest weight in the composite score because it is the foundation of customer trust and the most direct driver of regulatory complaints.

2. Reliability (13% weight)

Reliability in Armalo's scoring framework measures behavioral consistency — does the agent produce consistent outputs given similar inputs, across different times, under varying load conditions, and across the full distribution of customer interactions the agent will encounter?

For financial services, reliability is particularly critical for: fraud detection (an agent that flags the same pattern inconsistently creates both false negatives and false positives), credit decisioning (an agent that produces different assessments for substantively similar applications creates fair lending risk), and customer service (inconsistent advice creates complaint and redress risk).

Reliability evaluation samples across the full input distribution, not just representative cases. High reliability on a narrow test set that misses tail cases is not reliability in a MAS-satisfying sense.

3. Safety (11% weight)

Safety measures whether the agent avoids producing harmful, discriminatory, misleading, or inappropriate outputs — including in adversarial conditions where inputs are designed to induce unsafe behavior. For Singapore financial services, safety has direct regulatory relevance: MAS FEAT's Fairness and Ethics principles both depend on safety as an operational control.

Safety evaluation for financial services agents must cover: harmful content generation (does the agent produce discriminatory recommendations?), adversarial resilience (can the agent be prompted to produce unsafe outputs through carefully crafted inputs?), out-of-scope requests (does the agent handle requests outside its authorization correctly?), and vulnerable customer detection (does the agent apply appropriate care when indicators of financial vulnerability are present?).

Safety has the third-highest weight because safety failures in regulated financial services contexts are rarely recoverable without significant regulatory and reputational consequence.

4. Self-Audit / Metacal™ (9% weight)

Self-audit capability — measured by Armalo's proprietary Metacal™ evaluation — assesses whether the agent accurately represents its own confidence levels, limitations, and knowledge gaps. An agent that claims certainty when it is uncertain, or that fails to acknowledge when it is operating at the edge of its competence, creates a specific risk in financial advisory and customer service contexts.

For MAS purposes, self-audit directly relates to the transparency requirement of FEAT: agents must be capable of providing customers and supervisors with honest characterizations of their outputs, including acknowledging when they cannot reliably answer a question. An agent that confabulates rather than acknowledging uncertainty is a FEAT transparency violation by design.

5. Security (8% weight)

Security measures the agent's resistance to adversarial exploitation — prompt injection, jailbreaking, data exfiltration attempts, and credential manipulation. For financial services agents, security is not just a technical requirement; it is a customer protection obligation.

MAS TRM Guidelines require that technology controls protect customer data and prevent unauthorized access. For an AI agent, this means: the agent should not be exploitable to access data outside its authorized scope, should not be manipulated into executing transactions or disclosures it is not authorized to perform, and should maintain the confidentiality of system architecture and credentials even under adversarial prompting.

6. Bond (8% weight)

Bond measures whether the agent has made an economic commitment to its behavioral obligations — whether the entity deploying the agent has put real value at risk in proportion to the agent's operational authority. In financial services, bond-backed agents signal that the deploying organization has skin in the game: if the agent fails to meet its commitments, a pre-committed economic stake is forfeit.

This dimension is particularly relevant for financial institutions that deploy AI agents as counterparties in negotiated transactions, and for AI agent vendors whose agents operate in financial services contexts where performance guarantees need economic backing.

7. Latency (8% weight)

Latency measures the agent's response time consistency under realistic and peak load conditions. For financial services, latency is a customer experience issue but also a regulatory one — real-time fraud detection agents that degrade under load create operational risk, and customer-facing agents that become unresponsive during high-demand periods create conduct risk.

Armalo's latency scoring evaluates distribution statistics (P50, P95, P99), not just averages, because tail latency is where customer experience failures and operational risk concentrations occur.

8. Scope Honesty (7% weight)

Scope honesty measures whether the agent accurately represents the boundaries of its authorization and competence — whether it stays within its declared operational scope, acknowledges when requests exceed that scope, and refuses or escalates appropriately rather than attempting tasks it is not authorized to perform.

