{"id":2632,"date":"2025-05-15T12:14:21","date_gmt":"2025-05-15T12:14:21","guid":{"rendered":"https:\/\/www.velaninfo.com\/rs\/?p=2632"},"modified":"2025-05-15T12:14:21","modified_gmt":"2025-05-15T12:14:21","slug":"data-annotation-us-banks-are-leveraging-high-quality-data","status":"publish","type":"post","link":"https:\/\/www.velaninfo.com\/rs\/data-annotation-us-banks-are-leveraging-high-quality-data\/","title":{"rendered":"Data Annotation for Financial AI: How U.S. Banks Are Leveraging High-Quality Data"},"content":{"rendered":"<p><span data-preserver-spaces=\"true\">AI is transforming sectors all over the globe, and arguably the most impacted industry is <\/span><span data-preserver-spaces=\"true\">that of<\/span><span data-preserver-spaces=\"true\"> finance, considering the multitude of applications from fraud management to credit analysis. One such<\/span><span data-preserver-spaces=\"true\"> &#8220;<\/span><span data-preserver-spaces=\"true\">quiet revolution<\/span><span data-preserver-spaces=\"true\">&#8221; <\/span><span data-preserver-spaces=\"true\">that changes the financial landscape is annotated data for financial AI algorithms. The banking industry in the <\/span><span data-preserver-spaces=\"true\">US<\/span><span data-preserver-spaces=\"true\"> heavily relies on credit and risk-assessing algorithms, and their outcome is highly dependent on the training data. This illustrates the importance of data for financial AI.<\/span><\/p>\n<h2><strong>The Growing Role of AI in the U.S. Banking Industry<\/strong><\/h2>\n<p><span data-preserver-spaces=\"true\">Major<\/span><span data-preserver-spaces=\"true\"> U.S. banks, including JPMorgan Chase and Wells Fargo, are progressively utilising AI solutions to optimise operations, mitigate risk, and provide more personalised services to their customers. Currently, financial AI works for several applications, such as:<\/span><\/p>\n<ul>\n<li><span data-preserver-spaces=\"true\"><strong>Payment Defaults:<\/strong> AI systems studying payment behaviour and history tend to analyse present defaults.<\/span><\/li>\n<li><strong>Fraud Detection: <\/strong><span data-preserver-spaces=\"true\">Suspicious transactions can be detected automatically by real-time systems<\/span><span data-preserver-spaces=\"true\"> preemptively before being finalised.<\/span><\/li>\n<\/ul>\n<p><span data-preserver-spaces=\"true\">AI systems are currently not equipped to handle raw, unordered, unprocessed information. <\/span><span data-preserver-spaces=\"true\">Labelled datasets that are meticulously structured and well-kept are essential to these systems <\/span><span data-preserver-spaces=\"true\">working efficiently<\/span><span data-preserver-spaces=\"true\">.<\/span><span data-preserver-spaces=\"true\">\u00a0<\/span><\/p>\n<p><a href=\"https:\/\/www.velaninfo.com\/ai-ml-training-data-services\"><strong><span data-preserver-spaces=\"true\">Data annotation<\/span><\/strong><\/a><span data-preserver-spaces=\"true\"> works in altering the extensive amounts of financial information in a <\/span><span data-preserver-spaces=\"true\">format that is comprehensible<\/span><span data-preserver-spaces=\"true\"> for machine learning algorithms so that they can be trained efficiently. <\/span><span data-preserver-spaces=\"true\">Robo-advisors can manage user equity funds <\/span><span data-preserver-spaces=\"true\">with ease<\/span><span data-preserver-spaces=\"true\"> through algorithms based on machine-learning investment strategies <\/span><span data-preserver-spaces=\"true\">along with<\/span><span data-preserver-spaces=\"true\"> modern programming languages. <\/span><span data-preserver-spaces=\"true\">Accessing their investment is simple for users<\/span><span data-preserver-spaces=\"true\"> via a website or app.