Category: Name Screening

Ripjar Summit Singapore 2023: Challenges and Innovations in Customer Screening

In September 2023, Ripjar’s latest Summit took place at Singapore’s Swissôtel The Stamford, overlooking the city’s scenic Marina Bay. Senior financial compliance professionals from around the world attended the Singapore Summit, which included an exclusive breakfast and networking event, followed by a discussion on the latest innovations, challenges, and trends in customer screening, and a demo of Ripjar’s AI Risk Profiles solution.

Panel Discussion

Ripjar Chief Product Officer Gabriel Hopkins moderated the panel discussion, which included industry tech, data, and compliance experts Josh Heiliczer (PWC Managing Director), Andrew Chow (Synpulse Senior Advisor), and Simon McClive (Ripjar General Manager of Labyrinth Screening). 

The panel theme was ‘Innovation in Screening’ – here are some of the key highlights from the discussion.

How is customer screening changing?

Picking up the first discussion point, Andrew Chow highlighted the changing role of technology in driving fundamental change in modern screening strategies. He talked about the advent of public-private partnerships and the large data sets now available as part of those arrangements.  Andrew spoke on the need to think about the accuracy of data and referenced the recent so-called Fujian gang scandal, which is now thought to involve at least $2.4 billion in laundered money. When the perpetrators of the scandal first arrived in Singapore, it is likely that the banks failed to accurately understand their links to China due to their nationalities. 

Andrew provided an example from his own experience of carrying out a KYC check on a customer with a St Kitts and Nevis passport. After running the check, he later found out that the customer was a Chinese national, and had obtained a second passport. The incident highlighted the importance of data accuracy in customer screening.

Josh Heiliczer echoed the need for screening accuracy. He noted the importance of adverse media screening, as well as other public domain data sources, and even social media, in identifying the signals, and addressing the scale of international money laundering risk. He also noted that the accuracy problem may be attributed to an increase in false positives: compliance teams can reduce false positives by using secondary “identifiers”, and cross-script matching (which can also improve matching accuracy). Josh highlighted the need for firms to have a risk appetite framework in place, outlining which sources they are using to carry out effective screening. Those sources may vary depending on the customer’s region.

Simon McClive noted that firms increasingly have to deal with rapid changes in their compliance burdens, and used the example of recent Russia sanctions, which saw some organisations forced to upscale their screening solutions to accommodate thousands of new entities in a matter of weeks. Simon pointed out that firms need to have the processes and resources in place to cope with that kind of rapid change, all the while considering factors like new foreign language screening requirements and data quality, to ensure they’re building an accurate picture of the risks they face.  

What lessons can we learn from recent money laundering scandals?

Andrew Chow stressed the importance of banks and financial institutions never assuming that they are “100% protected” from criminal risk. He added that those institutions must understand that new threats will always emerge. In the Fujian case, inflows appear to have come from other countries in Asia, and the banks involved had also not adequately identified the source of funds. He suggested that without the use of the latest technology, the scam may not have been discovered. Furthermore, the subsequent asset recovery effort currently stands at over $2 billion, which is significant by general standards.

Josh Heiliczer commented on the seizure of funds, contrasting the Fujin total with the estimated $275 billion that banks spend globally each year to tackle money laundering, and to the estimated $5 trillion of funds which are laundered. In summary, he suggested that the “cost of laundering is about 1.5% right now,” adding that “when I started in this business, it was 20-30%.”  

Josh went on to talk about the way that money was moved across Asia, conducted on domestic payment networks despite being international funds transfers. For example, entities or individuals seeking to move funds outside of exchange controls such as $50,000 in China are often matched by laundering gangs with funds from a criminal origin (drugs, scams etc) to be moved into China. Once the criminal funds are in China they are “washed into goods” such as electronics for export and sale. Josh highlighted the value of bringing together transaction screening with adverse media and other data to get a complete picture of risk. CRS (Currency Reporting Standard) data can also add value to a balanced screening approach and Josh noted that “one of the things that clients don’t do well is looking back at client CRS data”. He forecast that there will be additional scrutiny of foreign exchange transactions in future.

