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Client intake, Donor due diligence:

9 AI background research use cases | Updated for 2024

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Regardless of your industry, the question is no longer whether you need AI… but how to use it. From scrutinising potential partners and suppliers to ensuring compliance with regulations, AI has become an indispensable time-saving and cost-reducing tool. Here, we’ve compiled 9 use cases that are transforming the way organisations know who they’re doing business with.

Know Your Customer (KYC)

Know Your Customer (KYC) is a regulatory requirement for many different sectors. While the term is associated with the financial sector due to heavy regulatory requirements, various organisations now perform KYC checks. This is either due to regulations or because it’s seen as essential for managing risks, reputation, and business growth.

KYC refers to the policies and procedures put in place by businesses to manage risk and verify the identities of customers, clients and suppliers. For regulated sectors, this ensures compliance with national and international regulations designed to prevent criminal activity, such as money laundering, terrorism financing, fraud and corruption. 

KYC is about truly understanding your customers and potential customers to detect selling goods and services to criminals. This includes researching publicly available data to investigate individuals, organisations, senior executives and directors. However, the explosion of publicly available information has meant that most KYC processes have become a misnomer. 

All too often KYC is perceived as a mere tick-box exercise. Compliance teams search corporate registries and other databases to complete the minimum regulatory requirements of a KYC check. But this approach falls short of truly understanding their customers beyond whether or not they appear on a dataset. Knowing that someone is listed on a database as a PEP for example doesn’t tell you any wider context or information about them. 

The growing mass of online data concerning public and private individuals has introduced both opportunities and challenges. While it allows organisations and their compliance teams to potentially gain deeper insights into customers, the sheer volume of online data can become overwhelming. 

AI plays a pivotal role here to search the entire indexed internet in conjunction with PEPs and sanctions databases, news and online media, corporate records, and platforms like LinkedIn, Wikileaks, and offshore leaks – all in under 10 minutes. Using Natural Language Processing (NLP), it then identifies vital networks and affiliations. 

AI enables organisations to see far beyond limited data sets to gain a holistic view of their subject. For instance, while a customer might not be directly subject to sanctions, AI can uncover connections in media coverage that reveal them as a ‘close associate’ of a sanctioned individual. Unlike conventional sanctions checks, these connections might remain hidden in the news, potentially exposing organisations to the risk of overlooking high-risk customers. 

By outsourcing this exhausting task to AI, humans are better able to step back and see the bigger picture. This enables them to spot the risks and opportunities associated with working with a given organisation or individual. Simply put, AI enables organisations to truly know their customers. 

Prospect research

Organisations need a strong fundraising strategy to not only maintain current donors but connect with new ones. That’s where prospect research comes in to help to identify potential donors beyond their existing pool. But this research often involves manually checking databases and searching the internet which returns an overwhelming number of results. 

Consequently, processes are put in place to prevent being overwhelmed, such as time limits on research or “number of pages of X search engine to review”. This leaves organisations open to missing critical information nestled somewhere in the thousands of result pages. Put simply, gathering all the data on an individual or entity would be humanly impossible or at least impractical. 

AI can do all of the above at a speed that surpasses a human’s ability by identifying instances where an individual’s name is mentioned in articles and on websites and then extending research from there. Mentions are then linked together to reveal how that person is discussed in different contexts. 

AI can scour the web for all donor-related information and surface any potential risks to know about. These insights aid in assessing a prospect’s alignment with an organisation, but also developing deeper relationships with them. By introducing AI early in the process, organisations can become laser-focused on prospects worth pursuing.

Donor due diligence 

When a person or business connected to a nonprofit is involved in a scandal, it can erode donor trust, lead corporations and other key stakeholders to withdraw support, and significantly impact future fundraising efforts. That’s why donor due diligence is a vital step in the gift acceptance process for any organisation. 

However, it requires a significant time investment, involving an in-depth online search to collect comprehensive information about a donor. Using just a single keyword like ‘investigation’ or ‘scam’ next to their name produces an overwhelming number of results. Furthermore, the amount of duplicated content to sift through demands even more time, which can lead to early burnout.

AI’s searching speed on the indexed web surpasses human capabilities. It reads thousands of times faster across multiple languages and extracts information from diverse sources such as blogs, news, company records, and more. After processing and understanding this data, AI presents it in a comprehensible format. 

In a matter of minutes, AI can identify any potential risks associated with the individual. This involves scanning for controversial topics, negative sentiments, or other indicators that might suggest a problematic history.

Corporate partnership research 

Nonprofits rely on corporate partnerships to secure steady funding and access more resources. But forming and maintaining ties with potential corporate partners takes time and effort. If organisations don’t have existing connections, they must search online to find ways to reach decision-makers. 

On the flip side, not doing thorough research can lead to misalignment between partner organisations which can lead to complications down the line and often significant wasted investments by both parties. Furthermore, partnerships with corporations perceived as having unethical practices can harm the partner organisation’s reputation and mission. 

AI bridges this gap, performing the same type of research at an unparalleled speed. For instance, a corporation’s intent to support a specific cause or its keen interest in a certain type of philanthropy might be reported in news articles. This valuable information could be easily missed by corporate partnerships teams due to the sheer volume of results from search engines.

