FinTech Interview with Yinglian Xie, CEO and Co-Founder of DataVisor

FTB News DeskJanuary 28, 202525 min

How integrated FRAML strategies and AI are redefining financial crime prevention, transforming how financial institutions combat threats in a digital world.

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Yinglian Xie, CEO and Co-Founder of DataVisor

A visionary leader and recognized expert in the fields of AI, machine learning, and big data security, Yinglian Xie is CEO and co-founder of DataVisor, the world’s leading AI-powered fraud and risk platform. As CEO, Xie spearheads the development and implementation of DataVisor’s award-winning solutions. The organization’s expertise and continuous innovation make DataVisor the trusted partner of choice for Fortune 500 companies and leading organizations worldwide. Prior to founding DataVisor, Xie worked in research at Microsoft focusing on advancing the security of online services with big data analytics and machine learning. Yinglian completed both her Ph.D. and post-doctoral work in computer science at Carnegie Mellon University, and currently holds over 20 patents.

To kick things off, can you share what motivated you to launch DataVisor and how your career led you to focus on the fintech space and financial crime prevention?
Prior to my role at DataVisor, I was at Microsoft for seven years where I saw firsthand the implications of fraud. Back then, different types of fraud were handled by different types of siloed fraud solutions. I strongly believed that we needed a better way to tackle the widespread and always changing fraud issues. This motivated me to go beyond solving for specific issues and come up with a more systemic approach to fighting fraud, and ultimately, what led me to found DataVisor with my co-founder Fang Yu in 2013. Today, DataVisor is the trusted partner of choice for Fortune 500 companies and leading organizations worldwide.

As financial institutions face ever-growing threats, how has the convergence of fraud detection and anti-money laundering through FRAML changed the landscape of financial crime prevention?
FRAML represents an evolution in financial crime prevention, combining the strengths of both fraud detection and AML to create a more resilient defence. The integrated approach enhances detection and prevention capabilities but also provides a more comprehensive understanding of financial crime patterns, which leads to better strategic responses. It’s changed the landscape by shifting to a proactive approach that enables fraud detection systems to refine their algorithms and improve accuracy.

From your perspective, what are the main advantages of combining fraud detection with anti-money laundering processes, and how does this integrated approach improve overall security for financial institutions?
A good FRAML approach offers numerous benefits, but ensuring accurate remediation paths is a key advantage of a true FRAML strategy. By integrating fraud and AML efforts, institutions can develop more precise and effective remediation plans. This integrated approach ensures that all aspects of financial crime are addressed, from detection to resolution, providing a comprehensive solution to mitigate risks. Accurate remediation not only protects the institution from financial losses but also strengthens its reputation and trustworthiness in the eyes of customers and regulators.

Overall, the power of FRAML lies in its ability to create a cohesive, data-driven, and technologically advanced framework that significantly enhances the effectiveness and efficiency of financial crime management.

In your experience, how is real-time data orchestration playing a role in financial crime prevention, and why is it becoming such a crucial component of FRAML?
Orchestrating fraud and AML data into one place in real-time breaks the silos that typically exist within FIs, allowing a complete picture of all events. The more data you have, the more informed and accurate your decision-making will be. By orchestrating all data into a FRAML system, fraud teams can benefit from the compliance team’s work and vice versa.

AML operations, for example, often focus on Know Your Customer (KYC) signals, which provide detailed information about customer identities and behaviors. Many of these KYC signals may not currently be utilized by fraud detection systems, but by orchestrating this data and making it available to fraud systems, institutions can better assess the risk associated with transactions like deposits or checks. This risk rating is invaluable for the fraud detection side, providing a more comprehensive view of potential threats.

Conversely, fraud detection systems generate a wealth of information, including data from two-factor authentication processes and transaction anomalies. Sharing this information with AML systems enhances their ability to detect and prevent money laundering activities. By having access to all transactional and authentication activities, AML operations can build a more complete profile of customer behavior, identifying suspicious patterns that might otherwise go unnoticed.

With AI and machine learning at the core of DataVisor’s offerings, could you walk us through how these technologies are shaping the future of fraud and AML strategies?
Fraudsters are constantly evolving their tactics and their attacks are becoming increasingly sophisticated, especially because they also have access to AI technology. Real-time payments are especially vulnerable to fraud because of their immediacy and the fact that they are irreversible. Manual fraud responses simply can’t keep up, creating a barrier for innovation especially among FIs that don’t have the resources or team to handle fraud growth as the company grows.

That’s where AI and machine learning come into play. The two most important features a fraud solution must have to stay ahead of emerging fraud are real-time data computation and the ability to ingest multiple data sources at scale. Both are impossible with manual fraud measures. Machine learning must be the backbone. Unsupervised machine learning allows companies to leverage a data-driven approach to stay ahead of new and emerging threats. It’s impossible to fight modern-day fraud without it.

