Confidential Data Collaboration in Fintech
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Unlocking Unprecedented Insights: Confidential Data Collaboration in Fintech
An interview with Frederic Lebeau, Co-Founder & CEO at Datavillage
With the advent of Open Banking, Open Finance and Open Data, the need for secure and insightful data handling has never been more critical. This article explores the advances in technology that pioneers in the field are bringing about to unleash the true power of data analytics through cross silo collaboration, while preserving data secure and private.
The financial services industry has taken significant steps on its journey toward embracing openness and collaboration. Historically characterized as secretive, self-reliant, and inward looking, the sector is undergoing a transformation. This shift is largely attributed to progressive directives such as PSD2 in Europe, along with various global Open Banking initiatives. These regulatory changes have been central in nudging the industry towards a more open and collaborative spirit, marking an inflection from its traditionally closed off approach.
“Data is the new oil” we keep hearing, in reference to its potential to create new business opportunities everywhere, however valorizing it, remains a source of enduring challenges. On the one hand, the ability to share and analyze data externally opens vast opportunities for insights, driving innovations and fostering unparalleled growth in the sector. When multiple data sources converge, the insights gleaned are not just additive; they are transformative, offering a deeper understanding of market trends, customer behavior, and economic shifts. However, this opportunity does not come without its caveats. The main hurdle is highly sensitive issues of privacy and security. In a world where data breaches are not just a possibility but a frequent occurrence, ensuring the confidentiality and integrity of customer data during external sharing is paramount. This challenge is further compounded by stringent regulatory frameworks like GDPR in Europe, which place the onus of data protection squarely on the shoulders of financial institutions.
Belgium Fintech Magazine spoke to Frederic Lebeau, Datavillage’s visionary CEO to gain unique insights into the company’s proposition to turn businesses into insights: “Our core value proposition is about enabling businesses to capitalize on their sensitive data without jeopardizing confidentiality and privacy” says Lebeau. Leveraging advanced algorithms in confidential computing technology, Datavillage connects various data sources, ensuring data remains secure, and fully encrypted, even when in use. «Today we are the only ones on the market to allow the connection of any type of data source to what we call confidential environments in a completely transparent and multi-cloud manner.” adds Lebeau. This approach allows financial institutions and other organizations to share insights, not data, maintaining privacy and compliance.
Impact of Open Data in Financial Services
“The term ‘open data’ carries nuanced interpretations” explains Lebeau. Traditionally, open data refers to data that is readily accessible, available on public platforms for anyone to use. However, in the context of financial services and initiatives like PSD2, the concept of open data takes on a more complex meaning. It’s not merely about data availability but also about the accessibility of data owned by companies under specific conditions, such as stringent authentication and authorization protocols. This distinction is crucial as it underscores a shift from open data being a freely available resource to a regulated asset, accessible under controlled conditions. This regulatory perspective is especially pertinent in the financial sector, where compliance, privacy, and security play a pivotal role.
This approach opens great opportunities for creating new services and products, optimizing processes, and generating new revenue streams, all while adhering to stringent privacy and compliance standards.
At the bleeding edge of technology
Datavillage enables the creation and application of AI models in confidential environments. This means financial institutions can process transactional data, apply AI models for classification, and gain insights without ever accessing the raw data. “We provide a way to create AI models or run AI algorithms on these data sets while making sure that the data and the AI model remain confidential.” Shares Lebeau. Its application of confidential computing technology ensures data encryption in transit, at rest, and importantly, in use.
This technology is becoming a mainstream, scalable solution across major cloud platforms, offering a robust foundation for Datavillage’s services and for collaborative efforts like anti- money laundering, where multiple institutions can share and analyze data while remaining GDPR compliant.
Benefiting from Data Collaboration
The benefits of data collaboration are numerous. As new use cases continue to emerge, it is important for companies to recognize the potential advantages to their organizations from effectively valorizing their data assets.
“Data collaboration, as envisioned by Datavillage, goes beyond mere data monetization. It’s about valorization” states Lebeau, who identifies three distinct types of benefits:
Process Optimization: financial institutions are increasingly seeking to integrate external data sources, such as government databases or credit bureaus, into their operational processes. This integration of diverse data sets allows for a more comprehensive view, enabling these institutions to refine and enhance their processes, such as credit assessments and risk evaluations.
For example, by accessing a broader range of data through collaborative means, banks can optimize their credit scoring processes. This is achieved by incorporating more variables and data points, leading to more accurate and reliable credit assessments. Furthermore, this collaborative approach to data usage can streamline other operational processes by introducing efficiency and reducing redundancy. The incorporation of external data sources not only broadens the scope of analysis but also introduces new perspectives and insights, which can lead to more informed decision-making and operational improvements.New Revenue and Cost Reduction: By facilitating the sharing and utilization of external data, companies can innovate and develop new products and services that were previously unattainable within the confines of their individual data silos. For instance, banks can leverage shared data to offer personalized financial products or enhance existing services, thereby appealing to a broader customer base and generating new revenue opportunities. Additionally, this collaborative approach allows for the development of unique data-driven solutions, such as advanced fraud detection services or tailored investment strategies, which can be monetized. “There is only one percent of money launderer activity that is recovered... the main reason is because the financial system today is so fragmented» explains Lebeau.
Creation of New Business Models: One key aspect of this evolution is the emergence of data intermediaries, a concept gaining traction in the European market with the advent of the Data Governance Act. These intermediaries act as facilitators, enabling new interactions between data providers and consumers. This development is fostering business models where companies can valorize their data by making it available through these intermediaries to achieve collective goals.
For instance, financial institutions can collaborate with retailers to create more targeted and efficient consumer offerings, like advanced cashback programs based on specific purchasing behaviors, thereby enhancing customer engagement and loyalty. “One very interesting use case we see coming is about cashback between financial institutions and retailers. This involves mixing transaction data and receipt data together. By paying with your bank card, you get automated cashback on the product you buy at the retailer in an automated way. This not only generates more traffic in retail stores but also builds cross-retailer insights that are shared back to the retailer” shares Lebau.
As a result, organizations are moving towards a model of data collaboration where the value is derived not just from the data itself, but from the collective insights and solutions that emerge from this collaborative ecosystem.
Vision and Future Directions
“Datavillage aims to assist organizations in solving challenges that cannot be addressed within their data silos.” adds Lebeau, referring to one of the main hurdles that currently limit the true potential of data analytics. From collaborative anti-money laundering efforts to innovative retail banking solutions, Datavillage’s vision encompasses a broad spectrum of industries and applications. With a commitment to enabling organizations to overcome fragmentation in data handling and achieve greater synergies through confidential data collaboration, Datavillage is making cross organisational data collaboration a reality in an increasingly opened and transparent world.
By Chris Crespo / Nordic Fintech Magazine
Published and Distributed by FinTech Belgium asbl
Contact: Alessandra Guion - CEO