<\/span><\/p>\n<p><span data-preserver-spaces=\"true\">Mobile applications have made investing easier today, enabling even those unfamiliar with technology to operate them with a device in their hands. The use of these devices aids domain novices in making educated and beneficial decisions. These technological luxuries are projected to improve financial literacy levels worldwide.<\/span><\/p>\n<h2><strong><span data-preserver-spaces=\"true\">Why Data Annotation Matters for Financial AI<\/span><\/strong><\/h2>\n<p><span data-preserver-spaces=\"true\">The labelling of various categories of datasets, such as transactions, loan applications, chat conversations, or legal documents, <\/span><span data-preserver-spaces=\"true\">is what<\/span><span data-preserver-spaces=\"true\"> comprises data annotation in finance, followed by processes executed in classification spanning processes of consolidating documents to sorting reliable transactions and automating the repetitive tasks in finance.\u00a0<\/span><\/p>\n<ul>\n<li><span data-preserver-spaces=\"true\">For example, within fraud detection, annotators mark up historical transactions as<\/span><span data-preserver-spaces=\"true\"> \u201c<\/span><span data-preserver-spaces=\"true\">legitimate<\/span><span data-preserver-spaces=\"true\">\u201d <\/span><span data-preserver-spaces=\"true\">or<\/span><span data-preserver-spaces=\"true\"> \u201c<\/span><span data-preserver-spaces=\"true\">suspicious<\/span><span data-preserver-spaces=\"true\">.\u201d<\/span><\/li>\n<li><span data-preserver-spaces=\"true\">These labels help train <strong><a href=\"https:\/\/velanapps.com\/\" target=\"_blank\" rel=\"noopener\">machine learning <\/a><\/strong>models to detect <\/span><span data-preserver-spaces=\"true\">certain<\/span> <span data-preserver-spaces=\"true\">patterns of fraud<\/span><span data-preserver-spaces=\"true\">, like multiple failed login attempts or large purchases made out of context.<\/span><\/li>\n<li><span data-preserver-spaces=\"true\">The AI gets better at detecting <\/span><span data-preserver-spaces=\"true\">suspicious activity that is new<\/span><span data-preserver-spaces=\"true\"> and thus not previously seen over time.<\/span><\/li>\n<li><span data-preserver-spaces=\"true\">Without accurate annotation, the AI could learn incorrect distinguishing features.<\/span><\/li>\n<li><span data-preserver-spaces=\"true\">This can lead to being too permissive, where fraud gets overlooked (which <\/span><span data-preserver-spaces=\"true\">actually<\/span><span data-preserver-spaces=\"true\"> is fraudulent), or overly aggressive, where legitimate transactions are questioned.<\/span><\/li>\n<li><span data-preserver-spaces=\"true\">Weak models can lead to irrational costs, erosion of reputation, and fines imposed by authorities.<\/span><\/li>\n<\/ul>\n<p><span data-preserver-spaces=\"true\">As previously mentioned, high-quality model training requires relevant and accurate data supervision or annotation. Foundation models of many financial AI systems need clean data to produce reliable results.<\/span><\/p>\n<h2><strong><span data-preserver-spaces=\"true\">The Employment of AI Annotations in <\/span><span data-preserver-spaces=\"true\">US<\/span><span data-preserver-spaces=\"true\"> Banking<\/span><\/strong><\/h2>\n<p><span data-preserver-spaces=\"true\">Leading banks now seem to integrate services with tailored data annotation companies or build in-house staff teams for comprehensive financial data annotation. <\/span><span data-preserver-spaces=\"true\">The<\/span><span data-preserver-spaces=\"true\"> impact of data annotation for banking AI on model performance <\/span><span data-preserver-spaces=\"true\">is reflected in this pattern<\/span><span data-preserver-spaces=\"true\">.<\/span><\/p>\n<p><span data-preserver-spaces=\"true\">It is clear from various case scenarios how <\/span><span data-preserver-spaces=\"true\">U.S.<\/span><span data-preserver-spaces=\"true\"> banks apply data annotation to empower AI technologies:<\/span><\/p>\n<ul>\n<li><span data-preserver-spaces=\"true\">Risk evaluation: Data is labelled so <\/span><span data-preserver-spaces=\"true\">that AI<\/span><span data-preserver-spaces=\"true\"> models can accurately predict risk using historical lending data.