Innovator Series Q&A: Fang Yu, Co-Founder and CPO at DataVisor

Mitek sat down to speak with Fang Yu to gain her insights on the critical role of balancing friction in customer experiences for financial institutions.

Q. What's the challenge for financial institutions in terms of fraud detection and user experience?

Fang: Financial institutions face a constant battle against fraudsters who are always looking for new ways to exploit vulnerabilities. To protect their customers and their own assets, these institutions need to implement robust security measures, such as multi-factor authentication, transaction monitoring, and identity verification.

However, these measures can create friction for users, leading to frustration and potentially driving them away to competitors who offer a more seamless experience. The challenge lies in finding the right balance between security and convenience, ensuring that legitimate users are not unduly inconvenienced while still effectively detecting and preventing fraudulent activity.

Q. What is the proposed two-part solution?

Part 1: Intelligent Friction

Fang: Instead of applying blanket security measures to all users, intelligent friction can be applied by leveraging machine learning models to identify transactions that may be suspicious. This allows financial institutions to apply additional friction, such as requiring additional verification or authentication, only to those transactions that are deemed high-risk. This approach minimizes the impact on the majority of good users who can enjoy a smoother and more streamlined experience.

Machine learning models can analyze vast amounts of data, including transaction history, user behavior, and device information, to identify patterns and anomalies that may indicate fraudulent activity. By continuously learning and adapting, these models can become increasingly effective at identifying suspicious transactions, allowing financial institutions to apply friction more precisely and efficiently.

Part 2: User Feedback Loop

Fang: The second part of the solution emphasizes the importance of incorporating user feedback into the system. By allowing users to provide input on their experience, financial institutions can gain valuable insights into how their security measures are impacting legitimate users.

This feedback can be used to fine-tune the machine learning models, ensuring that they are not overly sensitive and flagging legitimate transactions as suspicious. It can also help identify areas where the user experience can be improved, such as simplifying the verification process or providing more transparent communication about why certain actions are required.

By creating a continuous feedback loop, financial institutions can build a system that is both secure and user-friendly, fostering trust and loyalty among their customers.

Q. Can you give an example of how user feedback might improve the system?

Fang: Imagine a scenario where a user frequently makes large transactions or recurring bill payments. Initially, the system might flag these transactions as unusual and require additional verification. However, if the user provides feedback indicating that these transactions are legitimate, the system can learn to recognize this pattern and avoid flagging them in the future. This not only saves the user time and frustration but also helps the system become more intelligent and efficient over time.

Another example could be a user traveling abroad and making transactions from a new location. The system might initially flag these transactions as potentially fraudulent. However, if the user has informed the financial institution about their travel plans in advance, or if they provide feedback indicating that the transactions are legitimate, the system can adjust its risk assessment and avoid unnecessary friction.

Q. What's the ultimate goal of this approach?

Fang: The ultimate goal is to create a seamless and secure experience for users while effectively mitigating fraud risks. By leveraging machine learning and incorporating user feedback, financial institutions can build a system that is adaptive, intelligent, and customer-centric.

This approach not only enhances security but also improves customer satisfaction and loyalty. By minimizing friction for good users and providing a smooth and intuitive experience, financial institutions can differentiate themselves from competitors and build a strong reputation for both security and customer service.

Furthermore, this approach can help financial institutions optimize their fraud detection efforts by focusing their resources on high-risk transactions and avoiding unnecessary friction for legitimate users. This can lead to cost savings and improved operational efficiency.

For more from DataVisor's Fang Yu, check out her Innovator Series bio page or LinkedIn

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