Adding to those thoughts, Simon McClive raised the importance of native multi-language and multi-script screening capabilities in detecting international money laundering threats, including the need for solutions that operate across dialects and scripts, and deal with issues such as nicknames and aliases. 

How do you get multi-script screening right?

Expanding on his previous points, Simon McClive suggested that firms should focus on the risk-based approach when implementing a multi-script screening solution. In practice, firms must consider how they can refine their adverse media searches in ways that provide value: for example, is it useful to screen in a manner that surfaces Latin American risk, when searching for Asian Pacific entities of interest? Firms should instead seek to balance their screening solutions in a way that provides meaningful, relevant data. 

Josh Heiliczer noted that firms can also test their screening solutions based on certain risk perspectives. For example, a compliance team might take into account regional factors such as the presence of clients from a specific region of China, that might prompt a change in screening parameters in the future. Crucially, firms should set out their risk appetite and screening approach, and calibrate accordingly.

What is the role of AI in client screening, and how can people use it successfully?

Simon McClive noted that firms must be able to adapt to the changing capabilities of AI technology. For example, while generative AI is theoretically capable of pulling coherent information from vast amounts of unstructured data, its output is only ever going to be a probabilistic response, based on its predictive algorithm. Similarly, generative AI model responses are often inaccurate, biased, or fabricated – which limits the technology’s application in regulatory compliance contexts and means that firms must be aware of its risks. 

With that in mind, Simon noted Ripjar’s use of generative AI as a fast, accurate means to summarise customer risk data and present a concise overview – in turn, supporting quick, accurate analyst decisions, and setting out the provenance of each claim within the summary. He stressed the importance of being able to explain the responses that AI tools generate to authorities and regulators, so that the results can be used in investigatory contexts. 

Andrew Chow also raised the importance of explainability, noting that regulators typically don’t understand the probabilistic approach to customer data. Josh Heiliczer characterised the explainability problem as “significantly difficult” – and noted that firms might ultimately have to go through the “very complicated process” of understanding their data sets in order to be able to use them in regulatory actions.   

Building on those sentiments, Simon McClive suggested that it might be the responsibility of vendors to “lift the lid” on the AI space as a way to promote safe use of the technology. AI innovation is moving rapidly, and firms might be able to avoid some challenges and pitfalls by putting certain controls in place sooner rather than later. Simon remarked that, while AI is currently very useful at showing analysts what they should care about in a given data set, compliance decisions are ultimately still made by human compliance employees. Ripjar’s latest experiments highlight the ways in which new technology is increasingly capable of automating decision-making as part of a process that is likely initially validated by analysts. 

What are the big challenges for AI in adverse media screening?

Simon McClive listed the reliability of adverse media sources as a critical challenge for AI models – and warned specifically about the increasing volume of content created by generative AI models. With this in mind, firms need to be much more discerning about the sources they use as inputs for their screening solutions, and consider how far they trust that content. 

Josh Heiliczer stressed that firms need a way of effectively identifying entities within adverse media sources as a way to manage large volumes of false positive alerts. He emphasised the need for both high quality internal and external data coverage as a means to improve those false positive rates. Expanding on the question of quality that Josh raised, Andrew Chow noted the importance of adding context to certain critical data points as a way to facilitate more effective risk-based decision making. 

Presentation: AI Risk Profiles 

The summit also included a presentation on AI Risk Profile technology: an innovative addition to Ripjar’s Labyrinth Screening solution that enhances the depth and detail of risk data, and helps firms make stronger compliance decisions.

Why do we need AI Risk Profiles?

Opening the presentation, Gabriel Hopkins highlighted a number of issues and difficulties related to adverse media screening. He started by echoing the panellists’ earlier warnings about the challenge of false positive alerts – which can make finding true risk like searching for a needle in a haystack. Gabriel also pointed to the need to obtain “the right data” on subject entities, uncovering not just financial risk but, where demanded, other types of risk (such as ESG), without becoming overwhelmed with false positive hits in the process. 

