Italy is a wealthy Mediterranean nation with one of the largest economies in Europe and a historically-strong manufacturing industry. A busy trade hub, Italy’s international businesses export products, including cars, furniture, food, clothing, and luxury goods, around the world.
While Italy’s economic development created prosperity, it has also attracted criminals who exploit the country’s financial system to launder money and commit other crimes. That criminal threat is ongoing: in a 2023 Europol investigation, Italian authorities discovered criminal gangs perpetrating trade-based money laundering across Europe, with around €18.5 million traced back to Italy alone. Later, Italian authorities uncovered a money laundering network between Italy and China, with prosecutors seizing €292 million in illegal funds.
Italy’s government has responded to the money laundering threat by implementing strict anti-money laundering (AML) and counter-financing of terrorism (CFT) regulations, in keeping with its EU and global obligations. With money laundering still a serious threat, it’s important that firms understand Italy’s AML regulations, and how to achieve compliance.
Italy’s AML Regulator: The Bank of Italy
The Bank of Italy is Italy’s primary AML regulator and provides supervision for all banks and financial institutions in the country, including asset management companies, intermediaries, and trusts. Headquartered in Rome, the bank functions to “ensure the monetary stability and financial stability” of the Italian economy, and has the following duties and responsibilities:
Preparing and developing AML/CFT regulations in coordination with the Italian parliament and government.
Developing methods to assess and analyse AML/CFT compliance in supervised banks and financial institutions.
Implementing penalties and sanctions on entities found to be violating AML/CFT compliance rules.
Conducting periodic analysis of AML/CFT risks across the financial sector.
Publishing and disseminating documentation pertaining to AML/CFT regulation.
Participating in international AML/CFT efforts with foreign regulatory counterparts, including strengthening cross-border AML/CFT supervision.
There are other regulatory bodies that are tasked with supervising Italy’s financial institutions. These are:
Italy is also a member of the Financial Action Task Force (FATF), the intergovernmental organisation that sets global AML policy. The FATF issues AML recommendations that must be implemented as part of domestic law.
Italy’s Key AML Regulations
Italy’s main AML regulation is Legislative Decree No.231 2007, which sets out the definition of the crime of money laundering in Italy, and the need for cooperation between financial institutions and authorities in ensuring compliance. The Decree requires firms in Italy to take a risk-based approach to AML/CFT compliance (as prescribed by the FATF), which means that they must assess the criminal risk that their customers pose, and then implement a proportionate compliance response.
Anti-Money Laundering Directives: As a member of the EU, Italy must implement the EU Parliament’s Anti-Money Laundering Directives (AMLD) in domestic law. Accordingly, Italy periodically updates Legislative Decree No.231/2007 in order to meet the EU’s new AML standards. The latest EU AMLD was the Sixth Anti Money Laundering Directive (6AMLD) which came into effect on 3 June 2021.
How to Comply with Italy’s AML Regulations
In order to meet the requirements of risk-based AML regulations, firms in Italy must implement the following measures and controls:
Customer due diligence: Firms should seek to establish and verify the identities of their customers in order to perform accurate risk assessments. The customer due diligence (CDD) process may involve the submission of names, addresses, and other information, including biometric identifiers.
Beneficial ownership checks: Firms in Italy should also carry out beneficial ownership checks on customer entities in order to prevent criminals hiding their identities with shell companies or behind corporate infrastructure.
Transaction screening: Firms must be able to screen their customers’ transactions for money laundering risk indicators such as unusual transaction patterns, or transactions with high risk counter-parties.
In a risk-based compliance system, it is vital that firms capture and understand the level of risk that individual customers present. One of the most effective ways of doing this is to screen for adverse media that involves their customers, since news stories often contain valuable information about AML risk before it is confirmed by official sources such as government or police departments.
Adverse media screening (or negative news screening) requires searches of domestic Italian and global news stories in multiple languages. Searches should cover screen and print media, along with new media formats, such as blogs, social media posts, and forums. The screening process can be complex, and must account for factors such as language differences, content duplication, platform credibility, linguistic idiosyncrasies, nicknames, and aliases.
Recent AML Initiatives in Italy
The Bank of Italy has a dedicated ‘Notices and communications’ page on which it publishes the latest news, events, and developments pertaining to AML regulation in Italy. In April 2023, the Bank of Italy held its “New AML scenarios” workshop, in which it facilitated a discussion with trade representatives on “the main challenges that developments in policies, risks, and anti-money laundering supervision pose to the Authorities and intermediaries”.