<\/span><\/li>\n<li><span data-preserver-spaces=\"true\"><strong><a href=\"https:\/\/www.velaninfo.com\/robotic-process-automation\">Automation<\/a> <\/strong>of Customer Services: Chatbots are trained to converse with people accurately using labelled chat records.<\/span><\/li>\n<li><span data-preserver-spaces=\"true\">Fraud detection: Real-time fraud detection systems can rapidly detect anomalies due to the high-quality labelling of past fraudulent activities.<\/span><\/li>\n<\/ul>\n<h2><strong><span data-preserver-spaces=\"true\">Benefits of High-Quality Data Annotation for Financial AI in the <\/span><span data-preserver-spaces=\"true\">U.S<\/span><span data-preserver-spaces=\"true\">.<\/span><\/strong><\/h2>\n<p><span data-preserver-spaces=\"true\">In the <\/span><span data-preserver-spaces=\"true\">U.S.<\/span><span data-preserver-spaces=\"true\">, the financial <\/span><span data-preserver-spaces=\"true\">AI&#8217;s<\/span><span data-preserver-spaces=\"true\"> benefits that come from high-quality data annotation are countless.<\/span><\/p>\n<p><span data-preserver-spaces=\"true\">For the case of high-quality data annotation, the financial <\/span><span data-preserver-spaces=\"true\">AI&#8217;s<\/span><span data-preserver-spaces=\"true\"> pros in the <\/span><span data-preserver-spaces=\"true\">U.S.<\/span><span data-preserver-spaces=\"true\"> are<\/span><\/p>\n<h5><strong><span data-preserver-spaces=\"true\">Qualitative Improvement<\/span><\/strong><\/h5>\n<p><span data-preserver-spaces=\"true\">AI operates more and more with greater precision when it comes to labeled datasets, and thus models trained on clean data perform at their best.<\/span><\/p>\n<h5><strong><span data-preserver-spaces=\"true\">Labeling Compliance<\/span><\/strong><\/h5>\n<p><span data-preserver-spaces=\"true\">Regulated compliance for banks is made easy when data ensures <\/span><span data-preserver-spaces=\"true\">AI&#8217;s<\/span><span data-preserver-spaces=\"true\"> decisions are transparent through annotated data.<\/span><\/p>\n<h2><strong><span data-preserver-spaces=\"true\">Justification Of Data <\/span><span data-preserver-spaces=\"true\">Annotation&#8217;s<\/span><span data-preserver-spaces=\"true\"> Impact on Crafting Bank AI Models<\/span><\/strong><\/h2>\n<p><span data-preserver-spaces=\"true\">AI can<\/span><span data-preserver-spaces=\"true\"> &#8220;<\/span><span data-preserver-spaces=\"true\">learn<\/span><span data-preserver-spaces=\"true\">&#8221; <\/span><span data-preserver-spaces=\"true\">from financial data <\/span><span data-preserver-spaces=\"true\">in a meaningful manner<\/span><span data-preserver-spaces=\"true\"> through data annotation. Consider it analogous to instructing a child: by providing <\/span><span data-preserver-spaces=\"true\">them with<\/span><span data-preserver-spaces=\"true\"> examples of objects (such as pears and oranges) and accurately labelling them, they will eventually develop the ability to distinguish between them. AI operates in the same manner.<\/span><\/p>\n<ul>\n<li><span data-preserver-spaces=\"true\">AI models can recognize that banks use annotated data.<\/span><\/li>\n<li><span data-preserver-spaces=\"true\">Stock market patterns,<\/span><\/li>\n<li><span data-preserver-spaces=\"true\">Typical indicators of fraudulent transactions include<\/span><\/li>\n<li><span data-preserver-spaces=\"true\">Behaviours that are associated with the success or failure of loan repayment,<\/span><\/li>\n<li><span data-preserver-spaces=\"true\">Customer satisfaction or dissatisfaction trends.<\/span><\/li>\n<\/ul>\n<p><span data-preserver-spaces=\"true\">For instance<\/span><span data-preserver-spaces=\"true\">, to forecast the stock market<\/span><span data-preserver-spaces=\"true\">, a bank must train the neural network with historical market data.