Financial Services
Data strategy is non-negotiable and should be developed as early as possible.
The more you plan to use AI, the more access to high-quality data becomes important. Without high-quality data, automation is almost impossible.
The challenge
Fraud detection has never been more important for Australian insurance companies as both the volume and sophistication of cases rise.
Fraud in motor vehicle and property claims specifically is a significant cost driver, with individuals and criminal syndicates trying to exploit loopholes and reusing images to substantiate false claims. Investigation shows that 10-15% of claims could be fraudulent, and a single syndicate can represent risk exposure of up to $20 million.
Escient is collaborating with executives in an Australian insurance company to achieve a step-change in fraud management, enhancing their capabilities to protect their business and their customers.
We are leading a multi-year program to modernise claims processing for fraud control, replacing outdated systems and introducing smarter, data-driven processes that leverage AI so that fraudulent claims can be detected more quickly and comprehensively.
The approach
Escient first led the discovery and assessment phase of the program, identifying pain points through stakeholder engagement and evaluating technology options to inform a Design Proposal for the client. An integrated case management and analytics platform was chosen to replace fragmented legacy systems.
We then delivered a detailed Fraud Data Strategy, which is a critical element for any business seeking to adopt AI capabilities with confidence. The strategy included:
- Business data requirements to achieve quality results and future state reporting
- Prioritised value-adding data sources and acquisition patterns
- Delivery roadmap and migration plan for cloud adoption
- Data governance and operating model
- Resourcing requirements for delivery and operations.
Now, we are collaborating with our client to incorporate AI agents into the platform, enabling a state-of-the-art fraud detection capability that can be strengthened further over time.
AI is a game-changer for what we are trying to achieve. The goal is to make investigations as efficient as possible and optimise detection.
— Victor de Blecourt
Escient Program Lead
The outcome
Claims and policy data have been consolidated in a cloud-based system, with the capacity to incorporate broader enterprise data in line with strategy. Legacy fraud rules have been streamlined and combined with machine learning models to interrogate unstructured data, triage priority cases and make recommendations to inform further investigation.
An image processing service using Google AI will go live in early 2026 to detect reused or manipulated images that have been supplied as proof of loss. This is expected to significantly improve fraud detection by automatically flagging claims that include reused or altered images, using duplicate checks and metadata analysis to identify discrepancies.
FY27 will focus on scaling AI automation, embedding agentic workflows, and expanding integrations. The ambition is to eliminate manual triage, automate compliance steps, and enable fraud intelligence. This will help our client reduce their costs, increasing their capacity to keep premiums affordable for honest policyholders.