For MAS-regulated agents, scope honesty is a control against the risk of agents acquiring de facto authority beyond their formal authorization. An agent deployed for customer service that also offers unsolicited investment advice is a conduct violation even if the advice happens to be good — the agent exceeded its scope, and scope honesty scoring should detect it.

9. Cost Efficiency (7% weight)

Cost efficiency measures whether the agent achieves its objectives using computational resources proportionate to the task complexity — rather than invoking expensive LLM calls or external APIs unnecessarily. For financial institutions that deploy agents at scale, cost efficiency is both a financial performance metric and an operational risk indicator: agents that escalate unnecessarily to expensive resources may also be escalating unnecessarily to human reviewers, creating operational bottlenecks.

10. Model Compliance (5% weight)

Model compliance measures whether the agent operates within the acceptable use policies of the underlying LLM providers it uses. Financial services agents often operate across multiple model providers simultaneously. AUP violations by the underlying model can create both regulatory and contractual risk for the deploying institution.

This dimension is particularly relevant as LLM providers have begun issuing sector-specific AUP requirements for financial services use cases. Model compliance scoring tracks whether agents remain within these requirements as AUPs evolve.

11. Runtime Compliance (5% weight)

Runtime compliance measures whether the agent's infrastructure deployment meets security hardening, data isolation, and operational compliance requirements — particularly relevant for agents deployed in cloud or hybrid cloud environments where MAS Outsourcing Guidelines and TRM Guidelines set specific requirements for financial institutions.

12. Harness Stability (5% weight)

Harness stability measures whether the agent's evaluation results are consistent and reproducible across evaluation runs — a foundational property for trusting any other dimension score. An agent that produces wildly different scores across identical evaluation scenarios does not provide a reliable trust signal.

Mapping Dimensions to MAS Supervision Expectations

Setting Dimension-Level Thresholds for MAS-Supervised Deployments

The composite trust score provides an overall signal. For regulatory compliance purposes, dimension-level thresholds are more defensible:

• Customer-facing agents with advice or recommendation functions: Safety ≥ 80, Scope honesty ≥ 75, Self-audit ≥ 70 (these three directly address FEAT Ethics and Transparency)
• Fraud and risk detection agents: Accuracy ≥ 85, Reliability ≥ 80, Security ≥ 75 (failure in any of these three creates direct financial or regulatory loss)
• KYC and identity verification agents: Accuracy ≥ 90, Security ≥ 80, Scope honesty ≥ 75 (false negatives in KYC are a financial crime risk; scope violations are a PDPA risk)

These thresholds should be approved by the board or risk committee and documented as the institution's AI risk appetite for agent deployments. Trust scores that fall below threshold should trigger immediate review, not just logging.

Key Takeaways

1. Internal reliability metrics (uptime, accuracy on internal test sets) do not meet MAS supervisory evidence standards — they do not cover behavioral compliance, adversarial robustness, or scope adherence.
2. Armalo's 12-dimension composite trust score is designed to cover the full reliability surface that MAS supervision requires, with dimension weightings calibrated to reflect consequence in regulated financial services contexts.
3. The three dimensions most directly relevant to MAS FEAT compliance are safety (11%), self-audit/Metacal™ (9%), and scope honesty (7%) — all three should have dimension-level thresholds, not just a composite score minimum.
4. Reliability scoring must cover the full input distribution, not just representative cases — tail case behavior is where financial services regulatory failures concentrate.
5. Dimension-level thresholds, approved by the board as part of the institution's AI risk appetite, are the operational mechanism that transforms a trust score into a compliance control.

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Singapore financial institutions seeking to establish MAS-supervisory-grade AI agent reliability scoring programs can explore Armalo's 12-dimension evaluation framework and Trust Oracle at armalo.ai. The platform is designed for the specific evidence requirements of MAS-regulated agent deployments.