{"id":128141,"date":"2023-11-13T14:10:46","date_gmt":"2023-11-13T14:10:46","guid":{"rendered":"https:\/\/feedzai.com\/?p=128141"},"modified":"2024-04-09T09:12:08","modified_gmt":"2024-04-09T09:12:08","slug":"enhancing-ai-model-risk-governance-with-feedzai","status":"publish","type":"post","link":"https:\/\/feedzai.com\/blog\/enhancing-ai-model-risk-governance-with-feedzai\/","title":{"rendered":"Enhancing AI Model Risk Governance with Feedzai"},"content":{"rendered":"<div class=\"wpb-content-wrapper\"><p>[vc_row row_height_percent=&#8221;0&#8243; override_padding=&#8221;yes&#8221; h_padding=&#8221;2&#8243; top_padding=&#8221;1&#8243; bottom_padding=&#8221;2&#8243; overlay_alpha=&#8221;50&#8243; gutter_size=&#8221;3&#8243; column_width_percent=&#8221;100&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243;][vc_column width=&#8221;1\/1&#8243;][vc_row_inner][vc_column_inner width=&#8221;1\/12&#8243;][\/vc_column_inner][vc_column_inner width=&#8221;10\/12&#8243;][vc_single_image media=&#8221;128142&#8243; dynamic=&#8221;yes&#8221; media_width_percent=&#8221;100&#8243; uncode_shortcode_id=&#8221;140946&#8243;][\/vc_column_inner][vc_column_inner width=&#8221;1\/12&#8243;][\/vc_column_inner][\/vc_row_inner][vc_row_inner][vc_column_inner column_width_percent=&#8221;100&#8243; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_visibility=&#8221;yes&#8221; mobile_width=&#8221;0&#8243; width=&#8221;2\/12&#8243;][\/vc_column_inner][vc_column_inner width=&#8221;8\/12&#8243;][vc_custom_heading heading_semantic=&#8221;h3&#8243; text_size=&#8221;h3&#8243; text_weight=&#8221;400&#8243; uncode_shortcode_id=&#8221;177611&#8243;]<span style=\"font-weight: 400;\">Artificial intelligence (AI) and machine learning are pivotal in helping banks and institutions stay ahead of <a href=\"https:\/\/feedzai.com\/solutions\/transaction-fraud\/\" target=\"_blank\" rel=\"noopener\">fraud<\/a> and financial crime tactics. However, advanced technologies come with their own set of challenges, especially when it comes to <\/span><a href=\"https:\/\/feedzai.com\/blog\/model-risk-governance-for-acquirers-and-psps-for-fraud\/\"><span style=\"font-weight: 400;\">model risk governance<\/span><\/a><span style=\"font-weight: 400;\">, a comprehensive and structured approach to managing the risks that arise from the development, deployment, and continuous operation of quantitative AI models.<\/span>[\/vc_custom_heading][vc_column_text uncode_shortcode_id=&#8221;146477&#8243;]<span style=\"font-weight: 400;\">Learn the critical challenges with current AI model risk governance frameworks and how Feedzai is making a difference.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">The Challenges with Current AI Model Risk Governance Frameworks<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Many banks face two key challenges regarding AI model risk governance frameworks.<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">1. Self-Learning and Evolving Models<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">AI models are not static entities. They self-learn and evolve after exposure to real-world scenarios.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This dynamic nature can be a double-edged sword. On the one hand, it helps catch unexpected anomalies that traditional systems might miss. But on the other hand, it poses a challenge for fraud teams. Banks must ensure that these models continue to produce meaningful results.<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">2. Understanding Supervised and Unsupervised Models<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Two primary types of machine learning models come into play here: supervised and unsupervised.\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Supervised machine learning <\/b><span style=\"font-weight: 400;\">uses training data with labels, identifying &#8220;good&#8221; or &#8220;bad&#8221; examples. The model learns to classify new examples based on patterns found in the training data.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Unsupervised machine learning <\/b><span style=\"font-weight: 400;\">takes a more autonomous approach because it is not trained on labeled data. It identifies anomalies based on clusters of data that it deems similar. This makes it a powerful tool for uncovering unexpected fraud patterns.<\/span><\/li>\n<\/ul>\n<p>[\/vc_column_text][vc_single_image media=&#8221;119107&#8243; media_width_percent=&#8221;100&#8243; uncode_shortcode_id=&#8221;499131&#8243; media_link=&#8221;url:https%3A%2F%2Fhubs.la%2FQ028xt7Q0|target:_blank&#8221;][vc_column_text uncode_shortcode_id=&#8221;211859&#8243;]<span style=\"font-weight: 400;\">While the advantage of unsupervised models is clear, it is crucial to maintain vigilant oversight to guarantee their continued efficacy in real-world applications.<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">2. Regulatory Expectations for Governance<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Many jurisdictions, such as the US Office of the Comptroller of the Currency (OCC), mandate the documentation of the entire process involved in creating and maintaining a model that affects individuals\u2019 financial decisions. This documentation is a crucial step in ensuring fairness and accountability in using models. However, there are several challenges to overcome:<\/span><\/p>\n<h5><span style=\"font-weight: 400;\">1. Domain Expertise is Critical (and Time-Consuming)<\/span><\/h5>\n<p><span style=\"font-weight: 400;\">Effective model governance typically requires a dedicated team who is hands-on with the model development and monitoring process. This team should also clearly and explicitly communicate how they use the results.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This process isn\u2019t necessarily flashy because this governance process doesn\u2019t actively stop criminals. But it\u2019s crucial to always monitor and tune models, as well as demonstrate the validity of the decisions your financial institution produces.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Furthermore, it\u2019s detrimental if it\u2019s done incorrectly. Imagine if your credit bureau cannot prove its methodology for generating your credit score.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">End-to-end documentation of data sources, the intended purpose, development, training, and results of all models is a time-consuming process. A team needs to sit down and type out a multi-page report detailing this process with tables, graphs, and charts to demonstrate the model\u2019s purpose and effectiveness. Some regulatory agencies require this to be done on a semi-annual basis.