This is especially true for financial institutions, which must accurately identify and verify the identities of their customers to prevent fraud, money laundering, and other criminal activities. In this context, federated search technology has become an integral part of efficient and comprehensive KYC screening processes.
But what exactly is federated search, and why is it so critical for KYC screening? In this article, smartKYC’s COO, Hugo Chamberlain, thoroughly explores the role of federated search, explaining the differences between structured and unstructured data, comparing paid versus free data sources, and examining the value of both internal and external data. Hugo also discusses how emerging technologies such as artificial intelligence (AI) and natural language processing (NLP) are revolutionising the KYC process by not just retrieving information but also contextualising it for better decision-making.
What is federated search?
Federated search is a search technology that retrieves information from multiple databases or sources—whether they are internal, external, paid, or free—and returns a single, unified list of results. Unlike traditional search engines that pull data from one index or a limited set of sources, federated search reaches across various silos and aggregates information from disparate systems.
For KYC screening, the ability to tap into multiple data repositories in real-time is crucial. This could include government databases, news sources, social media, internal records, and more. Federated search ensures that KYC professionals get a comprehensive view of a customer by bringing together data from numerous sources. However, just retrieving information is not enough. The real power lies in how this information is processed and contextualised for the user, which is where AI and Natural Language Processing (NLP) come into play.
Structured vs. unstructured data
One of the main challenges in KYC screening is dealing with different types of data. Data generally falls into two categories: structured and unstructured.
Structured data is highly organised and easily searchable in a database. Think of it as data that fits neatly into tables or spreadsheets. Examples include customer names, addresses, phone numbers, and transaction histories. Structured data is easy to work with, but it often tells only part of the story when it comes to assessing the risk profile of a customer.
Unstructured data, on the other hand, does not follow a predefined data model. This includes text documents, emails, social media posts, news articles, and even images or videos. Unstructured data is far more abundant but much harder to analyse because it lacks the neat organisation of structured data.
In KYC screening, both structured and unstructured data are essential. Structured data provides the foundational information required for identity verification, while unstructured data offers more nuanced insights, such as reputational risks, social ties, and patterns of behaviour. Federated search enables the aggregation of both types of data, but it’s AI technology like NLP that makes unstructured data truly usable by extracting meaningful insights.
Paid vs. free data sources
KYC screening can rely on both paid and free data sources. Understanding the differences between the two is important when choosing the right tools for the job.
Paid data sources typically offer highly accurate, reliable, and up-to-date information. These sources include government databases, credit agencies, and commercial data providers. While accessing these resources may require a subscription or payment, they often provide high-quality structured data that is indispensable for KYC processes.
Free data sources encompass a wide array of publicly available information, including social media platforms, news websites, open-source intelligence (OSINT) tools, and even government-issued reports. While free data is more accessible, its accuracy and reliability can vary. OSINT is especially useful for gathering unstructured data on individuals and organisations from public sources, such as news articles, blogs, or even social media profiles.
The key advantage of federated search is that it can pull from both paid and free sources simultaneously, providing a more holistic view of the customer or entity in question. This combined approach enriches the KYC screening process by providing both the depth (from paid sources) and breadth (from free sources) of information.
Internal vs. external data
KYC screening also involves aggregating both internal and external data.
Internal data refers to the customer information that a company already holds in its internal systems. This can include transaction histories, communication logs, and past interactions. Internal data is highly structured and reliable, as it is generated and stored within the organisation’s own systems.
External data, on the other hand, refers to information sourced from outside the organisation. This includes data from news outlets, government databases, regulatory filings, and OSINT tools. External data provides additional context and helps organisations assess potential risks that may not be evident from internal data alone.
Federated search enables the simultaneous exploration of both internal and external data sources, thereby providing a more comprehensive view of the customer and their risk profile. This cross-referencing of data enhances the accuracy of KYC screenings and reduces the likelihood of false positives or false negatives.
The role of AI and NLP in automating KYC screening
The sheer volume of information available for KYC screening can be overwhelming, especially when dealing with unstructured data. That’s where AI and NLP come into play. AI technologies are increasingly being integrated into federated search platforms to not only retrieve data but also to analyse, contextualise, and interpret it.
For example, NLP can automatically scan news articles, social media posts, and other unstructured data sources to identify key phrases, patterns, or sentiments that indicate potential risks. AI algorithms can then correlate this information with structured data to give a more nuanced and accurate risk profile. This automation not only speeds up the KYC process but also reduces the likelihood of human error.
Not just about quantity—context matters
A common misconception is that the value of federated search lies in its ability to return large amounts of data. While quantity is important, the true value comes from the platform’s ability to contextualise the data. Rather than simply presenting users with raw data, federated search platforms enhanced with AI can summarise the findings, highlight risks, and present actionable insights.
For instance, a federated search might pull in hundreds of news articles about a person of interest. Instead of expecting a human to manually sift through all this information, an AI-powered system could automatically flag mentions of financial misconduct or political exposure, helping the user focus on the most relevant information.
Why technology beats manual screening
In the past, KYC screening was a largely manual process. Analysts had to sift through multiple databases, cross-reference information, and make subjective decisions about risk. This was not only time-consuming but also prone to human error.
By leveraging federated search and AI technologies, businesses can automate much of the screening process, reducing the time and effort required while improving accuracy. Automation ensures that organisations stay compliant with KYC regulations, reduce operational costs, and mitigate risks more effectively than through manual efforts alone.
Summing up, federated search is an integral component of modern KYC screening, offering the ability to gather and analyse both structured and unstructured data from a wide range of paid and free, internal and external sources. When enhanced with AI technologies like NLP, federated search can do much more than just retrieve data—it can contextualise and interpret that data to offer actionable insights. By automating these processes, organisations not only enhance their compliance efforts but also reduce human error and improve overall efficiency. In the evolving landscape of KYC, the use of advanced search technologies is no longer optional—it’s essential.
Follow Hugo Chamberlain for more insights on LinkedIn.
