Agefi Luxembourg - janvier 2025

Janvier 2025 39 AGEFI Luxembourg Digital ByOrianeKAESMANN,ResearchManager the LHoFT T he financial sector is confronting significant chal- lenges in combatingmoney laundering, exacerbatedby the rapid increase innon-cash transac- tions, which reached 1,3 trillion globally in 2023 (1) and are projected to reach 2.3 trillionby 2027, with a growing rate of 15%annually. This surge is driven by the proliferation ofdigitalpaymentsandinnovativefinan- cial services. Concurrently, money laun- dering schemes have become more sophisticated, with criminals exploiting advanced technologies and complex financial instruments to obscure illicit activities. Traditional rule-based AML systems are increasingly inadequate, generating high false-positive rates that burdencompliance teamswithresource- intensive investigations and failing to adapt toevolving laundering techniques. These inefficiencies underscore the urgent need for innovative approaches, such as AI-driven solutions (2) , to effecti- vely combat global financial crime. TheGeneral Role ofAI inModernisingAML TraditionalAMLsystems face significant challenges due to their reliance on static, rule-based mechanisms and manual processes. These systems often struggle to keep pace with the evolving sophisti- cationof illicit financial activities, suchas layering and structuring, which are de- signed to evade detection. This static ap- proach results inhigh false-positive rates, overwhelming compliance teams with alerts that require extensivehumaneffort to investigate, thereby diverting re- sources from genuine threats (3) . More- over, the cost-benefit imbalance is stark; financial institutions incur substantial compliance costs, yet the recovery rates of illicit funds remain low, highlighting the inefficiency of traditional AML frameworks (4) . These limitations under- score the urgent need formore dynamic and adaptiveAML solutions that can ef- fectively address the complexities of modern financial crime. Key Innovations Brought byAI AI is transforming AML frameworks by integrating advanced technologies that collectively tackle the complexities of modernfinancial crime.At the coreof this transformationisMachineLearning(ML), which enhances anomaly detection through adaptive algorithms capable of learning from vast, diverse datasets. By identifying subtle patterns and be- haviours,MLsignificantlyminimisesfalse positives, enabling compliance teams to concentrate on genuine risks rather than expending resources on irrelevant alerts. Graph analytics further strengthens de- tectioncapabilitiesbyfacilitatingtheanal- ysisofintricatetransactionnetworks.This method uncovers hidden connections and suspicious clusters that might other- wise remain undetected. Notably, graph analytics can reveal “unknown un- knowns”—newandunexpectedfinancial crime patterns—allowing institutions to identifyandmitigateemergingthreatsbe- fore they escalate. Underpinning these advancements is High-Performance Computing (HPC), whichensuresAIsystemscanscaletopro- cessbillionsofdatapointsefficiently.HPC provides the speed and computational powerneededtoanalyselarge-scale,com- plextransactiondatainreal-time,allowing institutions tomonitor and respond to fi- nancial crime as it unfolds. The Broader Impact The integration of AI into AML frame- works brings about transformative changesthatgofarbeyondimmediateop- erational gains. By automating intricate detection processes and streamlining in- vestigative workflows, AI dramatically improves efficiency, cutting down both investigation time and associated costs. This enables compliance teams to allocate theireffortstohigh-prioritycases,enhanc- ing overall accuracy and effectiveness. AI’scapacitytoanalysefinancialactivities in real time introduces continuous moni- toring and facilitates the detection of emerging money laundering patterns. This proactive approach allows institu- tions to anticipate and address evolving criminal tactics, rather than merely react- ing after they occur. Moreover, standardised and scalable AI toolsfostergreaterglobalcollaboration.By ensuring consistency and interoperability across institutions and jurisdictions, these technologies enable seamless information sharingandcoordinatedeffortstocombat money laundering on a global scale. Mopso’s Innovative Contribution toAI-DrivenAML Andrea Danielli, CEO and Founder of Mopso (5) , presents their solution: Combining