{"id":3235,"date":"2023-09-25T09:45:10","date_gmt":"2023-09-25T07:45:10","guid":{"rendered":"https:\/\/lyfense-pro.mrmilu.com\/illuminating-and-stopping-fraud-with-artificial-intelligence\/"},"modified":"2026-03-24T11:21:25","modified_gmt":"2026-03-24T10:21:25","slug":"illuminating-and-stopping-fraud-with-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/lyfense.com\/en\/resources\/illuminating-and-stopping-fraud-with-artificial-intelligence\/","title":{"rendered":"Highlighting Fraud with Artificial Intelligence"},"content":{"rendered":"\n<h3>Current state of play<\/h3>\nWe\u2019re in a transition period where society is almost expecting <a href=\"https:\/\/uncutmakers.com\/clientes\/lynxstage\/solutions\/lynx-fraud-prevention\/\" target=\"_blank\" rel=\"noopener\" rel=\\\"noopener\\\" target=\\\"_blank\\\">Artificial Intelligence services<\/a> as part of \u00a0their daily lives. There is great value shown in uses such as the large language models of ChatGPT4.0. However in Fraud Prevention solutions there is hesitancy to trust machine learning models, due to many reasons:\n<ul>\n \t<li><strong>Lack of understanding<\/strong>\u00a0in how the model works<\/li>\n \t<li><strong>Lack of trust\u00a0<\/strong>in the scores provided by the model<\/li>\n \t<li><strong>Lack of data\u00a0<\/strong>available to train the model<\/li>\n \t<li><strong>Bad historical performance\u00a0<\/strong>prior generations of fraud solutions using \u201cAI\u201d but with poor performance<\/li>\n \t<li><strong>Misconceptions\u00a0<\/strong>generally around how Machine Learning works and its limitations<\/li>\n<\/ul>\nThese are very valid reasons and you the reader are right to be cautious in using AI in fraud prevention solutions.\n\nToday I aim to provide information that will help answer some of the questions you might have about AI and build trust in Lynx Fraud Prevention.\n\nFirst let me explain that not all solutions that \u201cutilise AI\u201d or \u201cuse Machine Learning\u201d actually use it, or at least use it in a meaningful way.\n\nUltimately, any solution that makes use of AI should be able to confidently perform a proof-of-concept and outperform any rules based solution. To give you an idea, Lynx Fraud Prevention outperforms known leading rules based solutions by a factor of up to 100.\n\nThat means 10 times less alerts with 10 time better accuracy, meaning we give you less alerts and catch more fraud. We will gladly prove this to you in a POC, as will any vendor that is truly using AI in their solution, as it\u2019s simple to do.\n\nSo how do we utilize AI in the Lynx Fraud solution?\n<h3>Next Generation Fraud Prevention<\/h3>\nWe have taken very careful design decisions all with the aim of enabling customers to address any type of fraud they face. That means we can ingest any data, generate any feature and provide you with the best machine learning models for identifying fraud and any rule.\n\nWe have state of the art in-memory databases, algorithms, and low level code in our solution which makes it lightening fast whilst keeping the costs down.\n\nSo what does a next generation AI solution look like?\n<ul>\n \t<li><strong>Data agnostic approach<\/strong>\u00a0meaning we can ingest any type of event, transaction, file you need to protect<\/li>\n \t<li><strong>Build any feature<\/strong>\u00a0(intelligence points used by a model) meaning we have very\u00a0<strong>comprehensive intelligence<\/strong><\/li>\n \t<li><strong>Auto feature generation\u00a0<\/strong>means we automatically classify data and generate relevant features.<\/li>\n \t<li><strong>Realtime in memory feature calculation<\/strong>\u00a0meaning our models always have up to date data<\/li>\n \t<li><strong>Daily Adaptive Models<\/strong>\u00a0meaning the models do not drift and stay relevant to new attacks and products.<\/li>\n \t<li><strong>Monitor the performance<\/strong>\u00a0of the model to bring you confidence that it is outperforming the competition and massively outperforming any rules based solution.<\/li>\n \t<li><strong>Apply supervised learning,<\/strong>\u00a0which outperforms unsupervised learning by up to a factor of 10.