<\/span> <span data-preserver-spaces=\"true\">The data must be accurately designated<\/span><span data-preserver-spaces=\"true\">, for example,<\/span><span data-preserver-spaces=\"true\"> by tagging events that impacted the market (e.g., economic reports, company earnings, global crises).<\/span><span data-preserver-spaces=\"true\"> The artificial intelligence can learn to forecast more precisely as the more exact and detailed the annotation is.<\/span><\/p>\n<p><span data-preserver-spaces=\"true\">In the same vein, NLP models that analyse customer feedback necessitate <\/span><span data-preserver-spaces=\"true\">data that is accurately tagged<\/span><span data-preserver-spaces=\"true\">.<\/span><span data-preserver-spaces=\"true\"> For instance, <\/span><span data-preserver-spaces=\"true\">the phrase<\/span><span data-preserver-spaces=\"true\"> &#8220;<\/span><span data-preserver-spaces=\"true\">I am satisfied with the service<\/span><span data-preserver-spaces=\"true\">&#8221; <\/span><span data-preserver-spaces=\"true\">could be classified as a positive sentiment. <\/span><span data-preserver-spaces=\"true\">The negative sentiment<\/span><span data-preserver-spaces=\"true\"> &#8220;<\/span><span data-preserver-spaces=\"true\">The loan process took too long<\/span><span data-preserver-spaces=\"true\">&#8221; <\/span><span data-preserver-spaces=\"true\">may be associated with the processing time. <\/span><span data-preserver-spaces=\"true\">The <\/span><span data-preserver-spaces=\"true\">AI&#8217;s<\/span><span data-preserver-spaces=\"true\"> performance is directly influenced by the <\/span><span data-preserver-spaces=\"true\">quality of the annotation<\/span><span data-preserver-spaces=\"true\"> in both scenarios.<\/span><\/p>\n<h2><strong><span data-preserver-spaces=\"true\">The Importance of Data Labelling in Financial AI and Machine Learning<\/span><\/strong><\/h2>\n<p>Data labeling is not merely a back-end task in financial AI and machine learning; it is a fundamental strategic requirement. This is because financial institutions look after delicate, complex, and risky datasets. Poor interpretation can lead to suboptimal choices.<\/p>\n<ul>\n<li><span data-preserver-spaces=\"true\">Lapsed compliance matters.<\/span><\/li>\n<li><span data-preserver-spaces=\"true\">Inaccurate customer risk profiles.<\/span><\/li>\n<\/ul>\n<p><em><strong>The following cases show the need for data labelling.\u00a0<\/strong><\/em><\/p>\n<ul>\n<li><span data-preserver-spaces=\"true\">AI needs to retrieve some specific entities from the financial documents, like the <\/span><span data-preserver-spaces=\"true\">name of the account holder<\/span><span data-preserver-spaces=\"true\">, <\/span><span data-preserver-spaces=\"true\">the account number, the<\/span><span data-preserver-spaces=\"true\"> interest rate, and <\/span><span data-preserver-spaces=\"true\">the<\/span><span data-preserver-spaces=\"true\"> payment date.<\/span><span data-preserver-spaces=\"true\"> The elements are automatically recognized by the model with the help of labelling.<\/span><\/li>\n<li><span data-preserver-spaces=\"true\">Labelling in customer service files assists AI in understanding the context and attitude (positive, neutral, or negative).<\/span><\/li>\n<li><span data-preserver-spaces=\"true\">A label has to be attached to data sets if<\/span><span data-preserver-spaces=\"true\"> a model intends to <\/span><span data-preserver-spaces=\"true\">change<\/span><span data-preserver-spaces=\"true\"> from basic pattern recognition to useful, meaningful insight generation.<\/span><\/li>\n<\/ul>\n<h2><strong><span data-preserver-spaces=\"true\">High-Quality Data Annotation for Building Reliable Financial AI Models\u00a0<\/span><\/strong><\/h2>\n<p><span data-preserver-spaces=\"true\">The finance industry never compromises on precision and dependability. Take, for example, a financial AI model. If it predicts loan applicants who are of good standing and need financing as undeserving of such services or misclassifies transactions where payment fraud is attempted as innocent, massive losses, non-compliance with policies, and damage to trust follow.