THE DATA MOAT

Data Quality and Fraud Management is a complex topic. And, to address any complex problem, we need to build a complex system.

In many cases, ‘Data Quality’ and ‘Fraud’ are different problems – ironically, many fraudsters give good responses. They know they are being ‘watched’ and they want to collect rewards.

Poor Data Quality, on the other hand, might result and manifest itself in poorer responses, even if the respondent is not fraudulent and is valid for that survey – but fatigue, frustration or disinterest changes their desire.

Most companies try to patch fraud and data quality issues at a single point in the process, but the problem is sophisticated and varied and researchers, panel companies, etc. need to look at the end to end.

To that end, Rep Data has been making investments that attempt to “close the loop” from end to end. Defender looks at the ‘upfront’ aspects, Desk at the intermediate interactions during screening, and ReDem closes the loop on poor or tired in survey answers.

• Defender         – Upfront fraud prevention. Defender controls who enters the ecosystem. This includes checking on advanced identity resolution, device intelligence, behavioral fingerprinting, and anomaly detection prevent bad actors before they scale.
• Desk                   – In-field monitoring and control. This part of the process controls what happens during participation, including real-time behavioral monitoring detecting fatigue, acceleration, and instability as they emerge. Adaptive controls intervene before quality degrades.
• ReDem             – Downstream validation. Finally, this part controls what leaves the system and what is delivered to the client. This includes semantic modeling, engagement scoring, and pattern recognition, validate response integrity and feed intelligence back upstream.

 Individually, these are safeguards. Together, they are a compounding system and the effect is a controlled ecosystem - upstream, in process, and downstream. This effectively provides a list of advantages - stronger fraud detection through layered signal correlation, higher data quality via real-time engagement management, and faster issue resolution enabled by our services team and DIY delivery.

 Our goal is to invest in each of these pieces and fit them together seamlessly, and bring AI in the form of LLMs and ML into play to complete the circle.

Owning the full stack creates a moat that enables us to protect and keep out the fraud and manage to a higher level of Data Quality.