Social Network Analysis and AI for Continuous RiskMonitoring Mopso,incollaborationwiththeLIST,de- veloped the project PAMLA (Performant Anti Money Laundering Analytics), which has been granted a HPC bridge fromtheMinistryof Economy.We aimat improving network analysis in the trans- action monitoring domain, through the developmentoftechniquesforidentifying specificcrime-relatedtraitsinthetopology of the network and associated attributes. Thesoftwarewillidentifyandcharacterise clusters looking for network motifs and other features that map to established crime patterns, as defined by the authori- ties. Secondly, if we can dispose of real banking data, we will look for the un- known-unknown, i.e., we will test many machine learning techniques like graph neural networks and deep neural net- works, to explore unknown connections’ patterns not yet addressed by the actual transactionmonitoring systems. Centralising Data for a Comprehensive View of CustomerActivity Mopsousessemanticwebtechnologiesto integrateaverylargenumberofinforma- tionsourcesthatcanbeofdifferentnature: internal to the financial institution, com- ing from open data, fromdata providers and from open-source intelligence. This means that, for every customer, the solu- tion finds and combines up to 200 data sources, comparing data in between dif- ferenttimeintervals.Onceelaborated,this data could produce specific alerts, called “scenarios”, which create the customer’s individualmoneylaunderingriskprofile. Thenthetechnologyisabletocombinein- dividualriskprofilesintoabiggerpicture, thanks to network analysis, making it possible to spot profiles that, at first glance, seem legitimate. Prefilled SARs andMore WeusedifferentAItechniquesinoursoft- ware:theexploratorypartislinkedtofully explainable rule-basedalgorithms; on top of the analysis results we then use a layer of LLMto summarise the results or guide analysts in the necessary insights. The combination of the solutions allows to in- tercept, analyse and summarise the activ- ities at risk ofmoney laundering. TransformingAMLWorkflows Some preliminary results, prior to the PAMLAproject, allowedus to identifyas suspicioussomeoperationsthatnotrans- action monitoring system had inter- cepted. In one exemplary case, we succeededbecauseweidentifiedpatterns thatinvolvedseveralsubjects,bothItalian and foreign, all traceable to the samepiv- otal subject, sometimes implicated as an administrator, sometimes as a partner. We noted that this technology is very strong in combating the so-called shell companies, widely used in the field of money laundering as they are easy to open, operate and allow the laundering of huge amounts of money. Conclusion The financial sector stands on the cusp of a transformative era, asAI offers ground- breaking tools to combatmoney launder- ing. Traditional methods, constrained by staticrulesandplaguedbyhighfalse-pos- itive rates, struggle to keep pace with in- creasingly sophisticated financial crimes. AI-powered solutions enable real-time monitoring, reveal hidden patterns, and craft comprehensive customer risk pro- files, ushering in a proactive approach to tacklingfinancial crime. Themomentfordecisiveactionishere.As the industry turns toAI, financial institu- tions have an opportunity to lead by adopting these transformative technolo- gies, enabling amore transparent and re- silient financial ecosystem. Embracing cutting-edgeinnovationshelpsstrengthen compliance, and positions organisations as frontrunners in the fight against finan- cial crime in this newage ofAI. 1)Capgemini(14Sep2023)“Globalnon-cashtrans- actionvolumessettoreach1.3trillionin2023” https://lc.cx/-9ByhC 2)EuropeanInstituteofManagementandFinance (EIMF)“TheImpactofArtificialIntelligenceinAnti- MoneyLaundering ”https://lc.cx/C1c51h 3) Team Sanction Scanner (19 July 2024) “The Fu- tureofAnti-MoneyLaundering:TrendsandTech- nologies ”https://lc.cx/ZzlLkY 4) Raditio Ghifiardi (November 9, 2024) “The Ur- gency of AI in Anti-Money Laundering and Counter-Terrorism Financing: A Global Impera- tive ”https://lc.cx/9SHCDt 5 )https://www.mopso.eu/ The Age of AI Detection AMLTransformation ©Midjourney By Michel KABANGA KAYEMBE, CEO of 3nity-global.com A s the volume of data genera- ted by digital banking apps continues to grow, ensuring that no customer voice is left unheard has become a critical priority for fi- nancial institutions. By har- nessing the power of AI-driven platforms