<\/span><\/p>\n<p><span data-preserver-spaces=\"true\">To prevent this, we use prioritized, top-notch annotation services, which apply labels <\/span><span data-preserver-spaces=\"true\">with <\/span><span data-preserver-spaces=\"true\">such<\/span><span data-preserver-spaces=\"true\"> precision <\/span><span data-preserver-spaces=\"true\">as<\/span><span data-preserver-spaces=\"true\"> to produce accurate results (no errors or misclassifications).<\/span><span data-preserver-spaces=\"true\"> Why are top-notch annotation services used, which are labelled with such precision as to produce<\/span><\/p>\n<ul>\n<li><span data-preserver-spaces=\"true\"> Accurate (no <\/span><span data-preserver-spaces=\"true\">errors<\/span><span data-preserver-spaces=\"true\"> or misclassifications),<\/span><\/li>\n<li><span data-preserver-spaces=\"true\">Consistent\u2002(applies the same criteria in the different datasets),<\/span><\/li>\n<li><span data-preserver-spaces=\"true\">Context-aware (understands the nuances of financial\u2002language and transactions)<\/span><\/li>\n<\/ul>\n<p><strong>To achieve such a high\u2002quality, several banks are adopting a hybrid annotation strategy:<\/strong><\/p>\n<p><span data-preserver-spaces=\"true\">Human\u2002annotators:<\/span><span data-preserver-spaces=\"true\"> These are experts in the domain or human teams who understand the financial data <\/span><span data-preserver-spaces=\"true\">context<\/span><span data-preserver-spaces=\"true\">, such as legal language, types of transactions, and regulatory vocabulary.<\/span><span data-preserver-spaces=\"true\"> They\u2002are <\/span><span data-preserver-spaces=\"true\">especially useful<\/span><span data-preserver-spaces=\"true\"> in complex or delicate cases, as they ensure accuracy.<\/span><\/p>\n<ul>\n<li><span data-preserver-spaces=\"true\">Automated solutions: These tools use AI to accelerate or assist\u2002in the annotation. For example, algorithms can\u2002ultimately pre-annotate data, which humans can <\/span><span data-preserver-spaces=\"true\">then<\/span><span data-preserver-spaces=\"true\"> review and correct.<\/span><\/li>\n<li><span data-preserver-spaces=\"true\">In this human-in-the-loop system, large amounts of data can\u2002be efficiently maintained by the institutions (scalability), and the accuracy of the data can be ensured.<\/span><\/li>\n<\/ul>\n<p><span data-preserver-spaces=\"true\">Even a <\/span><span data-preserver-spaces=\"true\">small<\/span><span data-preserver-spaces=\"true\"> mistake in a high-risk environment like finance\u2002can <\/span><span data-preserver-spaces=\"true\">lead to<\/span><span data-preserver-spaces=\"true\"> dire consequences. <\/span><span data-preserver-spaces=\"true\">This is why reliable financial AI models, safe to be introduced into actual banking processes, are made possible only by <\/span><span data-preserver-spaces=\"true\">means of<\/span><span data-preserver-spaces=\"true\"> good-quality data annotation <\/span><span data-preserver-spaces=\"true\">that<\/span><span data-preserver-spaces=\"true\"> can be realized by <\/span><span data-preserver-spaces=\"true\">bringing together<\/span><span data-preserver-spaces=\"true\"> AI efficiency and\u2002human intuition.<\/span><\/p>\n<h3><strong><span data-preserver-spaces=\"true\">Conclusion\u00a0<\/span><\/strong><\/h3>\n<p><span data-preserver-spaces=\"true\">Although financial AI is reconfiguring the <\/span><span data-preserver-spaces=\"true\">financial<\/span><span data-preserver-spaces=\"true\">\u2002landscape, <\/span><span data-preserver-spaces=\"true\">U.S.