At a global scale, regulators are also beginning to expect more systematic adverse media checks. Jurisdictions like Singapore and the EU already have adverse media screening requirements in place for banks and other institutions, while the US and Canada are not far behind. International AML organisations are helping to build that regulatory momentum, with the Wolfsberg Group addressing adverse media screening specifically in its 2022 Negative News FAQs. 

As the adverse media landscape shifts, firms will need to integrate solutions capable of matching criminal threats, and satisfying regulatory responsibilities. 

How do AI Risk Profiles work?

AI Risk Profiles offer firms a way of surfacing risk on entities quickly and effectively – both in terms of structured data from sanctions, PEPs and watchlists, and from unstructured data in the form of news articles. Integrating machine learning algorithms, AI Risk Profiles technology is capable of extracting only the most relevant risk data for a given entity, across 26 languages, even selecting the more important and recent news stories to present a comprehensive up-to-date picture of risk. 

Once collected, the data is presented as part of a unique entity profile. The latest addition to AI Risk Profiles – about to be launched in beta – will see a short, large language model (LLM) generated summary of risk (including citations) added to screening responses. The LLM-generated summary will provide a clear, concise, but comprehensive overview of the associated risks, complete with links to the relevant news stories to ensure the explainability of that information. 

AI Risk Profiles in Action

The presentation included demonstrations of profiles for a number of Singaporeans involved in recent money laundering scandals. In one example, the AI Risk Profiles surfaced articles as far back as February 2019, highlighting the risk well before the subject was charged and before a watchlist entry was produced in August 2022. 

Ripjar’s Labyrinth Screening draws on around 6 billion news articles from multiple premium providers and, based on that content, identifies the important stories that contain information relevant to subject entities. With so much data to sort through, AI Risk Profiles works to cluster the relevant information, separating out individual entities (with similar or matched names, for example) in order to simplify analyst review. Relevant data points are assigned to specific profiles in order to add depth and detail, and build a clearer picture of risk.   

The demonstration included an example search for the name “David Cameron”. Using AI Risk Profiles technology, firms can utilise rich profiles for entities with a specific name (in this case David Cameron), where searches might previously have been overwhelmed with stories about the UK’s ex-Prime Minister. In the demonstration example, AI Risk Profiles used contextual information to build a profile for a convicted UK drug dealer named David Cameron, surfacing contextual data such as the subject’s birthdate, his place of residence, his brother, and the name of his convicting judge. By contrast, the profile for the UK Prime Minister included stories about politics, association with current Prime Minister Rishi Sunak, involvement in the Greensill scandal, and so on. 

In practice, should a firm deal with a customer named “David Cameron”, AI Risk Profiles would be capable of generating a series of relevant profiles, built out with contextual information, with the risk-relevant stories clearly surfaced. 

The Advantage of AI Risk Profiles

AI Risk Profiles help firms conduct their adverse media screening process with enhanced speed, accuracy, and confidence. In a real world case study, a bank set out to identify 75 confirmed identities, and using AI Risk Profiles, managed to massively improve its screening review process. Historically the bank would have looked at around 82,000 articles and would have identified 85% of the expected matches as part of their screening process. With AI Risk Profiles they had to review only 685 profiles and surfaced 90% of the expected matches. Elsewhere, a US investment bank integrated AI Risk Profiles as part of its onboarding process, reducing onboarding time from around 14 minutes to around 3 minutes. 

In future, and as generative AI evolves as a technology, it may be possible to take AI Risk Profiles further, having the platform make suggestions about how risk decisions might be made – based on the information available on subject entities. 

To learn more about Ripjar’s AI-powered adverse media screening technology, get in touch today

Ripjar Summit Paris 2023: Hot Topics and Trends in Screening

In June 2023, Ripjar brought together senior compliance professionals from banks and financial institutions operating in France and around the world, for an event at the Peninsula Hotel in Paris. Situated in the heart of the French capital, the second Ripjar Summit included a luxury networking breakfast, live demo of Ripjar’s new AI Risk Profiles solution, and an expert panel discussion of current screening challenges and trends. 

