Why Adverse Media Screening is vital for donor due diligence

When Adverse Media Screening is powered by AI, it’s easier to avoid risk, protect your reputation, and boost fundraising.

The Enterprise Risk Management (ERM) Initiative’s latest annual State of Risk Oversight report found that nonprofit organisations reported the largest increase in risk volume and complexity over the past five years (from 55% to 76%). This was attributed to economic and geopolitical challenges, the great resignation, supply-chain blocks, cyber threats, and elections.

When Adverse Media Screening is powered by AI, it’s easier to avoid risk, protect your reputation, and boost fundraising.

The Enterprise Risk Management (ERM) Initiative’s latest annual State of Risk Oversight report found that nonprofit organisations reported the largest increase in risk volume and complexity over the past five years (from 55% to 76%). This was attributed to economic and geopolitical challenges, the great resignation, supply-chain blocks, cyber threats, and elections.

In the same survey, 62% of nonprofits described themselves as ‘risk averse’ or ‘strongly risk averse’, but only 23% described their risk management oversight as ‘mature’ or ‘robust’.

Adverse media screening (AMS) is a key part of risk management and due diligence. It helps non-profit organisations identify high-risk donors, such as politically exposed persons (PEPs) or those with negative media coverage.

AMS involves checking news articles, social media posts, legal filings, and other public records to identify any potential risks that could harm the nonprofit’s reputation.

Those risks will vary according to the nonprofit, but they can include links to financial crimes, corruption, fraud, money laundering, terrorism financing, forced labour, environmental damage, human trafficking and more.

Why does AMS matter?

The consequences of failing to carry out proper AMS can be severe.

Here are a few from the UK.

• In 2019, the Royal Shakespeare Company announced it would end its partnership with BP four earlier than expected. The decision was made in response to feedback from young patrons who said that BP’s sponsorship was putting a barrier between them and their desire to engage with the RSC due to environmental concerns. 

• The Sackler Trust, a charitable foundation associated with the Sackler family, which has been accused of fueling the opioid epidemic in the US, donated over £13 million in the UK in 2021. King’s College received £750,000, while the Chelsea and Westminster Hospital NHS Foundation Trust received £250,000. The donations sparked a backlash from donors and the wider public, and both organisations eventually announced that they would no longer accept donations from the Sackler Trust.  

• In 2020, it was revealed that Save the Children accepted a £15 million donation from Qatar despite calling the country’s claim to have scrapped slave labour a stunt. A leaked memo said accepting the donation would leave the charity “open to accusations of hypocrisy and failing to abide by our values.”    

As you can see, the consequences of failing to carry out proper AMS can be severe.The difference is the rate at which Xapien can process this text – it can read thousands of times faster than any human, and can read in 187 languages.

Just one inappropriate donor can cause a nonprofit irreversible reputational damage. And just one link to a bad actor can be enough to tarnish the nonprofit’s reputation in a way that impacts its finances for years to come.

Key benefits of ASM

• AMS helps protect nonprofits’ reputations by ensuring donations are not accepted from individuals or organisations with questionable backgrounds.

• In turn, AMS limits exposure to reputational risks and financial losses. Such risks can be particularly severe if the non-profit is found to have received funds from individuals or organisations involved in illegal activities.

• AMS helps nonprofits avoid inadvertently becoming involved in scandals with far-reaching consequences. By identifying potential risks early on, nonprofits can prepare their response and take appropriate action to prevent the situation from escalating.

• AMS is increasingly becoming a legal requirement for nonprofits in some jurisdictions. Nonprofits that fail to comply with these requirements may face legal consequences and financial penalties.

• Even if it’s not a regulatory requirement, thorough and transparent AMS demonstrates a nonprofit takes security seriously and positions itself as an industry leader.

• And, even if AMS doesn’t reveal any suspicious information, it can help nonprofits gain insights into prospective donors at speed.

But AMS in the nonprofit sector does face certain challenges, especially because of how complex the donor due diligence process already is.

Many nonprofits lack time resources or large research teams to look into potential donors. And even if they did, there is far too much information available online for human researchers to ever be confident they’ve read it all.

This is why nonprofits are turning to AI-powered solutions that enable them to access data from the entire indexed internet and turn it into actionable risk intelligence in minutes.