The reduction of manual processes is a key benefit of FRAML. How is automating these tasks translating into greater cost efficiencies for financial institutions?
Reducing manual processes helps reduce many of the repetitive tasks that drain resources and frees analysts up to focus on investigations rather than manual administrative tasks. A unified system also reduces the need for multiple platforms, streamlining processes and minimizing maintenance costs. The consolidation of data and operations also allows for more efficient use of resources, reducing redundancy and improving overall operational efficiency. This approach not only saves money but also enhances the institution’s ability to respond to financial crime swiftly and effectively, especially when under a large attack. A FRAML approach can also reduce the number of vendors that need to be contracted with, which saves on cost and coordination. By minimizing the number of vendors, institutions can further benefit from reduced expenses and simplified management.

With tightening regulatory frameworks, how does FRAML help financial institutions stay compliant while also enhancing their ability to detect and prevent financial crimes?
With regulatory pressure intensifying, especially for sponsor banks, FRAML solutions enable compliance while empowering financial institutions to combat evolving fraud challenges. Fintech are diversifying their partnerships to manage risk, prioritizing banks with strong compliance resources. Meanwhile, sponsor banks are facing scrutiny to take a hands-on approach to these relationships, recognizing that outdated, fragmented systems and limited data integration leave them vulnerable to sophisticated, real-time attacks and severe penalties. A centralized, adaptive FRAML architecture that integrates data across all partners is critical, enabling rapid detection and streamlined compliance to meet these new regulatory demands.

What are the biggest challenges financial institutions face when it comes to fraud and money laundering, and how does DataVisor’s approach help them navigate these complex threats?
FIs face a number of challenges when it comes to fraud and money laundering. Among the biggest challenges, the increasing sophistication of fraudsters, growing reliance on digital transactions, and limited resources are likely to cause the largest impact. Fraudsters are constantly evolving their methods, making it hard for FIs to keep up—especially those with limited teams and resources. The world’s increasing reliance on digital transactions has also created new opportunities for fraudsters. These transactions can be more difficult to monitor and detect than traditional in-person transactions.

DataVisor leverages both supervised and unsupervised machine learning technology to give FIs optimal protection against both known and unknown threats in real-time, which allows our clients to capture attacks in milliseconds. Many organizations rely on fragmented solutions to fight fraud, which isn’t as effective as one holistic solution, like DataVisor. Our solution is also highly scalable, so companies from smaller fintechs to large enterprises can fight all forms of fraud through one sophisticated platform.

With the rise of digital banking, what emerging trends are you noticing in fraud and AML efforts, and how is DataVisor adapting to these shifts?
With the rise of digital banking, bad actors are increasingly adept at leveraging technology to their advantage, often using advanced tools to outmaneuver traditional defenses. In response, the financial industry is making a strong push to adopt the latest technologies, with one prime example being the use of AI to counter AI-driven fraud. Institutions are focused on integrating AI, including generative AI, to streamline operations, enhance detection, minimize manual workloads, tackle organizational silos, and drive down costs – ultimately staying competitive in a tech-driven landscape.

In the wake of rising scams and financial crimes, institutions also seek solutions that consolidate their data in addition to risk signals for a comprehensive view of each customer. This is where consortium-based data solutions are gaining traction; they provide predictive insights from shared intelligence, which strengthens prevention and detection through a more unified approach.

At DataVisor, we’re leading the way by continuously innovating and ensuring our clients have access to the latest technologies and tools. Our mission is to keep them a step ahead of fraudsters, equipping them with advanced solutions to stay resilient in an ever-evolving threat landscape.

As financial crime prevention continues to evolve, how do you envision the role of FRAML advancing in the next few years, and what’s next for DataVisor in this space?
As financial crime prevention continues to evolve, I see FRAML as a key driver in breaking down the traditional silos between fraud and AML. By orchestrating all data into a centralized intelligence hub, both fraud and compliance teams can have a comprehensive view of customer risk, empowering them to make more informed decisions. This integration also fosters an essential feedback loop between the teams, resulting in better detection of financial crimes.

AI and machine learning, widely adopted in fraud prevention but still underutilized in AML, will play a key role in a FRAML approach. These technologies will refine AML processes, enhance detection, reduce manual workload and costs, and provide explainable insights that meet compliance needs.

At DataVisor, our mission is to be at the forefront of this evolution. We aim to support fraud and compliance teams as they come together to share data and insights, building a fully customer-centric approach to financial crime prevention. By facilitating this collaboration, we enable institutions to achieve better outcomes in the fight against financial crime, helping create a safer and more trustworthy financial system.

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