The panel discussion involved international compliance and financial technology experts, with a focus on customer and counterparty screening challenges, and on how screening and compliance will change in the future. The panellists included Jérôme Grelier (Partner Manager Director at Accenture Consulting), Vera Akiotu (Director of Financial Crime Compliance Proposition at Dow Jones), Bertrand Bouquet (Head of Process Leader for Screening & Filtering at BNPP), and Simon McClive (General Manager of Labyrinth Screening at Ripjar). The discussion was moderated by Ripjar’s Chief Product Officer, Gabriel Hopkins. 

Let’s explore some of the key points and insights from the Ripjar Paris Summit panel discussion: 

What regulatory changes are we seeing in screening frameworks in Europe and around the world?

Bertrand Bouquet opened the discussion with a European perspective on recent regulatory screening changes. He pointed out that the fundamental customer screening regulations have broadly stayed the same in recent years, with regulators maintaining a high expectation for technical compliance. Bouquet pointed out that efficiency is key to managing regulatory change in this environment – a challenge which can quickly add to a firm’s administrative burden. He stressed that firms must be deeply invested in, and consistent with, their screening compliance efforts in order to achieve the required level of efficiency, and adapt to new challenges.

Adding to Bouquet’s thoughts, Jérôme Grelier mentioned the EU’s new Anti-Money Laundering Authority (AMLA), which will begin operations in 2024. Grelier characterised AMLA as the “beginning of a new era” and suggested that while the regulator would not necessarily make drastic new regulatory provisions, it would focus on tackling the anonymity of financial criminals as part of its approach to screening, targeting in particular beneficial owners, and the use of digital assets such as cryptocurrencies. 

Grelier noted that one of AMLA’s objectives will be the further harmonisation of regulations across Europe as a way of addressing compliance weaknesses, raising compliance standards, and making data sharing and collaboration easier. 

How has the invasion of Ukraine changed attitudes to customer screening, and how have institutions responded?

Vera Akiotu pointed out that the conflict in Ukraine has created an unprecedented challenge for compliance teams around the world, thanks mainly to the sheer volume and frequency of new sanctions, and the coordination with sanctioning bodies. Akiotu highlighted the particular challenge of identifying Russian shell companies, and the administrative burden of dealing with the multitude of sanctions not only against the Russian state, but against Russian individuals. 

Simon McClive characterised the Russian invasion of Ukraine as a “step change” which had generated an enormous amount of work for financial institutions. He cited an example bank that previously had to screen around 180 Russian individuals and 53 Russian organisations prior to the invasion, but that now had to screen over 1,500 individuals and over 200 organisations. McClive noted that the speed and intensity of the new Russia sanctions had forced a collective mindset shift in financial institutions, which had to find ways to deal with an influx of new names and entities, and a spike in false positive alerts. 

The Ukraine situation has also prompted financial institutions to think about the wider geopolitical landscape. McClive mentioned China and Taiwan as an area of growing concern, and the need for firms to gauge their preparedness for new sanctions in event of future conflict. In this environment, firms should think carefully about how to prioritise sanctions compliance resources, and rely on technology to mitigate unexpected burdens. 

What new sanctions screening considerations might emerge in the next couple of years?

Reiterating the prospect of a China-Taiwan conflict as a significant sanctions concern, Simon McClive pointed to the significant exposure of Western firms to supply chain and wealth management risks. He highlighted the production of semiconductors across China as a likely sanctions target – an issue that has led many firms to begin sourcing the technology in the US and Europe.

Vera Akiotu noted that Russia sanctions aren’t going away and, in fact, are likely to expand in the future. She suggested that India and Singapore are territories to watch, as a result of their connections to the Chinese economy, but that these countries have also demonstrated a desire to build connections with the West. Akiotu also mentioned the particular danger of sanctions evasion in countries like Kyrgyzstan in the CIS region. 