<\/span><span data-preserver-spaces=\"true\"> banks using AI will need to focus on curating high-quality data for financial AI. It is not just\u2002more data; it is better data. The road to safer, <\/span><span data-preserver-spaces=\"true\">smarter<\/span><span data-preserver-spaces=\"true\"> banking is made of well-labeled\u2002information, from annotated financial data to well-calibrated models.<\/span><\/p>\n<p><span data-preserver-spaces=\"true\">By recognizing the importance of data annotation in predictive\u2002banking models, banks can leverage the power of machine learning to make better decisions, gain a competitive edge, and ultimately deliver more to their customers.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI is transforming sectors all over the globe, and arguably the most impacted industry is that of finance, considering the multitude of applications from fraud management to credit analysis. One such &#8220;quiet revolution&#8221; that changes the financial landscape is annotated data for financial AI algorithms. The banking industry in the US heavily relies on credit&#8230;<a class=\"continue-reading text-uppercase\" href=\"https:\/\/www.velaninfo.com\/rs\/data-annotation-us-banks-are-leveraging-high-quality-data\/\"> Continue Reading <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.velaninfo.com\/rs\/wp-content\/themes\/velaninfo\/images\/reading_arw.png\" alt=\"Continue Reading\" width=\"16\" height=\"12\"\/><\/a><\/p>\n","protected":false},"author":3,"featured_media":2634,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[494],"tags":[],"class_list":["post-2632","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-annotation-for-ai-ml"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v19.5 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Data Annotation for Financial AI in Banking<\/title>\n<meta name=\"description\" content=\"Learn how annotated data enables secure, compliant, and intelligent AI systems in U.S. banks-driving efficiency and smarter decision-making.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.velaninfo.com\/rs\/data-annotation-us-banks-are-leveraging-high-quality-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Annotation for Financial AI: How U.S. Banks Are Leveraging High-Quality Data\" \/>\n<meta property=\"og:description\" content=\"Learn how annotated data enables secure, compliant, and intelligent AI systems in U.S. banks-driving efficiency and smarter decision-making.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.velaninfo.com\/rs\/data-annotation-us-banks-are-leveraging-high-quality-data\/\" \/>\n<meta property=\"og:site_name\" content=\"Velan\" \/>\n<meta property=\"article:published_time\" content=\"2025-05-15T12:14:21+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.velaninfo.com\/rs\/wp-content\/uploads\/2025\/05\/01-outsourcing.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"630\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Jack Manu\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Jack Manu\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.velaninfo.com\\\/rs\\\/data-annotation-us-banks-are-leveraging-high-quality-data\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.velaninfo.com\\\/rs\\\/data-annotation-us-banks-are-leveraging-high-quality-data\\\/\"},\"author\":{\"name\":\"Jack Manu\",\"@id\":\"https:\\\/\\\/www.velaninfo.com\\\/rs\\\/#\\\/schema\\\/person\\\/2c4719e8319fe78d44c96c2d2d520589\"},\"headline\":\"Data Annotation for Financial AI: How U.S. Banks Are Leveraging High-Quality Data\",\"datePublished\":\"2025-05-15T12:14:21+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.velaninfo.com\\\/rs\\\/data-annotation-us-banks-are-leveraging-high-quality-data\\\/\"},\"wordCount\":1436,\"commentCount\":0,\"image\":{\"@id\":\"https:\\\/\\\/www.velaninfo.com\\\/rs\\\/data-annotation-us-banks-are-leveraging-high-quality-data\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.velaninfo.com\\\/rs\\\/wp-content\\\/uploads\\\/2025\\\/05\\\/01-outsourcing.jpg\",\"articleSection\":[\"Data Annotation for AI &amp; 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