Redefining Risk Management: Strategies for a Safer Future

Nhu Nguyen, Risk Data Analysis Manager, Pnj Group

Nhu Nguyen, Risk Data Analysis Manager, Pnj Group

Nguyen’s Role and Responsibilities

As a risk data analysis manager at PNJ Group, Vietnam’s leading jeweler, I bring my e-commerce background and data-driven expertise to assess risks and combat fraud across e-commerce, fintech, and operations. Leveraging the analytical skills honed in e-commerce, I focus on detecting fraudulent activities early, streamlining processes and reducing human error to minimize losses effectively.

Assessing the Effectiveness of Current Technologies

Whatever technologies we look to take on board must be able to deal with data in real-time to minimize the lag between fraud occurrence and detection. It should also have high accuracy and provide deep insight for improved forecasting at higher compliance levels. One key challenge during the evaluation process is accurately forecasting, in dollar terms, the positive impact of new technologies and balancing that with the long-term cost of adoption.

Proactive and Impactful Fraud Detection

Currently, we are working on a project that will define a compliance strength index (CSI) and risk control index (RCI) to oversee compliance at a departmental level within the organization.

We believe the indices will be a useful metric to help increase the awareness within each individual department of the need to work together to prevent, reduce and control risk for the company.

Improved Risk Management within an e-Commerce Environment

Previously, we were faced with an emerging complex fraud trend that had the potential to significantly impact the company’s bottom line. Working with several departments, we rolled out a series of measures including new SOPs and fraud rules, as well as fraud tracking and technical investments to mitigate the risks arising from this within six months.

Frameworks to Assess the Strength of Data Protection Technologies

We look at several frameworks. First, information sensitivity—data is shared with relevant and necessary parties only. Second, threat modeling—to detect and prevent phishing, malware, insider threats and data leakage. Third, regulatory compliance—to ensure alignment with legal mandates. And lastly, security controls such as multi-factor authentication, role base access control, third-party auditing and the like.

Hands-on experience is key. Nothing beats working with actual data sets, so get as much of that experience as possible

Advancing Fraud Detection and Risk Management

AI is a game-changer, especially when it comes to detecting fraud trends like account takeovers and deepfakes. However, AI-derived risk insight is, at the moment, still limited. More training - both by the person using the AI and the AI itself - is needed to bridge that capability gap, which remains significant.

Career Advice for Emerging Professionals

First, hands-on experience is key. Nothing beats working with actual data sets, so get as much of that experience as possible. Second, hone your critical thinking skills. This will allow you to approach problems differently and identify novel solutions. Third, don’t be afraid to ask for help. It’s always better than incurring additional costs due to delayed corrective action.