In order to transform payment fraud detection by integrating quantum artificial intelligence (AI), AdvanThink and Quandela have established a strategic scientific relationship. This partnership combines Quandela’s state-of-the-art quantum computing capabilities with AdvanThink’s vast payment security experience to tackle the ever-increasing problems of real-time financial fraud.
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The Strategic Alliance: Enhanced Fraud Detection using Quantum AI
The goal of AdvanThink and Quandela’s alliance, which was revealed in mid-June 2025, is to develop the next generation of payment fraud detection.
Important facets and goals of this partnership include:
Creating a Proof-of-Concept: A proof of concept that illustrates the benefits of incorporating a Quantum Machine Learning algorithm into AdvanThink’s industrial processes is the first and main goal. This will evaluate the usefulness of quantum AI in a real-world context.
- Performance Improvement: The main objective is to ascertain whether quantum-enhanced models are capable of surpassing the capabilities of existing AI systems. This evaluation of performance will concentrate on important metrics like:
- Speed: Increasingly quick detecting.
- Accuracy: Guaranteeing more accurate fraud detection.
- Creating more sustainable solutions is the goal of energy efficiency.
- Building systems that are more resilient to changing fraud strategies is a way to increase resilience to sophisticated attacks.
- The partnership will especially make use of Quandela‘s quantum machine learning techniques, which are already in use. Originally created for credit risk assessment, these algorithms have demonstrated a high degree of promise for identifying payment fraud.
- Prompt Achievement and Preparedness: The first demonstrator has already been finished. This demonstrator fulfilled all requirements for a deployment-ready system by incorporating Quandela’s technology into an industrial-grade AdvanThink pipeline. “Full of promise for experts in the fight against fraud” is how one defines this early achievement.
- Resolving Industry Issues: One of the biggest operational, technological, and strategic issues is the identification of payment fraud in real time. Demand for quicker, more accurate, energy-efficient, and attack-resistant models is rising as fraud strategies continue to change. This collaboration is a direct response to these growing demands.
- The Partnership’s Basic Assumption: Quantum machine learning may provide significant performance improvements over traditional techniques. By analysing many data states at once, quantum computing may theoretically improve pattern recognition. Faster learning and more successful anomaly identification could result from this, which is important in high-throughput settings where every millisecond matters.
- Prospects for the Future: Quantum Readiness: According to both businesses, this collaboration marks the start of a larger initiative to integrate quantum-enhanced AI into financial security products. This project is part of a larger movement in cybersecurity and finance to investigate “quantum readiness” in advance of potential dangers and performance requirements rather than replacing current systems right away. Financial institutions are encouraged to begin considering this change immediately.
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Views on Leadership:
- AdvanThink CEO Brice Perdrix emphasised the business’s longstanding dedication to innovation, saying, “AdvanThink has placed technological innovation at the heart of its development strategy for 35 years.” He said that “Quantum AI holds great promise for fraud detection and represents a real innovation lever for building tomorrow’s payment security.” Additionally, he emphasised the instant success, saying, “We have already been successful in incorporating Quandela’s technology into an AdvanThink pipeline that has all the requirements for an industrial system that is prepared for deployment. For professionals in the battle against fraud, this first demonstrator holds great potential.
- Quandela’s co-founder and CEO, Niccolo Somaschi, described the adaptability of their quantum machine learning models as follows: “Quandela has created a quantum machine learning model to enhance credit risk assessment. Additionally, there is a lot of promise for this system to identify payment fraud. This model may be “integrated into an industrial workflow and compared with the best products available on the market” with the cooperation, he added.
All things considered, this collaboration between AdvanThink and Quandela is a big step towards utilising quantum computing to keep ahead of complex financial crimes. It promises quicker, more precise, and more robust fraud detection capabilities for payments in the future.
Leading Payment Fraud Detection Company AdvanThink
One of the top companies in Europe for detecting payment fraud is AdvanThink. Over its 35-year history, the company has continuously made technological innovation the focal point of its development plan. Its primary goal is to give financial institutions the ability to quickly identify and halt payment fraud.
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An important component of AdvanThink’s strategy is artificial intelligence. By continuously learning from and adjusting to new threats, its platform uses adaptive AI to find intricate fraud patterns across enormous datasets. Because of its long-standing dedication to AI, AdvanThink has established a solid reputation for accuracy and promptness while dealing with increasingly complex financial crimes. In order to guarantee ongoing learning and adaption against changing fraud strategies, the organisation has been creating strategic AI-based products for decades.
Quandela: Advancing Photonic Quantum Information Processing
Quandela, a French photonic quantum computing startup, leads this cutting-edge business. The company excels at quantum machine learning models. Developed for use in industrial settings, these models have demonstrated great promise in improving a number of activities, including credit risk assessment and most importantly fraud detection. The goal of Quandela’s algorithms is to perform better than traditional models while operating with efficiency.