Strategic Plan: The Bank of Italy also recently released its Strategic Plan 2023-2025. The Plan included several significant AML provisions, including the establishment of a new Anti-Money Laundering Supervision and Regulatory Unit (SNA), and a commitment to reorganise and strengthen Italy’s Financial Intelligence Unit (FIU).
AMLA: In March 2023, the EU revealed that it would be establishing a new European Anti-Money Laundering Authority (AMLA) to help enforce and standardise AML/CFT regulations across member states. In October 2023, the Italian government announced that it would be bidding to host the AMLA headquarters in Rome.
Next Generation AML Screening in Italy
Building an effective AML solution in Italy requires firms to not only collect and understand the risk data they collect, but to use it to act decisively. Screening processes, especially adverse media screening, often generate vast amounts of data, which can, in turn, create high volumes of false positive alerts, slowing down the compliance process and placing significant pressure on employees. To meet this challenge, firms need efficient, agile, automated screening solutions that can adapt to changing risk landscapes without compromising performance.
Ripjar’s Labyrinth Screening platform is designed for exactly this purpose. Powered by cutting-edge machine learning algorithms, Labyrinth Screening offers customisable adverse screening tools and powerful adverse media name search capabilities in over 25 languages. Labyrinth screens against thousands of global news sources, watchlists, and sanctions lists, and puts actionable financial intelligence at your fingertips in seconds.
The Labyrinth platform also adds valuable depth and detail to your screening process with the integration AI Risk Profiles technology. Automatically identifying and extracting only the most relevant data on subject entities, AI Risk Profiles enables firms to build out individual profiles for subject entities, eliminating duplicate content, similar names, and other data that typically increase the chance of a false positive alert, and ensuring your compliance team is in the best position to make strong risk decisions.
We’re proud to have been placed as a category leader in the Chartis RiskTech Quadrant for KYC Solutions, 2023.
Chartis Research is the leading provider of research and analysis on the global risk technology market, providing in-depth analysis and advice on all aspects of risk and compliance technology. As part of this, Chartis produces reports on risk management solutions for financial crime, including Know Your Customer (KYC) solutions.
Currently, the global KYC and anti-money laundering (AML) landscape is shifting, with increasing sanctions and regulations affecting all sectors, and particularly the global supply chain. This is leading to organisations implementing more rigorous screening methods to improve due diligence processes.
In the US, for example, recent regulation changes – such as new sanctions relating to the Russia/Ukraine war, FinCEN’s final rule on beneficial ownership information, and the US Treasury’s National Strategy for Combating Terrorist and Other Illicit Financing – have had a significant impact on due diligence requirements. When combined with increasingly complex supply chains and transactions, the number of companies needing to implement more robust KYC solutions is increasing.
The Chartis KYC Solutions quadrant assesses against capabilities such as customer onboarding, reporting and dashboarding, and customer profile enrichment with additional data – an area in which Ripjar’s industry-leading AI Risk Profiles excels.
With solutions assessed on the completeness of their offering and their market potential, Ripjar’s high score in both areas has placed us as a category leader.
Chartis RiskTech100 2024
Also announced this week, Ripjar has once again been included in the Chartis RiskTech100 – a ranking of the world’s 100 most important players in risk and compliance technology. In the 2024 rankings, we’re proud to have risen a further 10 places from last year, demonstrating our ongoing commitment to innovation and helping our customers stay ahead of the threat.
Discover how Labyrinth Screening can help ensure your AML Compliance
Ireland is a prosperous, northwestern European nation with a highly developed economy, including successful international technology, science, and financial service industries. Ireland’s global business profile has also led to financial crime risks: in 2021, Irish police revealed that they had recorded over 500 money laundering crimes in 2020, more than double the amount in 2019, and a sixfold increase in two years. To address that threat, the Irish government has committed to bolstering the powers and resources of authorities to fight financial crime and in particular to address offences such as money laundering and terrorism financing.
The increased focus on anti-money laundering (AML) and counter-financing of terrorism (CFT) regulations means that organisations in Ireland must understand the risk landscape, and be capable of achieving compliance with the relevant regulations. Prepare your organisation for criminal threats, and stay ahead of your compliance obligations with our guide to Ireland’s AML regulations.
