Hawk

Financial institutions are increasingly seeing value in AI for a range of AML/CFT compliance activities, including transaction monitoring, suspicious activity reporting, sanctions and PEP screening, and more. Data from GSMA indicates that FIs have high confidence in AI's ability to support AML/CFT programs, which leads to higher effectiveness (finding more crime and increasing risk coverage) while also increasing efficiency (reducing false positives). If you'd like to know how Hawk can help you to implement AI-powered solutions for AML/CFT, visit our website and request a demo to see how it works: https://hawk.ai/ #aml #fincrime #artificialintelligence #ai #antifraud #frauddetection #gsma #financialinstitutions

How close are financial institutions to eliminating false positives from anti-money laundering? How can AI help? Join us at the Datos Insights' Financial Crime and Cybersecurity Forum in Charlotte, NC, from August 27-28, to get answers to these questions in our session entitled 'False Positives...Will We Ever Get Rid of Them'? Our VP of Strategy, Chris Caruana (MBA, CAMS), will be covering: - How close are we to completely eliminating false positives? - The role of AI and advanced analytics in detection accuracy and false positive reduction - The impact of false positives on regulatory reporting and audit processes If you'd like to meet with us at the event, you can pre-book a meeting with us here: https://lnkd.in/deZ_ZkRN See you there! #aml #fincrime #datosinsights #fincrimeforum #banking #bankingtech #compliance #ai #machinelearning #cybersecurity #falsepositives

How can financial institutions use the power of Large Transaction Models (LTMs) to enhance their AI strategy against financial crime? Our CPO and Co-founder of Hawk, Wolfgang Berner, spoke to PYMNTS about the results that Hawk is seeing from LTMs. LTMs are generative AI models that have been adapted for anti-financial crime so that banks, payment companies and other financial organizations can prevent, detect and manage financial crime more effectively and accurately. Wolfgang explained Hawk's approach to Austin Prey at PYMNTS, highlighting: - LTMs see transactions as sentences, which means they learn language and grammar of transactions similar to how Large Language Models (LLMs) like GPT-4 learn from internet text. This allows LTMs to understand transaction behaviors so they can identify what is legitimate or suspicious - Hawk is seeing that LTMs significantly improve false positive reduction (FPR), increasing the FPR rate from 50% to 65% while maintaining accuracy - LTMs will continue to evolve and improve, in both current and additional anti-financial crime use cases Watch the video or read the article for full insight here: https://lnkd.in/d4SrgBpF #fincrime #aml #afc #ai #artificialintelligence #banks #fraud #risk #genai #largetransactionmodels #ltms

How should BaaS providers, banks and fintechs be using AML technology to manage risk and meet compliance regulations? In this article, we provide an example of how AI-powered AML technology can be used effectively by all of the participants in the BaaS ecosystem: - A community bank partners with a BaaS provider to offer a new instant payments product - The BaaS provider relies on a sponsor bank to provide licensed banking products - The sponsor bank has modern, AI-powered AML technology in place that can monitor activity down the chain - The banks and BaaS provider also use AI-powered AML technology to screen customers and payment counterparties, monitor transactions, and produce customer risk ratings (also known as perpetual KYC, or pKYC) - As transactions occur, the AI models flag suspicious activity using anomaly detection and pattern recognition - Human investigators at both banks review the AI's findings in a case manager that aggregates relevant contextual information - Simultaneously, the AI models analyze rule-based alerts and prioritize them for investigators with behavioral analytics, reducing false positives (and therefore costs) - The AI models deliver human language explanations to the sponsor banks’ investigative teams for every decision they make - The bank can now easily file suspicious activity reports (SARs) and defend their AML controls and risk-based approach to regulators in their periodic exams - Both banks and the BaaS provider have thereby unlocked a valuable partnership from the BaaS model, while protecting themselves from potential regulatory actions and reputational damage Read the full article by Chris Caruana (MBA, CAMS) that explains how the benefits of BaaS can be balanced with regulatory compliance: https://lnkd.in/dTsMeHcx Alternatively, request a demo to see how Hawk helps banks and BaaS providers to improve their AML and fraud risk coverage. Visit https://hawk.ai/ #fincrime #aml #baas #bankingasaservice #fraud #ai #artificialintelligence #financialinstitutions #banks #frauddetection #sponsorbanks #fintech

This week, the Hawks have been running instead of flying! Kudos to the team in our Munich HQ that took part in the Infront B2Run GmbH event. 'Our Team Is Why We Win' is one of our values here at Hawk, so working on our team spirit, health and wellness at the same time was a great opportunity. We're hiring, so if you would like to join a fun, growing team that is doing important work in fighting financial crime, check out our vacancies in Customer Success, Development and HR at: https://lnkd.in/dGG4sr8D #b2run #munich #teamactivity #ai #aml #fincrime #antimoneylaundering #aml #financialcrime #employeespotlight #running

