Values
Values
As financial institutions accelerate digital transformation, ethical AI has emerged as a critical priority; especially in high-stakes areas like credit scoring, fraud detection, and investment decisions.
As AI plays a bigger role in payments, expense management, and transaction monitoring, organisations face a critical challenge: how to ensure these systems make fair, transparent, and accountable decisions. This is where ethical AI becomes essential.
While artificial intelligence offers efficiency and predictive power, it also introduces risks around bias, opacity, and accountability.
With AI projected to contribute up to AUD $59 billion to Australia’s economy by 2035, organisations are under growing pressure to adopt AI responsibly.
Within today’s regulatory and customer-centric landscape, ethical AI extends beyond compliance requirements to serve as a key competitive differentiator.
AI adoption in financial services has grown rapidly, particularly in mature markets like the UK and Australia.
According to a report published by the Bank of England:
75% of UK financial firms are already using AI, with another 10% planning adoption in the next three years
55% of AI use cases involve automated decision-making, highlighting the increasing role of AI in influencing financial outcomes
However, 46% of firms report only a partial understanding of the AI systems they use
This gap makes ethical AI and explainability essential, especially in finance where trust and transparency are key.
found that organisations are increasingly prioritising fairness, accountability, transparency, explainability, and safety in AI systems.
Ethical AI refers to the development and deployment of AI systems that are:
Transparent and explainable
Fair and free from bias
Accountable and auditable
Secure and privacy-compliant
In financial services, ethical AI ensures that decisions – such as flagging suspicious payments, approving transactions, or automated expense auditing – are justifiable, traceable, and aligned with regulatory expectations.
UK regulators, for example, emphasise five key principles for AI governance: fairness, transparency, accountability, safety, and contestability.
Example:
For example, in expense management, an AI system may flag a travel claim as non-compliant. Without explainability, employees may not understand why it was rejected.
With ethical AI, the system can clearly indicate that the expense exceeded policy limits or lacked required documentation – improving transparency and reducing disputes.
Building Trust with Customers
Opaque “black-box” models can undermine customer confidence. According to the CFA Institute, lack of explainability can erode trust and create regulatory risks in finance.
When customers are denied credit or flagged for fraud, they increasingly expect:
Clear reasoning
Transparent processes
The ability to challenge decisions
Explainable AI (XAI) enables institutions to meet these expectations.
* Black box models are AI or machine learning systems where internal decision-making processes are hidden, showing only inputs and outputs. While highly accurate at identifying complex patterns—common in deep learning and neural networks—their lack of transparency makes it difficult to understand, trust, or audit the reasoning behind their predictions.
Meeting Regulatory Expectations
Financial regulators globally – including the UK, APRA (Australia), and ASIC – are placing growing emphasis on accountability in AI systems.
In the UK, regulators explicitly highlight “appropriate transparency and explainability” as a core principle of AI governance.
Without explainability:
Firms may struggle to demonstrate compliance
Audits become more complex
Legal and reputational risks increase
Reducing Bias and Ensuring Fair Outcomes
AI systems trained on historical data can unintentionally reinforce bias. Ethical AI frameworks require:
Bias detection and mitigation
Fairness testing across demographic groups
Continuous monitoring of model outcomes
Explainability helps identify why a model made a decision; making it easier to detect discriminatory patterns.
Strengthening Risk Management
AI is increasingly embedded in critical financial decisions, but risks remain:
Only 2% of AI use cases in UK finance are fully autonomous, reflecting caution in high-risk scenarios
78% of UK finance leaders express concerns about AI-related risks
Explainable AI allows institutions to:
Validate model outputs
Detect anomalies early
Maintain human oversight in critical decisions
While much of the data comes from the UK, similar trends are evident across Australia and New Zealand:
Strong regulatory scrutiny (APRA, ASIC) around model risk and governance
Increasing use of AI in lending, wealth management, and compliance
Growing demand for responsible AI frameworks in banking and fintech
In AUNZ markets, ethical AI is particularly important due to:
Strict consumer protection laws
Emphasis on fairness and responsible lending
High customer expectations for transparency
To operationalise ethical AI, financial institutions should focus on:
Model Transparency
Use interpretable models where possible
Apply explainability techniques (e.g., SHAP, feature importance)
Governance Frameworks
Define clear accountability for AI systems
Align with regulatory principles and internal risk policies
Human-in-the-Loop Decisioning
Combine AI automation with human oversight
Especially critical for high-impact decisions
Continuous Monitoring
Track model performance and fairness over time
Regularly audit AI systems for bias and drift
Third-Party Risk Management
Many firms rely on external AI providers
Ensure visibility into outsourced models and data sources
As Inlogik expands its use of AI, we are adhering to ethical AI best practice by applying the same principles that already underpin our platform approach: security, privacy, compliance, auditability, visibility, and operational control. Our focus is on using AI in ways that are transparent, governed, and appropriate for financial environments where trust, accountability, and data protection are essential.