Ireland’s AML Regulator: The Central Bank of Ireland
Founded in 1943, and headquartered in Dublin, the Central Bank of Ireland (CBI) is Ireland’s primary anti-money laundering regulator and is responsible for “effectively monitoring credit and financial institutions’ compliance with their AML and CFT obligations”. The CBI’s duties and responsibilities include:
Providing oversight for Ireland’s financial institutions to ensure compliance with AML/CFT regulations.
Conducting on-site inspections to verify that financial institutions are meeting their regulatory compliance obligations.
Monitoring adoption and implementation of risk-based AML/CFT compliance procedures.
Ensuring that financial institutions keep AML/CFT compliance policies and procedures up to date and available for inspection, and that senior management are aware of their own compliance responsibilities.
Enforcing administrative sanctions against financial institutions that fail to comply with their compliance obligations.
As a national regulatory body, the CBI plays a role in the development of new financial regulations with the Irish government. Similarly, the CBI works with other European Supervisory Authorities (ESA) in order to foster a consistent approach to anti-money laundering regulations across the EU. Ireland is also a member of the international intergovernmental AML organisation, the Financial Action Task Force (FATF).
The CJA 2010 defines the offence of money laundering in Ireland and requires all financial institutions in Ireland to implement a risk-based AML/CFT solution. Key CJA 2010 compliance measures include customer due diligence (CDD), customer screening processes, suspicious activity reporting (SAR) mechanisms, the appointment of a competent compliance officer, and the implementation of employee training.
In addition to the CJA 2010, Ireland has passed several additional articles of AML/CFT-relevant legislation, these include:
The Criminal Justice (Terrorist Offences) Act 2005
The European Union (Anti-Money Laundering: Beneficial Ownership of Corporate Entities) Regulations 2019
The European Union (Anti-Money Laundering: Beneficial Ownership of Trusts) Regulations 2019
The European Union (Information Accompanying Transfers of Funds) Regulations 2017
The CJA 2010 requires that firms in Ireland implement a risk-based AML/CFT compliance solution. In this context, risk-based compliance means that firms must conduct a risk assessment of customers at onboarding to establish the level of individual AML risk that they present, and then deploy proportionate compliance measures.
A CJA 2010 compliance programme should include the following measures and controls:
Customer due diligence: Firms in Ireland must identify their customers by collecting identifying information, including names, addresses, and dates of birth. Firms must also identify ultimate beneficial owners (UBO) in order to prevent the misuse of shell companies.
Transaction screening: Firms must screen customer transactions in order to detect suspicious activity, such as unusual transaction patterns, transactions with high risk counter-parties, or transactions involving high risk jurisdictions.
Watchlist screening: Firms should screen customers against relevant watchlists, including PEP lists.
Adverse media screening: In order to understand customer risk levels as comprehensively as possible, and fulfil the CJA 2010’s risk-based obligations, firms in Ireland should implement an adverse media screening solution. Adverse media is valuable to AML because risk-relevant information is often revealed by media sources before it is confirmed officially, meaning that firms can perform more accurate risk assessments and deliver better compliance outcomes.
Accordingly, adverse media screening solutions should take in sources from Ireland and around the world, including foreign language media, and cover everything from stories by established news outlets to blog posts, forum entries, and social media posts. Adverse media solutions should be capable of multi-language searches, account for regional variations in spelling, non-Western characters and conventions, and factor in the credibility of the sources.
Recent AML Initiatives in Ireland
In addition to the TFR, Ireland will also implement another EU virtual asset regulation: Markets in Crypto Assets (MiCA). A landmark, EU-wide regulation, MiCA will address the AML risks posed by certain crypto-assets, and introduce new licensing and registration requirements for crypto-asset service providers. MiCA is expected to come into effect across the EU in 2024.
In 2023, Ireland announced that it would be bidding to host the new EU Anti-Money Laundering Authority (AMLA). The bid reflects Ireland’s desire to increase its profile as a European financial centre, and international AML leader.
Next Generation AML Screening
As Ireland’s AML landscape evolves, financial institutions will need to work harder to keep up with new regulatory obligations and to address new criminal methodologies. In this environment, automated screening solutions are critical: firms must be able to collect and analyse vast amounts of customer data, while minimising false positives and making decisions quickly.