What role does AI play in compliance?

Jérôme Grelier noted that AI can play a variety of compliance roles but highlighted the technology’s potential for enhancing customer screening processes, and reducing false positive ratios, by helping to identify and extract the most relevant information from data. Grelier pointed out that AI tools could be used to fully automate low level screening tasks (such as data collection), and to allocate AML/CFT alerts to the right teams quickly and efficiently. 

Bertrand Bouquet also acknowledged AI’s potential to improve screening, but stressed the importance of understanding how to deploy AI across a compliance infrastructure, and of building the right team, with the right skills, to manage it. Bouquet pointed out that financial institutions must apply scrutiny when selecting AI use cases, to ensure “progressive and adequate mastering of those solutions”. He noted that AI solutions must remain under control at all times, be reactively configurable, and be “explainable” to regulators in compliance contexts, especially with regards to sanctions programmes.   

Vera Akiotu echoed this sentiment, noting that “AI can’t achieve everything on its own. It’s bringing in that human expertise with AI and automated solutions that gives the true output that organisations can rely on.”

How should financial institutions balance AI tools such as Chat GPT with customer screening processes?

Simon McClive referenced “explainability” as a critical factor in the integration of AI in customer screening. He highlighted the phenomenon of “hallucination” in which generative AIs such as ChatGPT create false data points that “look plausible” (such as customer dates of birth) but that are seemingly manufactured by the predictive language model the tool is using. 

While that kind of output is undesirable, McClive pointed out that AI tools are particularly effective at digesting and summarising large amounts of information. He noted specifically the importance of training AI models extensively on large volumes of high quality data while considering the types of issues that might affect language models, especially in terms of addressing regional risks. By bringing together rich, relevant datasets with Large Language Models, McClive suggested it would be possible to generate highly accurate, succinct summaries of risk that could revolutionise the way banks and other organisations conduct their screening. Bertrand Bouquet echoed that sentiment, characterising the potential impact of the technology of AI as “a matter of training and monitoring”. 

Jérôme Grelier cited the capability to read any type of documents as one of AI’s most disruptive applications in compliance contexts, especially if tools could be tasked with reading and understanding human language. That capability could provide a huge efficiency boost for screening, and be used, for example, to pre-generate forms and customer profiles. 

Using AI Risk Profiles in Adverse Media Screening

In addition to the panel discussion, Ripjar’s Head of Operational Data Science Abhijith Rajan gave a presentation on the Labyrinth Screening platform and the potential impact of AI Risk Profiles technology in adverse media screening. 

In an increasingly complex compliance environment, many regulators are pressuring firms to integrate adverse media into their screening solutions as a way to better capture risk and enhance the quality of financial intelligence. Labyrinth’s AI Risk Profiles technology has been developed to help firms step up and meet these new challenges, even as the landscape evolves.

Extracting Relevant Data

Adverse media searches take in potentially billions of data sources, across different language systems, and with varying degrees of credibility. In developing Labyrinth Screening, Ripjar realised that firms needed a way of resolving entities from the vast amounts of collected data: for example, if an article had information on 5 people and 2 organisations, an adverse media search would ideally be able to identify each entity – and produce relevant data for searches concerning a given individual. The sheer volume of similar stories relating to a particular entity can also be a challenge: customer name searches might generate tens of thousands of articles, (especially for common names), and so add a significant amount of work to the compliance burden. 

A Step Forward 

To address the challenges of adverse media searches, Ripjar took a step forward by developing AI Risk Profiles. Rather than solely looking for media articles, AI Risk Profiles reverse the process to search for the people in those articles. As Abhijith Rajan described it: “We’ve pivoted away from presenting news media and we’ve now generated entities that are linked to this unstructured data.”