built onNatural Language Pro- cessing (NLP) and Large LanguageModels (LLMs), institutions can achieve ope- rational excellence and signifi- cantly enhance customer engagement. These advanced tools enable banks to proactively address user concerns, improve satisfaction rates, and deliver seamless digital experiences. This updated analysis, powered by detailed in- sights, evaluates how financial institutions in Bel- gium, Luxembourg, and France are executing their digital strategies. It identifies the leaders ex- celling in satisfaction and responsiveness and highlights opportunities for those lag- gingbehind to leverage technology for bet- ter results. Leaders: High Performers inDigital Banking France: Delivering Excellence Ma Banque from Credit Agricole app demonstrated exceptional performance, achieving 83% positive re- views—the highest in this anal- ysis. With an average response time of just 0.4 days, it under- scores the value of swift engagement in maintaining high levels of customer satisfaction. However, a 43%non-response rate reveals roomfor growth in proactively addressing user concerns. Belgium: Reliable and Engaged Argenta Banque maintained a solid reputation for reliabilityanduser satisfactionwith76%positive re- views, supported by an industry-leading 3% non- response rate, highlighting a proactive approach to user feedback. KBC app achieved 60% positive reviews, with a 2.6-day response time, demonstrating steady per- formance despite a 17% non-response rate. These institutions exemplify the success of customer-cen- tric design paired with timely and effective en- gagement. Laggards: Where Improvement is Needed Luxembourg: Progress and Pitfalls Luxembourg’s digital banking apps showedmixed results. LuxTrust app achieved a respectable 53% positive review rate, reflecting efforts to enhance functionality. However, a 100% non-response rate signals a lack of engagement in addressing user feedback, a critical shortcoming in fostering trust and loyalty. France: Persistent Challenges Several Frenchapps failed tomeet user expectations, with 0% positive reviews and 100% negative feed- back. Response times ranging from 4.1 to 6.0 days andnon-response rates exceeding 50%indicate sig- nificant gaps in customer engagement. Banxo app, with a 77% non-response rate, stands as a stark re- minder of the impact of neglecting user feedback. Belgium: Room for Growth Crelan recorded 0%positive reviews, coupledwith a 100%negative review rate. Although its response times averaged 4.3 days and its non-response rate was relatively low at 40%, the lack of user satisfac- tion highlights the need for enhanced usability and a stronger feedbackmechanism. Insights: Data-DrivenTrends andKey Success Factors Thisdetailedanalysis,integraltoKAM-XF’splatform offering, reveals three critical factors that separate leaders fromlaggards in the digital banking space: 1. Customer-Centricity :Appswith seamless naviga- tion,intuitiveinterfaces,anddependablefunctionality consistently achieve higher satisfaction rates. 2. Proactive Engagement : Institutions that prioritize rapidresponses to feedback—within5daysor less— build stronger customer loyalty. The top-performing app,witharesponsetimeof0.4days,exemplifiesthis. 3. Feedback Management : Apps with low non-re- sponseratesaremorelikelytoretainusertrust.Ignor- ing feedback, as evidenced by apps with 100% non-response rates, directly correlateswithpoor user satisfaction. Conclusion: HowInstitutions CanExcel in theDigitalAge KAM-XF’sanalysisunderscoresthatexecutingasuc- cessfuldigitalbankingstrategyrequiresmorethanjust functional apps, it demands proactive engagement, swiftresponsiveness,anduser-centreddesign.France and Belgium provide examples of excellence, with appsthatlistentoandactonthevoiceofthecustomer leadingthepack.Forappslaggingbehind,addressing usabilitychallenges,reducingnon-responserates,and improving responsiveness are critical steps to stay competitive. Thedatahighlights that institutions em- bracing real-time feedback mechanisms and cus- tomer-centric digital strategies are better equipped to succeed in the evolvingfinancial ecosystem. ByleveragingKAM-XF’sinsightsandanalytics,insti- tutions can align their strategies with the needs of today’s digital-first users, ensuring sustainedgrowth and customer loyalty. Digital Banking Strategies in Action: LeveragingAI to Empower Customer Engagement in Belgium, Luxembourg, and France