Ripjar’s Labyrinth Screening platform is designed to meet those challenges – in Ireland and jurisdictions around the world. Labyrinth Screening enables firms to search customer names against thousands of global media sources, including PEP lists and sanctions lists, in real time, and generate actionable financial intelligence in seconds. Built with next generation machine learning technology, Ripjar has also implemented AI Risk Profiles as part of Labyrinth searches: using AI Risk Profiles, firms can quickly identify and extract only the most relevant risk information about their customers, speeding up the screening process, enhancing accuracy, and ultimately enabling faster, stronger compliance decision-making.
Labyrinth Screening now supports four new Nordic languages: Norwegian, Danish, Swedish and Finnish. This takes the total number of supported languages to 26.
Rather than using translations, Labyrinth Screening carries out multilingual screening, whereby all documents and articles are screened natively in the language in which they are published, removing the risk of nuance or context being lost. While the platform was already screening versions of Nordic news stories which had been published in English or other supported languages, this new language update will enable customer AI Risk Profiles to benefit from an even wider range of data sources.
Why does Labyrinth Screening’s Nordic language update matter?
Recent spikes in financial crime in Nordic countries have meant that the ability to screen natively in those languages has increased in importance in combatting financial crime risk and ensuring Nordic AML compliance.
The addition of the new Nordic languages to Labyrinth Screening means we can now use a broader set of data sources – both from web-scraping and formal sources of data – providing more data points and richer data to enhance our AI Risk Profiles and provide greater screening confidence.
Not only can new media articles be processed in Nordic languages, but Labyrinth Screening can now also reprocess older Nordic-language documents already held in the platform’s data store, and use the full range of analytics on them. By supplementing existing risk profiles with any additional risk data or context extracted from these sources, this new Nordic language capability enables Labyrinth Screening to add even more value to our customers.
Over 63 million articles have already been processed in Norwegian, Danish, Swedish and Finnish since implementing these languages within Labyrinth Screening, with hundreds of new articles being added every day.
Within Labyrinth Screening, all our analytics are carried out in the source language of each individual news article or document, so as not to lose the nuance of the original language. The addition of these extra languages into the platform is therefore hugely valuable to any of our customers who are screening Nordic entities, as it ensures no context is lost by translating first.
How is Ripjar’s name screening different?
While other platforms may translate articles and documents before screening them, Labyrinth Screening only processes documents in the language in which they were written, ensuring it provides the most accurate, reliable results, with no loss of context.
In addition to its multilingual name-matching capabilities, Labyrinth Screening is also an industry leader on multi-script language screening, with the ability to screen languages with non-Latinate characters, such as those using Cyrillic, Arabic or logographic alphabets. It can also undertake transcription between languages and transliterate customer names from their native script into Latin characters – including the potential name variations this creates – ensuring the best possible identity-matching.
For example, Finnish has a particularly complex system of declensions – changes to a name’s representation that indicate properties like gender or grammatical case – with 51 types of declension, each with seven different grammatical cases. A Finnish surname like “Hautamäki” could be represented as diversely as “Hautamäen”, “Hautamäelle”, or “Hautamäkeä”, depending on the context:
Labyrinth Screening provides extensive declension handling, allowing us to correctly infer the nominative case – the base form of the name – more than 90% of the time, covering all the major declension types in each of the seven cases. This is essential for effective adverse media screening, as people and organisations are regularly mentioned with different declensions of their names in the news.
What are Nordic adverse media screening requirements?
As part of their EU membership, Denmark, Sweden and Finland (and Norway, as part of the EEA) are required to comply with the EU’s Sixth Anti-Money Laundering Directive (6AMLD) as well as their own national AML regulations.
Regulatory compliance in the Nordics involves the implementation of customer due diligence processes, transaction monitoring, sanctions and watchlist screening, and adverse media screening. For example, Denmark’s financial regulator, the Danish Financial Supervisory Authority, has risk management guidelines which state, “It is essential that financial institutions include media screening of customers and beneficial owners in customer due diligence.”
Also referred to as negative news screening (NNS), adverse media screening forms a vital part of a risk-based approach to compliance, as news and other media sources often highlight potential risk relating to people and organisations before it is officially confirmed. This early detection of risk is valuable in many ways, from anticipating criminal activity to avoiding reputational damage.
The addition of Norwegian, Danish, Swedish and Finnish languages into Labyrinth Screening – and the resulting ability to screen natively in these languages – is therefore another vital way to ensure that Nordic adverse screening requirements are met as robustly as possible and with the greatest accuracy.
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