In practice, this means Inlogik applies ethical AI best practice through clear governance and accountability, transparency and auditability in AI-assisted workflows, strong data protection and privacy controls, ongoing monitoring for quality and risk, and disciplined management of any third-party models, tools, or data sources involved. Importantly, we believe ethical AI should support human decision-making, not replace it, so we are adopting a human-in-the-loop approach across our AI capabilities to help ensure users remain in control of financial decisions at every stage.
Governance and accountability: Inlogik establishes clear internal accountability for AI-enabled capabilities, with defined governance, oversight, and approval processes for how AI is designed, deployed, and maintained.
Transparency and auditability: Inlogik prioritises AI-assisted workflows that can be understood, reviewed, and audited, supported by appropriate visibility, logging, reporting, and record-keeping.
Human oversight and control: Inlogik applies a human-in-the-loop approach across AI-enabled workflows so AI supports decision-making while appropriate users retain review, approval, and intervention capability. For example, if AI is used to recommend the most appropriate expense coding based on prior transactions and company policy, that recommendation is presented to the user for review, approval, or adjustment rather than applied autonomously.
Monitoring and exception management: Inlogik monitors AI-enabled processes for performance, quality, exceptions, and emerging risks so issues can be identified, investigated, and addressed promptly.
Security, privacy, and compliance: Inlogik applies AI within the same strong security, privacy, and compliance environment that underpins our platform, including disciplined data handling, access controls, and alignment with relevant regulatory and assurance obligations.
Third-party and ecosystem discipline: Where third-party models, tools, or data sources are involved, Inlogik applies appropriate due diligence, control, and oversight to help manage external dependencies responsibly.
This approach is designed to improve efficiency and consistency while maintaining transparency, accountability, and human oversight. As Inlogik continues to evolve its AI capabilities, our aim is to apply them in ways that are responsible, explainable, and aligned with the expectations of customers, regulators, and financial stakeholders.
Ethical AI in Payments and Expense Management
As AI becomes more embedded in day-to-day financial operations, its role in payments and expense management is growing rapidly.
From real-time transaction monitoring to automated policy enforcement, organisations are relying on AI to make fast, consistent decisions; making it essential that these decisions are fair, transparent, and easy to explain.
Detecting fraudulent transactions without unfairly blocking legitimate payments
Automatically flagging expense claims that violate company policy
Ensuring consistent approval decisions across employees and departments
Providing clear explanations for declined payments or rejected expenses
As AI becomes more embedded in financial ecosystems, the focus will shift from “Can we use AI?” to “Can we trust AI?”
As AI investment accelerates across APAC financial institutions, organisations that prioritise transparency and responsible AI governance will be better positioned to build long-term customer trust.
Key trends shaping the future include:
Increased regulatory clarity around AI governance
Greater demand for explainable, auditable models
Integration of ethical AI into enterprise risk frameworks
Competitive differentiation through responsible AI practices
Ethical AI is no longer optional in financial decision-making – it is foundational.
With widespread adoption of AI across financial sectors, institutions must prioritise:
Explainability
Transparency
Accountability
Those that invest in ethical AI today will not only meet regulatory expectations but also build long-term trust with customers and stakeholders.
What is ethical AI in finance?
Ethical AI in finance refers to the use of AI systems that are transparent, fair, and accountable, ensuring financial decisions are unbiased, explainable, and compliant with regulations.
Why is explainability important in AI?
Explainability is important because it helps organisations understand and justify AI-driven decisions, building trust, ensuring compliance, and making it easier to identify errors or bias.
How is ethical AI used in payments?
Ethical AI is used in payments to detect fraud, monitor transactions, and approve payments in a way that is fair, transparent, and does not unfairly block legitimate activity.
What are the risks of AI in financial services?
Key risks include bias in decision-making, lack of transparency, regulatory non-compliance, and over-reliance on automated systems without proper human oversight.