\u00a0<\/span><\/p>\n<h5><span style=\"font-weight: 400;\">2. New AI Techniques Carry Risks<\/span><\/h5>\n<p><span style=\"font-weight: 400;\">The advent of generative AI, or <a href=\"https:\/\/feedzai.com\/blog\/myai-customer-consent-data-privacy-generative-ai-age\/\" target=\"_blank\" rel=\"noopener\">GenAI<\/a><\/span><span style=\"font-weight: 400;\">, introduces a new set of risks that go beyond the model itself. Transparency is key, and it&#8217;s vital for building trust in the decisions made by these models. It\u2019s critical to understand and document where the data originates.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Consider this scenario: If the data sources are not transparent, how can banks trust the responses or decisions that AI models provide? Or explain it to regulators? For example, if models draw from biased data sources, questions about fairness and reliability will arise. When AI models impact people&#8217;s lives by determining whether they can open a savings account or get a credit card, transparency and reliability become paramount.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The Biden administration recently issued the first-ever <\/span><a href=\"https:\/\/feedzai.com\/blog\/what-recent-ai-regulation-proposals-get-right\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">executive order<\/span><\/a><span style=\"font-weight: 400;\"> on artificial intelligence\u2019s societal impact. The order aims to ensure AI is implemented safely, prepare agencies for AI\u2019s growth, and mandate transparency from model developers. In other words, expect model transparency to be required as new AI regulations take shape.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">How Feedzai Stands Apart<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Feedzai understands these challenges and has crafted solutions that set it apart in the field of AI model risk governance.<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">1. Automatic Model Monitoring<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Feedzai\u2019s proactive monitoring system automatically detects changes in models often unseen by the human eye. This process begins with feature engineering and the automatic selection of the best features for the model. As the model produces results, it&#8217;s crucial to ensure that it still performs as intended. Feedzai streamlines this aspect, saving time and resources.<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">2. Automatic Feature Monitoring<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Data drift is a significant concern in AI model governance. Feedzai offers automated alarms and data drift detection by monitoring the distribution of data features. It measures data stability by comparing feature distributions over time, thus providing insights into potential issues.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A real-world example from a bank in EMEA found broken fields and data drift when comparing training data with production data. Intuitive visuals explain observed shifts in the data, simplifying the decision-making process.<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">3. Automatic Model Governance Reports<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Feedzai automatically generates standard Model Governance Reports with all the relevant information and numbers, such as data sources, features used by the model, detection performance, and a bias audit. The system captures any edits or changes made to the model, automatically documenting them within the system. Banks can easily pull these details into the report, saving considerable time and effort.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Why Choose Feedzai?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Feedzai delivers a different AI model risk governance experience for banks. This experience features two important benefits.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Built-in Value: <\/b><span style=\"font-weight: 400;\">Time is valuable for any organization. Banks can either handle all these tasks manually or build similar systems themselves. This can be a resource-intensive and time-consuming endeavor.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Time and Cost Savings: <\/b><span style=\"font-weight: 400;\">Banks can also reduce the time and effort required for model governance. The system does the heavy lifting, allowing institutions to make and document changes efficiently.\u00a0<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">These benefits cater to both bigger and smaller banks. Larger organizations will see report preparation times reduced from two weeks to a few days. This enhances efficiency and allows for quicker response to evolving fraud patterns. Meanwhile, for smaller banks, which may not have robust model governance systems, the system helps reduce risks and boost capabilities.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Feedzai delivers automated monitoring, feature analysis, and report generation that ultimately saves banks time, money, and resources. Feedzai ensures that AI models provide effective results and are adaptable in the shifting landscape of financial crime detection.<\/span>[\/vc_column_text][\/vc_column_inner][vc_column_inner column_width_percent=&#8221;100&#8243; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_visibility=&#8221;yes&#8221; mobile_width=&#8221;0&#8243; width=&#8221;2\/12&#8243;][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Artificial intelligence (AI) and machine learning are pivotal in helping banks and institutions stay ahead of fraud and financial crime tactics. However, advanced technologies come with their own set of challenges, especially when it comes to model risk governance, a comprehensive and structured approach to managing the risks that arise from the development, deployment, and continuous operation of quantitative AI models.<\/p>\n","protected":false},"author":106,"featured_media":128154,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[88],"tags":[501,78],"acf":[],"yoast_head":"\n<title>Enhancing AI Model Risk Governance with Feedzai | Feedzai<\/title>\n<meta name=\"description\" content=\"Learn the critical challenges with current AI model risk governance frameworks and how Feedzai is making a difference.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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