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Safe GenAI Testing and Digital Transformation for Financial Services
Data Infusion helps banks and financial institutions streamline processes, strengthen data governance, and safely test GenAI solutions, without exposing real customer information.
Designed for banks, insurers, and financial institutions operating in highly regulated, customer-facing environments.
Financial services organisations face increasing pressure to deploy GenAI while maintaining trust, regulatory compliance, and reputational integrity. Data Infusion helps banks and financial institutions streamline processes, strengthen data governance, and safely test GenAI solutions, without exposing real customer information.
The Core Challenge for Financial Services & Banking
Many industries access to realistic, compliant data for GenAI testing is one of the biggest blockers facing financial services. While structured data can be extracted and anonymised with relative ease, the most valuable GenAI use cases depend on unstructured customer data such as customer complaints, correspondence, call transcripts, and documents.
Attempts to mask or anonymise unstructured data using rules-based or hybrid approaches consistently fail to preserve context, intent, and behavioural signals. Existing test data management tools were not designed for unstructured data at scale, leaving organisations unable to safely test GenAI models and agents without exposing themselves to regulatory, reputational, security, and financial risk. Most with unstructured customer data.
Why Common Approaches to Test Data Fail
When financial services organisations recognise that real data cannot be used safely for GenAI testing, they typically turn to one of six workarounds. Each appears reasonable. Each fail, for reasons that are worth understanding before they cost you a failed deployment.
The three data handling approaches; redaction, masking, and anonymisation, all start with real data and attempt to make it safe. They differ in how much privacy risk they eliminate, but none of them produce test data that reflects how real financial services communications actually read under real-world conditions.
The three DIY alternatives; AI-generated data, manually created staff datasets, and scraped public data, avoid real data entirely but produce something that does not behave like it. AI systems evaluated against these datasets are not evaluated against the language patterns, edge cases, and contextual signals that matter most.
Every common approach either starts with real data and attempts to make it safe or avoids real data and produces something that does not reflect reality. The result is AI systems that perform well in testing and fail in production.
Regulatory Reality for Financial Services
Financial institutions across Australia, the US, the UK, and Singapore are increasingly expected to demonstrate how GenAI systems are tested, validated, and governed. Regulators including ASIC, APRA, the FCA, and the MAS emphasise accountability, explainability, and risk management including how test data is sourced and controlled.
Using real customer data, even when masked, introduces unacceptable regulatory, reputational, and audit risk. Boards and executives must be able to confidently state that customer data is not being used to develop or test GenAI systems.
What Synthetic Unstructured Data Does Instead
Contains no real customer data
Preserves language realism and intent
Supports safe GenAI testing and automation
Enables measurable performance validation
Provides audit defensibility
Data Infusion Solutions
Client-Embedded Digital Solutions
Designed, built, and deployed within approved client environments to streamline processes, automate workflows, and strengthen data governance using Microsoft 365 and the Power Platform.
Regulatory Response & Evidence Management
Centralised, auditable management of regulatory obligations, submissions, supporting evidence, and correspondence within SharePoint and Power Platform. Enables rapid response to ASIC/APRA requests with full lineage and version control.
Risk & Compliance Control Frameworks
Design and implementation of control libraries, attestations, exception tracking, and assurance workflows embedded directly into client environments. Provides real-time visibility into control effectiveness and operational risk posture.
Customer Complaint & Case Management
Secure, structured workflows for managing customer complaints, investigations, remediation actions, and regulatory timeframes. Supports consistent handling, escalation, and reporting aligned to regulatory expectations.
Policy, Procedure & Obligation Mapping
Digitised policy management with traceability between policies, controls, risks, and regulatory obligations. Reduces manual effort and improves governance clarity during audits and reviews.
Operational Workflow Automation
Automation of high-volume, compliance-sensitive processes such as approvals, reconciliations, incident handling, and internal reviews leveraging Microsoft's M365 and Power Platform ecosystem reducing manual risk and cycle time.
Reporting & Insights
Role-based dashboards that surface operational insights, track performance, and provide visibility into risk exposure and compliance posture - built directly on top of your existing solution.
Secure Intranet & Collaboration Hubs
Governed intranet solutions enabling secure collaboration across business units, with controlled access to sensitive information and clear ownership of data and content.
Client-Embedded Use Cases:
Financial Services
Unstructured Synthetic Data Services
Unstructured synthetic data batches and subscriptions that enable safe GenAI testing and validation without exposing real customer, employee, or operational data. Data Infusion provides unstructured synthetic data through one-off datasets, or ongoing programme-based delivery, and a licensed platform offering.