Rapidly evolving sanction lists and shifts in public perception about what constitutes moral behaviour mean that traditional methods of manual research, PEPs and Sanctions, and ID verification software, are no longer enough.

The challenges of AMS

Xapien knows that ‘he killed it on the basketball court’ doesn’t make your subject a killer

The most common way to check for adverse media is ‘keyword search’, which is used alongside databases and ID verification software.

Researchers enter the donor’s name and keywords such as ‘financial crime’, ‘corruption’, ‘fraud’, and ‘money laundering’ into a search engine and see what comes up.

The problem with this approach? Researchers are shooting in the dark. If there’s an issue they don’t think to enter into the search engine, it likely won’t be found.

However, somebody else might… after the donor has been accepted.

Moreover, it’ll produce thousands and even millions of search results. Most researchers won’t go beyond page 20. So, if the adverse media is on a later page, they won’t discover it.

And, Google shows different results to people depending on their search history. What might be on page two for one researcher could be buried on page 30 for another, which is known as a ‘filter bubble’.

Then you’ve got the challenge of names. It’s impossible to control what Google shows you in the results of a search. You can’t tell Google that the only ‘Chris Smith’ you’re interested in lives in Denver and works as a lawyer.

Nonprofits attract donors from all around the world. The vital piece of negative news might be in another language, and again, easily, missed.

Even if the right person is found, researchers still need to check:

• Risk directionality – your subject might not be the guilty party

• ​Negations – if your subject worked in ‘counter-fraud’, or ‘fighting financial crime’

• ​​Context – unearthing ‘he killed it on the basketball court’ doesn’t make your subject a killer

This is why nonprofits are turning to AI-powered solutions that enable them to access data from the entire indexed internet and turn it into actionable risk intelligence in minutes.

How Xapien makes AMS simple and beneficial

Xapien empowers organisations with the knowledge they need to make informed decisions about who they do business with.

The explosion of online data and increasing transparency requirements have made that information available to everyone. The challenge is using it in a meaningful way for your nonprofit.

Xapien replicates the manual search process but at an incomparable scale and speed. It reads and processes millions of online corporate records, shareholder data, media and news articles, corporate websites, blogs, Wikileaks, and more. Then, it analyses it all to create a background research report on any organisation or person based on information from the entire indexed internet.

Institutions that wait to incorporate automation and prospect research software into their prospect research will fall behind. Not only will they devote more time and resources to research, they will miss out on the extra growth opportunities it can deliver.

In minutes, it can do the work of 100 researchers.

This frees up researchers and fundraisers to understand, analyse, and evaluate risks for effective decision-making. Instantly, they’re equipped with hard-to-find insights that make it easy to approach suitable donors.

Information overload

With Xapien’s developments in Natural Language Processing, we can go beyond keyword searches to read all of the open-source data about your person.

Rather than trawling through dozens of articles, you have an easily-digestible and comprehensive (not to mention fully-sourced and traceable) report to base informed decisions.

While traditional research tools may be effective for structured data, they’re not equipped to handle unstructured data such as text, speech, and video. Need proof? Gartner estimates that more than 80% of the data on the internet is unstructured.

By training our platform to read, understand, and analyse text just like a human would, we can extract valuable information from unstructured data at incredible speed and scale.

Directionality, negations and context

Xapien can detect the grammatical subject of a risk-related word. Let’s say a news article contains the sentence Chris Smith accused Julie Davies of corruption. Xapien knows that Julie is the risky person and not Chris Smith, who’s levelling the accusation. This means it can tell whether or not a risk is tied to your person, and if it’s actually risky.

Xapien can also differentiate between real risks and false ones. If you run a string search with the risk words ‘court’ and ‘kill’, then an article with the sentence ‘He killed it out there on the basketball court!’ will be one of the top results.

But it isn’t risky, so Xapien won’t highlight it as a risk.

Names

Xapien can distinguish between two people with the same name, and identify potential risks associated with each of them. It does this by processing the many factors surrounding the mention, such as other people, sectors, topics or organisations, just like a human would.

It has also been trained to understand cultural naming customs. It can detect which language or culture a name is likely to derive from, translate and transliterate it into the relevant languages, and then run searches with those different versions.