By building AI Risk Profiles for particular entities, firms can bring together a range of data sources, including watchlists, media stories, and private networks, and even connect the profile with other organisations and risk factors. Abhijith Rajan used the example of the name ‘Richard Ferrand’, referring to both a US citizen involved in an armed confrontation, and the former president of the French National Assembly. The AI Risk Profiles solution was able to set out the risk factors associated with each individual. For the French Richard Ferrand, the entity profile included his politically exposed person (PEP) status, relevant watchlist information, and his network connections, including friends and relations. Each profile includes links to relevant media stories, organised by the most recent involving the specific individual.  

Reducing the Compliance Burden 

The ability of Ripjar’s AI Risk Profiles to vastly simplify the search for risk brings significant operational benefits. Rajan used a real-world case of a company that conducted an evaluation of the solution. They set out to compare the experience of searching for risk for 77 different identities in AI Risk Profiles to the same search conducted in an article-based system where the Risk Profiles technology was not used. 

Without AI Risk Profiles, searching for the chosen identities produced just under 83,000 individual news articles. Analysts reviewed the articles to identify risk and were able to find 85% of the expected identity-risk combinations.

Adding the AI Risk Profile technology was a game changer. Searching for the same identities returned 685 profiles and a subsequent review of the profiles highlighted 90% of expected identity-risk combinations. 

While all the risks had been present in both experiments, analysts were simply unable to find the needle in a haystack when presented with the non-profile result set – providing an increase  in effective recall (a mathematical measure of success) of 5% while reducing the volume of results that analysts had to review by over 99%.

The evaluation demonstrated the effectiveness of AI Risk Profiles technology, which surfaced risk data that would otherwise have been missed in an article-based name search. 

Exploring Risk Profiles

Abhijith Rajan showcased a range of risk profiles generated for challenging names, and used the example of former UK Prime Minister David Cameron – both a famous and a common name. On review, the profile for the former Prime Minister himself included names of friends and close associates  (such as current Prime Minister Rishi Sunak), and referenced thousands of relevant media stories. 

The demonstration then set out profiles of other examples of individuals named ‘David Cameron’ from around the world. These profiles were distinct from the former Prime Minister’s, including only risk data relevant to those individuals, rather than the thousands of media stories relating to the British politician. 

Using an example of a banking unit in the US, Rajan pointed out that Ripjar’s AI Risk Profiles is saving around 2.6 million minutes of analyst time per year – equivalent to the work of around 21 full-time employees. 

Discover Labyrinth Screening

As the global risk landscape changes, Labyrinth gives you the power to meet screening challenges as they emerge. The Labyrinth Screening platform enables firms to search thousands of watchlists, news stories and other data sources, in over 20 foreign languages, and generate accurate, actionable intelligence in seconds. Ripjar’s AI Risk Profiles help your team extract the most relevant AML data from a crowded, complex landscape, and make strong, effective compliance decisions for every customer. 

To learn more about how Ripjar can help you manage compliance challenges, get in touch today

The Future of Screening: Key Learnings from the Ripjar Summit 2023

The Ripjar Summit 2023 took place on 25 April 2023 at The Shard in London. The exclusive event saw senior compliance and fintech professionals from some of the world’s most prestigious banks and financial institutions participate in a discussion of the industry’s most pressing regulatory challenges, with a focus on the ways in which technological innovation delivers advantages in an evolving risk landscape. 

Hosted by Ripjar’s Chief Product Officer Gabriel Hopkins, the expert panel included FINTRAIL Managing Director Maya Braine, Kharon Vice President of Sales Chris McDonagh, and Ripjar’s General Manager of Labyrinth Screening Simon McClive. The panel discussion was followed by a presentation from Ripjar’s Head of Analytics Simon Smith, on the application of AI Risk Profiles as part of an adverse media screening solution.  

Let’s explore some of the highlights of the 2023 panel discussion. 

What changes are we seeing in regulatory frameworks around screening?

In the context of screening challenges, the panellists pointed out that global regulators are converging around consistent messaging about the integration of technology and automation, including the sharing of AML/CFT data. 