AI can also analyse historical information to predict the success of potential corporate partnerships. By assessing past collaborations, donation trends, and market dynamics, AI can inform corporate partnership teams about how a new partnership might unfold. This enables organisations to make data-driven decisions that lead to fruitful collaborations.

M&A due diligence

One significant aspect of M&A due diligence involves evaluating a target company’s reputation and the potential risks tied to it. This usually entails investigating negative media coverage and any claims or disputes involving the company or its senior staff. 

However, this can be a time-consuming research process that often requires manual Internet searches. AI steps in to address this challenge by swiftly scouring the entire internet, uncovering even the most obscure details about a target company online. 

Given the vast amount of online information, human researchers cannot practically search and analyse everything manually. AI’s ability to read online content like a human allows it to surface allegations, controversies, adverse media, and risks related to ESG factors. These insights can be identified early in the deal process, significantly reducing the likelihood of missing critical reputational risks.

Full M&A due diligence is an expensive and involved effort, requiring significant legal and other outlays. Using AI to run background research on the partner corporation and its directors early on in any M&A process can save vast amounts of wasted resources down the line. 

Litigation analysis

For law firms, determining whether pursuing a dispute is worth the costs is a pivotal question. To arrive at an answer, they must gauge not only the strength of their case but also whether it’s even worth pursuing the other parties.

In the past, attorneys had to engage in extensive background research on the opposition to gain insight into the challenges they would face. This process was time-consuming and overwhelming, often resulting in overlooked insights. But that’s where AI does things differently. 

AI rapidly scans the entire web, gathering data from various online sources. For instance, AI can reveal if the opposition has a history of bribery accusations in foreign jurisdictions, which might have previously gone unnoticed through manual research. Without it, attorneys would have less ammunition to strengthen their argument and secure a strategic client advantage.

Anti-bribery and corruption research 

Most jurisdictions have strict legislation to prevent bribery and corruption. To comply with this legislation, organisations are required to perform checks on potential customers before working with them. If done manually, this can involve hours’ worth of online research.  

That’s where AI comes into play. 

By thoroughly scanning the indexed web, AI can gather information from diverse sources, revealing potential risks. For instance, it could unearth historical bribery allegations spread across different countries. Armed with this capability, various entities, including law firms, can proactively tackle new accusations that their clients might confront, effectively safeguarding their reputation in the long term.

ESG supply chain risk research 

Defining ESG risk is challenging due to its broad, varied, and nuanced nature. From human rights issues and climate impact to bribery concerns, among others, it’s challenging for organisations to identify and assess these different risks. This only intensifies for organisations with complex global supply chains. However, AI is reshaping how organisations understand and assess ESG compliance and performance across their entire supply chain

Unlike a simple ESG checklist or basic compliance check that provides yes-or-no responses, AI can triangulate information between multiple sources to identify themes and patterns. It then processes vast amounts of information such as industry reports and ranking to help businesses evaluate a supplier’s ESG performance across various criteria, from carbon emissions to labour standards. 

As a result, it surfaces potential risks much faster than a human could. For instance, if a supplier is linked to environmental controversies or labour violations, AI would surface it with its verified source, enabling the organisation to take fast action. 

HR background research

When organisations bring in a new employee or executive without truly knowing their background, they’re essentially putting the company’s reputation on the line. In the past, companies would check public data sources to prevent this, spanning commercial databases and manually searching the web to ensure businesses make well-informed hiring decisions.

But this process is time-consuming and complex, leaving room for potential oversights. For example, when a business evaluates a candidate with a history of concealed financial misconduct, uncovering such information on the web can be labour-intensive and easily missed by a human researcher. 

Moreover, search engine results can be influenced by biases. AI, on the other hand, remains impartial to biased SEO results and tirelessly navigates through numerous pages, even up to page 200 on Google, absorbing and retaining vast amounts of information. 

Should a potential employee or executive suddenly emerge in negative media for unethical behaviour, AI would surface it. Its swift analysis combined with human supervision can strengthen a company’s selection procedures, ensuring protection against appointing directors with concealed risks or questionable histories.

What’s next for AI and background research? 

A clear distinction has been drawn between searching and researching in this article. 

While traditional search engines and look-up tools can answer a single question, they can’t provide nuance and context. For this, you need research. This means taking single items of information – like that provided by search engines – and knitting them together to form complex ideas such as “personas” and not just isolated nuggets of information about an individual or entity. 

Historically, this has only been possible using human intelligence. But the latest developments in AI can take the laborious, time-consuming element of this research off human hands. Moreover, it can hold vast quantities of data in a sophisticated knowledge graph, allowing it to draw connections between disparate pieces of information in a way that would at the least be humanly impractical if not impossible.  

Looking to the future

What this will unlock in the future is hard to predict but it’s clear that for businesses across the world today, being AI-enabled is driving massive cost savings. This is due in part to the time saved from manual research methods, which enables professionals, such as lawyers, to cut down due diligence time and start billing clients sooner. 

Vast sums can be also saved when background research is implemented into business processes. By using AI to conduct background research on potential customers, partners, investors and applicants as the first step in any onboarding process, businesses can prevent huge amounts of time and money from being wasted further down the line. 

If you want to find out how using AI for background research can drive efficiencies in your organisation, get in touch with one of our consultants today. 

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