AI Vs. fraudsters
Editor’s note: Siddish Reddy is the founding AI engineer of Metaforms.
What if your qualitative survey responses are too good to be true? Open-ended questions have always intimidated both market researchers and survey respondents. To cut corners and make things far more difficult to detect, historically valid survey panelists are turning to AI to complete screening questionnaires and qualitative surveys.
Marketing research agencies are witnessing a rise in AI-assisted fraudulent survey responses and ghost completes. Automated bots facilitate fraudulent responses leading to significant quality issues and financial repercussions for research projects. According to McKinsey, the fraud landscape has evolved with the development of new technologies, leading to more advanced and complex fraudulent activities. Additionally, Sumsub reported that identity fraud in various industries has nearly doubled from 2021 to 2023, with a notable increase in sophisticated fraud schemes.
You might wonder, why not return to our good old traditional screening methods? Back when qualitative researchers connected with potential panelists over a phone call, in-person meeting or a Zoom call to interview and recruit the right survey participants. Yes, everything was done manually, but at what cost? And, did it result in zero fraudulent practices back then? Definitely not.
Adopting AI-powered tools
Recent advancements in AI-powered qualitative research tools have shown significant improvements in data quality and efficiency. They capture more content and provide richer context compared to traditional methods, allowing researchers to understand participants' emotions and sentiments more effectively. Additionally, qualitative interviews using AI often mitigate biases and provide a more objective analysis of the data.
Conversational AI has surpassed its scope and application in the field of market research, especially in qualitative interviews. It leverages varied AI capabilities to minimize fraudulent survey responses and optimize data quality. For instance, dynamic voice agents employ robust AI-driven, anti-fraud security systems that include a comprehensive multi-stage interview screening including pre-screening and rescreening to maintain data quality and data integrity in qualitative market research.
Using AI for real-time participant verification
Conversational AI verifies the identity of participants in real-time, reducing the chances of impersonation or multiple entries from the same individual. For instance, voice recognition and biometric verification as part of the panel recruitment screening will ensure that the participant is who they claim to be.
Transcription and analysis
AI voice interview agents transcribe and analyze data in real time, enhancing accuracy and speeding up the analysis process. They instantly analyze hours of voice interviews with large volumes of data to identify patterns, trends, key themes and to generate meaningful insights. Though qualitative interviews are all about diving into contextual conversations and less about scalability, AI makes it possible for market researchers to make the most of their time, budget and resources.
Cross-verification
AI voice interviewers cross-reference responses with existing data to ensure consistency and accuracy. Advanced machine learning models analyze behavioral patterns and detect anomalies, which indicate unusual or suspicious activities based on discrepancies between provided information and known data points to help identify fraudulent activities.
Implementing a future-proof fraud detection system involves data collection from multiple pre-validated sources, data preprocessing with built-in feature engineering capabilities, model training and evaluation based on specific qualitative analysis metrics as part of the conversational AI-powered voice interviews.
Adaptive questioning
Conversational AI-powered voice agents dynamically adjust the interview process based on real-time analysis, asking follow-up questions that are difficult for fraudulent participants to answer convincingly. This adaptive approach ensures that responses remain relevant and coherent, thereby exposing inconsistencies that are typical in fraudulent behavior.
Ipsos underscores that generative AI enhances data quality and accuracy in qualitative research by asking well-crafted, adaptive questions which are crucial for capturing nuanced feedback and identifying discrepancies indicative of fraud. It significantly improves the reliability of qualitative interviews, ensuring that the data collected is both accurate and representative.
Ultimately, the process of implementing conversational AI in panel recruitment does not end with deployment. It demands continuous monitoring and improvement on the go. Establish a robust analytics framework to monitor and understand how your conversational AI agent is performing, identify areas for improvement and gather user feedback.
Check for system errors or glitches, monitor user satisfaction levels and understand the impact on recruitment outcomes. This data-driven approach will enable you to continuously refine your conversational AI systems, enhance user satisfaction and optimize recruitment outcomes.
Conversational AI has the potential to revolutionize your qualitative panel recruitment operations. However, its success lies in strategic implementation, effective design, bias mitigation and continuous improvement. So, embrace conversational AI, but do so with the right approach and best practices.