While the direction to use technology is sometimes featured in regulatory detail, the panel suggested that it was more often included in guidance and messaging from regulators, such as “Dear CEO” letters, and from enforcement actions, all of which offer clear signals about screening expectations. Panellists referenced a number of recent regulator publications that emphasised the importance of new AML technologies, including papers from the Financial Action Task Force (FATF), and the Wolfsberg Group. They also highlighted adverse media screening as an AML/CFT technology requirement in a growing number of jurisdictions – including the EU, the UK, Singapore and Australia.

How should organisations balance the use of adverse media in their screening process?

One panellist stressed the need to minimise false positives during the adverse media screening process, and avoid overwhelming compliance teams with information. With that in mind, it was suggested that adverse media screening could be made more efficient in conjunction with digital onboarding processes, which allow AML/CFT solutions to digest and distribute data quickly across a network, and to fine tune algorithms to increase the accuracy of their results. 

Another panellist talked about the need to work with clients to develop the quality of the datasets they collect from adverse media screening. The better the quality of data, the more valuable it is for compliance teams, and the less likely it is to generate false positives. Essentially, firms should think less about volume and more about getting “good data, good technology, and then demonstrating a return on investment”.  

Are we seeing innovation in specific regulatory areas?

One member of the panel raised adverse media screening as a particular focus for AML/CFT innovation. As banks trend away from high risk customers and towards services for retail customers, the panellist pointed out that adverse media screening becomes more challenging since it necessarily involves a higher volume of names and greater use of common names – both factors that typically generate resource-sapping false positive alerts. With that in mind, many institutions are seeking ways to leverage technology to meet their increased administrative burden and maximise compliance efficiency. 

What kind of sanctions challenges can we expect in the future?

On the invasion of Ukraine, one member of the panel noted that a number of firms were caught off-guard by the speed with which the Russia sanctions landscape changed throughout 2022, and felt “blind” as a result of a lack of data. They spoke about the need to not only collect data but to understand it – so that it can be turned into actionable financial intelligence. Part of the challenge is to conduct effective horizon scanning, and for firms to think about their risk exposure in the event of certain geopolitical events. The panellist cited the tension between China and Taiwan as an area of increasing concern, and suggested that firms should use data from the implementation of current Russia sanctions programmes to anticipate potential pain points should new sanctions emerge. 

Echoing those sentiments, another panel member noted that horizon scanning often poses a greater challenge for smaller organisations that may be forced to engage in a level of “geopolitical analysis” that they never anticipated. They suggested that external sanctions support, such as screening technology, can be a huge advantage for smaller firms in this context – as long as they are able to source sufficient risk data on their customers. 

A third panellist referenced the need for timely data as a sanctions priority, especially in the fast-moving Russia-Ukraine space, pointing out that “UK sanctions grew from 150 people and 50 organisations, to around 1,500 people being sanctioned within 12 months”. In such a crowded landscape, firms must be able to make rapid decisions, taking into account language and transcription discrepancies, supply chain risks, and the possibility of future sanctions changes. 

Following a series of massive sanctions fines in 2022, what are the lessons that banks should learn?

On the topic of AML/CFT failures, and subsequent investigations and enforcement actions, one panel member noted that “most of the time… banks are failing at the basics”. They cited failures of banks to perform rigorous KYC checks, internal compliance tests, audits, and quality assurance, as examples of some of the compliance fundamentals that institutions struggle with – and eventually result in violations and costly penalties. It was stated that it’s better to tackle these foundational problems from the ground up “as opposed to playing whack-a-mole, just dealing with the last thing flagged by a regulator”. 

The importance of getting the compliance balance right was emphasised: “Greater use of data and tech is what’s going to solve this. You can’t keep throwing people at the problem. Ultimately, it’s going to be technology, automation, and using your data in a more intelligent manner.”

Another panellist also called on financial institutions to integrate technology to meet their basic compliance requirements, and to ensure they have adequate data to generate valuable positive outcomes. In this sense, it was stated that compliance should not be viewed as a “cost” but as a means of “actively protecting the business”. 

Do generative AI tools such as ChatGPT have an important role to play in compliance?