<\/li>\n \t<li><strong>Genuinely multichannel\u00a0<\/strong>meaning we are not blindsided by cross channel attacks and are able to identify attacks traversing multiple channels.<\/li>\n<\/ul>\nNot only do we do this, we also return a result to the calling application in just 25 milliseconds and enable you to blend this score, if you wish into a rule.\n\nThis means you can use the score on its own, as many of our customers do. And\/or if you want to, you can blend the score with rule logic to reduce the amount of false positives received from the rule. Remember this will reduce false positives by a factor of up to 100. That\u2019s up to 100 times less work for you and your team for the same amount of fraud identified.\n<h3>Legacy Solutions<\/h3>\nWhat does a legacy AI solution look like?\n<ul>\n \t<li><strong>Claim to use AI<\/strong>\u00a0<strong>however they do not use labelled data<\/strong>, remember supervised learning can reduce false alerts by a factor of 10, versus unsupervised learning which doesn\u2019t use labelled data. So, if you see another solution utilizes unsupervised learning, that\u2019s up to 10 times more work for you and your team for the same amount of fraud. Unsupervised learning has it\u2019s benefits for example in facial recognition, image recognitition, anomaly detection, nlp and other types of tasks where identifying data types and classifying data is onerous. However it should not be used as a main approach in fraud prevention, as it is inefficient as the main approach to identify fraud. Instead it can compliment a performant solution with additional atypical interaction and transaction identification.<\/li>\n \t<li><strong>Claim to train their models regularly but do not and need a data science team.<\/strong>\u00a0We train our models every day and have been doing so with top financial institutions automatically for years. We do this without the need for a data scientist on your side. This means that we not only outperform other solutions claiming to use AI, but we you will be able to identify solutions that do not truly know how to apply machine learning i.e. those that require you to uplift your data every quarter and manually retrain your models.<\/li>\n \t<li><strong>Professional Services costs are kept low<\/strong>\u00a0as you don\u2019t have the professional services overhead or the data science cost on your side to keep the model relevant.<\/li>\n \t<li><strong>Rules based.<\/strong>\u00a0This means that every new attack needs to be studied and identify how it beats the current rule set. Not only that but attackers are able to interpret rules and logic based on machine learning attacks and beat the rule set. Ultimately a rule set is not adequate to defend against todays advanced attacks.<\/li>\n \t<li><strong>Claim to continuously learn<\/strong>, yet when you review the data that the models are using you realize there is a delay of a day. Meaning you and your customers are vulnerable to attacks until the new data is available.<\/li>\n<\/ul>\n<h3>Attacks we can identify<\/h3>\nLynx fraud prevention can identify and stop the following attacks:\n<ul>\n \t<li>Merchant Fraud<\/li>\n \t<li>Account Takeover<\/li>\n \t<li>Authorised Push Payment Fraud (APPF)<\/li>\n \t<li>Social engineering based attacks<\/li>\n \t<li>Phishing victims<\/li>\n \t<li>Skimmed cards<\/li>\n \t<li>Replayed transactions<\/li>\n \t<li>Stolen identity<\/li>\n \t<li>And more<\/li>\n<\/ul>\nWe do this by understanding your users better than the competition. We deeply understand the device, user behaviour, locations, travel, spend, patterns of interaction, their associated beneficiaries, how much money they typically transfer\/ spend and when.\n<h3>Why don\u2019t you give us a try?<\/h3>\nWe were born out of data and data science. We live and breathe data, algorithms, insight and intelligence. We ensure that:\n<ul>\n \t<li>Our models are the best in the business<\/li>\n \t<li>We reduce your costs by reducing false positives<\/li>\n \t<li>We reduce fraud<\/li>\n \t<li>We reduce the complexity of rule building<\/li>\n \t<li>We improve job satisfaction and alert fatigue by giving you meaningful alerts<\/li>\n \t<li>We continuously learn to changing attacks and new products \/ customer behaviour<\/li>\n \t<li>We ensure our models are drift and attack resistant<\/li>\n<\/ul>\nWe\u2019re confident that we\u2019re able to stop the attacks you face and have been doing so for the last 20 years. Don\u2019t take our word for it have a look at other financial institutions that love our solution.