Here is a closer look at the panel “How Can Fintechs Accelerate AI Adoption?”
This panel brought together industry experts to discuss how fintechs can play a central role in democratizing AI within the financial sector. The speakers included Julien Van Quackebeke from All4test, Hala Najmeddine, AI PhD at Active Asset Allocation, Nicolas Bentolila from Ipepper, Nadia Khedrougui from Kheops, and Daniel Collignon, Senior Advisor at Selencia. Together, they explored the opportunities and challenges associated with Artificial Intelligence in an increasingly digital context.
Fintechs are startups that have stood out by leveraging two key factors: first, regulatory openness, which has allowed them to operate in areas once reserved for traditional players. They have also capitalized on technological advances such as Big Data, Robotic Process Automation (RPA), blockchain, APIs, biometrics, and, of course, artificial intelligence. These companies aim to simplify financial services by making them more efficient, secure, and cost-effective.
Why is AI Crucial for the Financial Sector?
As the speakers highlighted, AI naturally integrates into finance due to the needs for productivity, cost reduction, and customer service. It allows the processing of large amounts of data, improves real-time decision-making, and automates processes, providing more agility. Additionally, fintechs can contribute to its democratization across the industry.
The Challenges to AI Adoption
The panel began with introductions from the various speakers. Julien Van Quackebeke, founder of All4test, explained how his company specializes in software quality, using AI to automate test creation. For him, one of the main challenges lies in the reliability of AI systems themselves, specifically how to test AI to ensure it functions reliably.
Hala Najmeddine, AI PhD at Active Asset Allocation, shared her expertise in real-time asset portfolio management, considering AI-assisted market shifts. She emphasized the scarcity of AI specialists, a significant barrier to faster adoption of this technology.
Nicolas Bentolila from Ipepper discussed the technical hurdles in integrating AI into financial systems, such as the need for structured databases for AI to generate relevant results. Nadia Khedrougui from Kheops highlighted the importance of participative inclusion in AI models and their application in automated processes.
Daniel Collignon shared concrete examples of AI’s potential impact on life insurance and customer relationship management. He mentioned the challenge executives face in understanding and trusting AI, often seen as opaque technology.
Overcoming the Barriers to Wider Adoption
One of the main obstacles to AI adoption, as mentioned by Hala Najmeddine, is the talent shortage. Profiles capable of mastering AI, machine learning, and data modeling are rare, slowing the integration of this technology into financial processes. Julien Van Quackebeke added that another barrier is the very understanding of what AI is. For many executives, AI remains an abstract concept, associated with risks such as data loss or security breaches.
Daniel Collignon explained that some insurers still operate with outdated IT systems, complicating the adoption of modern technologies. This hinders the transition to AI solutions, and business leaders often don’t know where to start integrating this technology into their processes.
Nicolas Bentolila stressed that companies must still overcome the challenge of data reliability. For AI to be effective, it must be able to connect to reliable and structured databases. However, in the financial sector, data is often heterogeneous, making interconnection difficult and limiting AI effectiveness.
Opportunities Presented by AI in the Financial Sector
The automation of repetitive tasks is one area where AI can bring significant gains. This would allow employees to focus on higher-value tasks, thereby increasing their productivity. For example, in the case of insurance contract management, AI could systematically analyze client-submitted documents, reducing processing times.
Julien Van Quackebeke also emphasized how AI can improve the reliability of the software used by fintechs. By generating automated tests, fintechs can ensure their software works correctly and that the data it processes is accurate.
Hala Najmeddine highlighted the efficiency of AI algorithms in asset portfolio management, enabling financial advisors to optimize their decisions in real-time. This represents a considerable competitive advantage for companies able to integrate these technologies into their services.
Daniel Collignon mentioned the enormous potential of AI in fraud detection, a field where the speed and accuracy of algorithms can make a difference. Thanks to AI, insurers can detect suspicious behaviors early and take preventive measures.
Fintechs: Pioneers in AI Adoption
Fintechs have always been pioneers in adopting new technologies, and AI is no exception. They are agile enough to test and integrate AI solutions into their processes, allowing them to leverage this technology more quickly than traditional financial institutions. The panelists agreed that fintechs can pave the way for broader AI adoption in the sector by demonstrating how these technologies can be implemented effectively and profitably.
Nicolas Bentolila explained that at Ipepper, they use AI to analyze vast datasets to help companies make real-time, data-driven decisions. This is a crucial lever for companies looking to improve their responsiveness in financial markets.
The Future of AI Adoption in Fintechs
The future of AI adoption in fintechs looks promising, but many challenges remain. One point raised by Nadia Khedrougui of Kheops is the need to improve the transparency of AI models. Today, many algorithms are perceived as “black boxes,” which raises trust issues for users. It is essential to make these models more explainable to facilitate broader adoption.
Julien Van Quackebeke mentioned that one of the significant challenges is managing unpredictable events like economic or health crises, which can disrupt predictive models. Fintechs need to develop algorithms that can quickly adapt to unprecedented situations.
Conclusion
AI represents a significant opportunity for fintechs, but it requires a thoughtful and strategic approach to overcome technical and human challenges. Thanks to their agility and innovation capabilities, fintechs can play a central role in the widespread adoption of AI while shaping the future of financial services.
Interview conducted by Pascale Caron.