These services enable organisations to safely test, validate, and monitor GenAI and automation initiatives without exposing real customer or employee data.
All datasets are fully synthetic, privacy-safe by design, and engineered to support auditability, performance measurement, and regulatory confidence across regulated environments.
What This Looks Like in Practice
A wealth management firm spent four months and two full-time salaries manually creating 800 synthetic test documents for an AI triage system, only to discover the manually created data bore little resemblance to how real clients write under financial distress.
Post-deployment, the system misclassified a significant proportion of hardship correspondence and failed to escalate complaints correctly, requiring the organisation to rebuild the solution and repeat evaluation before safe re-deployment.
Detailed Representative GenAI & Automation Use Cases in Financial Services & Banking
Use Case 1
Client-Embedded Use Cases:
Financial Services
Challenge
Financial services organisations receive high volumes of unstructured client correspondence including complaints, dispute narratives, hardship communications, and escalation requests. AI systems built to triage, classify, and route this correspondence must be tested against data that reflects the full range of how clients actually write, under financial pressure, in dispute with their provider, or in genuine hardship. This data contains sensitive personally identifiable information and is subject to strict obligations under the Privacy Act 1988 and ASIC and APRA regulatory frameworks. It cannot be safely used for AI testing.
Why Masking Fails
Masking removes client names and account references but strips the tonal and contextual signals that define genuine financial services correspondence, the difference between a standard enquiry and a complaint from a client in financial hardship, or one who is about to escalate to the regulator. AI systems tested on masked financial correspondence are not tested against the communication patterns that matter most for ASIC and APRA compliance.
Synthetic Data Approach
Construct fully synthetic client complaint and correspondence datasets that replicate:
Informal and emotionally varied complaint language across financial products and service categories
Financial hardship indicators and vulnerability signals embedded in unstructured client communications
Multi-turn escalation sequences with embedded urgency, regulatory, and remediation signals
High-value client communications requiring priority routing and adviser escalation
Edge cases including implicit complaints, misdirected correspondence, and cross-channel interactions
Executive Value
Cut the cost of complaint misrouting - catch classification failures in testing, not after deployment
Reduce regulatory and financial remediation exposure from hardship cases the AI was never tested to detect
Eliminate privacy and consent risk from using real client correspondence in AI testing
Defensible evaluation methodology for ASIC, APRA, and board-level risk committee review
Use Case 2
Financial Crime and AML Investigation Support (GenAI Testing)
Challenge
Anti-money laundering (AML) and financial crime detection AI systems require long-running, narrative-based data trails that rarely exist cleanly in production systems. These systems must be tested against realistic unstructured transaction narratives, investigation correspondence, and suspicious activity records that reflect genuine behavioural patterns across time. This data contains sensitive client information, legally privileged investigation records, and commercially sensitive intelligence that cannot be safely used for AI testing without significant legal and regulatory exposure.
Why Anonymisation Fails
Anonymisation aims to meet legal and regulatory privacy thresholds by transforming data, so individuals cannot be identified. In practice, for complex unstructured financial correspondence including complaint narratives, adviser communications and dispute records; reliable anonymisation is extremely difficult to achieve and even harder to validate. Context that appears benign in isolation can reintroduce identity when combined with other information. For financial services organisations under ASIC and APRA scrutiny, anonymisation is risk-managed, not risk-free.
Synthetic Data Approach
Construct fully synthetic AML and financial crime investigation datasets that replicate:
Transaction narratives and suspicious activity patterns across extended timeframes
Persona-based behavioural consistency across multiple transaction types and channels
Investigation correspondence and escalation sequences with embedded risk signals
Edge cases including structuring patterns, layering narratives, and complex multi-entity scenarios
Controlled variation in risk indicators to support model calibration and threshold testing
Executive Value
Reduce the risk of AML model failure in production - validate detection accuracy before deployment, not after
Eliminate legal and regulatory exposure from using real investigation records in AI testing
Improve detection of edge-case and novel typology patterns before they reach compliance teams
Defensible AI testing documentation for AUSTRAC, ASIC, and board-level governance review
These use cases are representative examples. Actual implementations are tailored to each organisation’s regulatory, operational, and risk context.
Compliance and Trust
For client-embedded solutions, Data Infusion works within approved client environments and governance controls. For synthetic data services, no real customer or operational data is accessed or stored. All datasets are fully synthetic and designed for auditability and regulatory confidence.
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