\n\nWe\u2019re the AI Solution you\u2019ve been patiently waiting for since they came onto the scene.\n\nSo why don\u2019t you reach out and ask for a<a href=\"https:\/\/uncutmakers.com\/clientes\/lynxstage\/request-a-demo\/\" target=\"_blank\" rel=\"noopener\" rel=\\\"noopener\\\" target=\\\"_blank\\\"> proof of concept<\/a> today, you won\u2019t be disappointed.\n","protected":false},"excerpt":{"rendered":"<p>Current state of play We\u2019re in a transition period where society is almost expecting Artificial Intelligence services as part of \u00a0their daily lives. There is great value shown in uses such as the large language models of ChatGPT4.0. However in Fraud Prevention solutions there is hesitancy to trust machine learning models, due to many reasons: [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1180,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","url":"","_lynx_whitepaper_file_id":0,"_lynx_whitepaper_file_url":"","footnotes":""},"categories":[42,39,43],"tags":[],"tipologia":[41],"class_list":["post-3235","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-fraud","category-machine-learning","tipologia-article"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Highlighting Fraud with Artificial Intelligence - Lyfense<\/title>\n<meta name=\"description\" content=\"Learn how artificial intelligence is revolutionizing fraud prevention. Explore strategies to illuminate and stop fraud effectively.Learn how artificial intelligence is revolutionizing fraud prevention. Explore strategies to illuminate and stop fraud effectively.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/lyfense.com\/en\/resources\/illuminating-and-stopping-fraud-with-artificial-intelligence\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Highlighting Fraud with Artificial Intelligence - Lyfense\" \/>\n<meta property=\"og:description\" content=\"Learn how artificial intelligence is revolutionizing fraud prevention. Explore strategies to illuminate and stop fraud effectively.Learn how artificial intelligence is revolutionizing fraud prevention. 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Explore strategies to illuminate and stop fraud effectively.Learn how artificial intelligence is revolutionizing fraud prevention. 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S\u00e1nchez","author_link":"https:\/\/lyfense.com\/en\/resources\/author\/davidsanchez\/"},"uagb_comment_info":0,"uagb_excerpt":"Current state of play We\u2019re in a transition period where society is almost expecting Artificial Intelligence services as part of \u00a0their daily lives. 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However in Fraud Prevention solutions there is hesitancy to trust machine learning models, due to many reasons:&hellip;","_links":{"self":[{"href":"https:\/\/lyfense.com\/en\/wp-json\/wp\/v2\/posts\/3235","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lyfense.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lyfense.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lyfense.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/lyfense.com\/en\/wp-json\/wp\/v2\/comments?post=3235"}],"version-history":[{"count":1,"href":"https:\/\/lyfense.com\/en\/wp-json\/wp\/v2\/posts\/3235\/revisions"}],"predecessor-version":[{"id":5143,"href":"https:\/\/lyfense.com\/en\/wp-json\/wp\/v2\/posts\/3235\/revisions\/5143"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/lyfense.com\/en\/wp-json\/wp\/v2\/media\/1180"}],"wp:attachment":[{"href":"https:\/\/lyfense.com\/en\/wp-json\/wp\/v2\/media?parent=3235"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lyfense.com\/en\/wp-json\/wp\/v2\/categories?post=3235"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lyfense.com\/en\/wp-json\/wp\/v2\/tags?post=3235"},{"taxonomy":"tipologia","embeddable":true,"href":"https:\/\/lyfense.com\/en\/wp-json\/wp\/v2\/tipologia?post=3235"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}