Acknowledging the dramatic growth of generative language AI platforms over 2022 and 2023, the panel pointed out that the technology has strengths and weaknesses. While generative platforms are “very, very good” at summarising large volumes of information, and providing plausible responses to prompts, they are not always accurate or even factual. In screening contexts where there is an absence of facts, for example, it was suggested that generative AIs sometimes make incorrect assumptions – a trend that inevitably leads to false positive alerts. One panellist also pointed out that training AI models is a long and expensive process, often costing millions of dollars. The training process must include foreign languages, and must be constantly updated to account for new trends and risks. 

Despite those drawbacks, it was emphasised that generative AI has clear, game-changing potential – for helping compliance teams digest huge volumes of data and to “identify risk in a way that isn’t just keyword driven”. 

Another panellist pointed out that AI tools have “proven very useful” in compliance contexts for decades. In particular, they cited AI tools’ effectiveness in combating fraud, and running credit risk assessments and claims management processes. It was suggested that the significant challenge of generative AI is “explainability”: since users “do not know why it is saying what it is”, it cannot be tested, its results cannot be checked for accuracy, and it can’t be explained to regulators. That issue currently precludes generative AI from being used widely in screening contexts. 

AI Risk Profiles in Adverse Media Screening

Following the Ripjar Summit’s panel discussion, Ripjar’s Simon Smith gave a presentation on the advantages of using AI technology to enhance adverse media searches. 

Understanding AI Risk Profiles

In a crowded adverse media landscape, Simon Smith emphasised the need to reduce noise, especially in situations where “thousands, or potentially millions” of articles cover a single story. Simon suggested that firms need to be able to “pivot that article view of the world” into an “entity focus” and noted that Ripjar is achieving that objective by launching technology capable of creating “risk profiles” of individual customers. 

He explained how Ripjar’s AI Risk Profiles pull together the most relevant data from different financial risk topics, including sanctions list and watchlist data, to map out an entity’s risk exposure much more efficiently than searches of thousands of adverse media stories.   

Creating a Risk Profile

Simon demonstrated the AI Risk Profile technology with a profile for Belarussian president Alexander Lukashenko. In creating the profile, the system identified relevant information, from news articles and other media, that linked to specific risks, including sanctions risks and financial risks, in order to build out a comprehensive, accurate risk profile. The profile integrated Lukashenko’s relatives and close associates, and set out the reasons why he was considered to be high risk, including connections to money laundering and voter fraud activities. 

The Advantages of AI Risk Profiles

Ripjar’s AI Risk Profile technology essentially allows firms to take huge volumes of data and then automatically resolve that data around a single profile in order to “make it easy to surface the risks”. Simon referenced firms using article-based adverse media screening and generating unmanageable volumes of results from their searches, reaching into the tens of thousands, but then only finding a relatively low number of genuine risks. By contrast, AI Risk Profile technology significantly reduces the false positive alert rate, by up to 99% in some cases, while increasing the number of accurately identified risks. 

Simon used the example of a criminal with the name “David Hugh Cameron” – a close name match to the previous UK prime minister. Article-based searches for the target name were swamped with results relating to the former prime minister, but with Ripjar’s AI Risk Profiles, firms are able to explore a list of the relevant individuals, with specific risk data integrated into each profile. Simon characterised the new approach as a “holistic view” of risk that captured a range of meaningful negative news signals about an individual in order to deliver a truer profile.   

Discover Labyrinth Screening

What was clear from the Ripjar Summit is that the global risk landscape is changing, and organisations need to be prepared to meet their compliance challenges as soon as they emerge. With that goal in mind, Ripjar’s Labyrinth Screening platform is capable of searching thousands of global data sources including watchlists, sanctions lists and news stories, in over 20 foreign languages, and delivering quality, actionable intelligence in seconds. Powered by cutting edge machine learning technology, Labyrinth enables you to extract the most relevant AML data, in order to minimise noise and false positives, and build more accurate, meaningful risk profiles for every customer. 

To learn more about how Ripjar can help you manage compliance challenges, get in touch today