How can financial institutions use Generative AI to improve their anti-money laundering, sanctions screening, and fraud prevention operations? In our recent article, we identified four key areas of opportunity: - Detecting Sanctions Violations Generative AI excels in detecting sanctions violations by understanding the semantics of language, identifying synonyms and slang terms that traditional algorithms might miss, thereby reducing false positives and improving accuracy. - Detecting Money Laundering and Fraud Activity Generative AI enhances fraud prevention by understanding complex relationships and transactional language, leading to increased detection rates and significantly reduced false positives. - Aiding Case Investigations Generative AI aids case investigations by generating high-quality case narratives and summaries based on investigation history, allowing human investigators to focus on verifying and validating the facts. - Improving QA Processes Generative AI analyzes case outcomes and narratives to identify deviations, suggest training measures, and generate internal summary reports, ensuring consistent and high-quality AML and fraud operations. Read the full article here: https://lnkd.in/dMDka2hd If you'd like to know more about how AI can help your organization to enhance the efficiency and effectiveness of your AFC operations, book a demo with us at: https://hawk.ai/ #fincrime #aml #generativeai #genai #fraud #ai #artificialintelligence #financialinstitutions #sanctionsscreening #frauddetection

The demand for AI across a range of AML/CFT use cases continues to grow. It's now a given that financial institutions are using AI to reduce false positives and negatives, as this data from GSMA shows. FIs also see value for AI in transaction monitoring and behavioral analysis, while almost three quarters are looking to AI for network analysis - and we expect that to increase rapidly, as Heads of Risk and Compliance see results from solutions like Hawk's Entity Risk Detection. Hawk's explainable AI is helping banks and payment organizations to significantly increase the effectiveness of their AML detection (find more crime) while also increasing efficiency (reduce false positives). Request a demo now to see how it works: https://hawk.ai/ #aml #fincrime #artificialintelligence #ai #antifraud #frauddetection #gsma #financialinstitutions

AI can help you to significantly reduce false positives in anti-money laundering. How? - AI applies more fine-grained rules than rules-based AML technology - AI dynamically generates a network of interrelated rules for different segments of an FI’s customer portfolio - Because AI can apply more of these contextual filters simultaneously, it weeds out more false positive alerts The Rules-Based Approach - Consider three rules, each having a 10% false positive rate, deployed to detect suspicious behavior - When we apply these rules, the total false positive rate is 30% - The rate is high because all the rules act together, and they don't choose any specific type of behavior to look at. The same rule applies to every single customer. The AI Approach - AI generates a large set of fine-grain rules that look for specific combinations of behavior - AML investigators can tailor AI much more precisely to what a particular customer does - For example, with AI we can apply five rules, each with two conditions having a false positive rate of 10% - The rules only fire if both conditions are met, resulting in a false positive rate of 1% per rule - When we combine the five rules, we get an overall false positive rate of 5% - In this scenario, we’ve applied more rules and still reduced the false positive rate significantly Imagine the resulting gains in efficiency and effectiveness when AI is applied at scale. Read the full article that summarizes the examples and how AI reduces false positives: https://lnkd.in/dkHVeu8G Alternatively, for a demo on how Hawk uses explainable AI to help banks and payment providers to reduce false positives while increasing risk coverage, visit: https://hawk.ai/ #aml #fincrime #acams #banking #bankingtech #payments #compliance #ai #machinelearning

What are the pitfalls that you need to avoid when you're implementing AI to detect financial crime? Our Chief Solution Officer, Michael Shearer, has highlighted a number of things that should be avoided during implementation: - Rushing data collection - Training too soon - Training on insufficient casework Michael provides more advice from his personal experience of implementing AI at a global bank in this article: https://lnkd.in/eqKCWmfT If you'd like to know more about how Hawk can help your organization use AI for AML and fraud detection, book a demo with us today: https://hawk.ai/ #aml #fincrime #banking #financialinstitutions #compliance #ai #machinelearning #aiimplementation

How does our latest product innovation, Entity Risk Detection, help financial institutions to identify cases of sanctions evasion? In this scenario: - An alert is triggered for a customer - Entity Risk Detection has identified another customer who shares the same date of birth, email, and phone number - The system updates the risk rating based on these multiple attribute risk factors - An investigator opens the case and, with the help of Entity Risk Detection, determines that the two customers are the same person using different aliases, indicating potential sanctions evasion - The financial institution implements new controls for the customer based on the updated risk score Read more here: https://lnkd.in/e6vmN5my Want to know how Hawk's new solution, Entity Risk Detection, can help you to identify hidden financial crime with maximum efficiency? Request a demo today and find out: https://hawk.ai/ #aml #antimoneylaundering #fincrime #banking #payments #sanctions #fraud #entityresolution #datamanagement