As GPTs are becoming an increasingly important channel for stakeholders to seek information about both companies and individuals, Dablam has developed new methodologies for protecting its clients interests within these models.
GPTs & KYC: GPT models are increasingly being integrated into customer due diligence. For many law firms and banks, GPT models are now becoming the first measure in gathering and analysing data from public records, online media, and other online databases to assess a client’s Adverse Media Coverage. This means that in the future, a client’s reputation in GPT models is likely to have a bigger impact on their social license to operate than their reputation in search results.
KYC & ‘Adverse Media Coverage’: ‘Adverse Media Coverage’ is typically the one of the primary Know Your Customer (KYC) checks in assessing the risk profile of a client. The summaries influencing this check are compiled from publicly available information, including news articles, blog posts, social media activity, and other online content, to help financial institutions and businesses understand the client’s business activities, associations, and potential reputational risks.
Dablam’s research has shown that these algorithms are highly influenced by the following factors:
Relevancy: Positive ‘topic-clusters’ are critical for a strong reputation in GPTs. A ‘topic cluster’ is a collection of references, relating to a subject matter, which GPTs can be trained to recognise in multiple pieces of content.
Dablam focuses on training these algorithm to prioritise positive topic clusters for its clients, through creating long form content on high domain authority websites and discussing these clusters with GPT4.
Recency: Due to concerns about GPTs displaying outdated or inaccurate information, GPT4+ results are likely to be updated frequently based on the latest information published. Dablam ensures that recent information is available about its clients by consistently creating and publishing new long-form content within its media partner network, it then focuses on training the algorithm to index this information through regularly asking GPT4 for new information about its clients.
Conclusion: The emergence of GPT models as a key factor in due diligence represents a paradigm shift in the assessment of client reputations and risk profiles. Dablam’s approach to influencing these models to prioritise positive content, while ensuring accuracy through up-to-date information, underscores a new chapter in digital reputation management. By focusing on the relevancy and recency of content, Dablam is not just adapting to this shift but also actively shaping the way GPTs influence the perception of its clients.
The impact of these models on a client’s social license to operate is poised to surpass traditional search engine results, marking a significant milestone in the intersection of artificial intelligence and corporate due diligence.
