{"id":2794,"date":"2022-12-12T13:01:39","date_gmt":"2022-12-12T13:01:39","guid":{"rendered":"https:\/\/nilg.ai\/?p=2794"},"modified":"2025-03-17T12:18:17","modified_gmt":"2025-03-17T12:18:17","slug":"our-vision-of-ai-in-financial-services","status":"publish","type":"post","link":"https:\/\/nilg.ai\/pt\/202212\/our-vision-of-ai-in-financial-services\/","title":{"rendered":"Our vision of AI in Financial Services"},"content":{"rendered":"<p><img decoding=\"async\" src=\"https:\/\/nilg.ai\/wp-content\/uploads\/2022\/12\/Test-02_Small.jpg\" alt=\"Featured\" \/><\/p>\n<p><span style=\"font-weight: 400;\">In recent years, the financial services industry has been innovating technologically, supported by a complex ecosystem including banks, financial service providers, and start-ups (<\/span><a href=\"https:\/\/www.fintechnews.org\/how-ai-is-affecting-fintech\/\"><span style=\"font-weight: 400;\">link<\/span><\/a><span style=\"font-weight: 400;\">). Within this blogpost, we showcase our vision of AI in Financial Services.<\/span><\/p>\n<h2><b>AI in Financial Services<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">From our point of view, we can group use cases in AI in three distinct entities, depending on the level of granularity<\/span><\/p>\n<ul>\n<li><b>Microentity Level: <\/b><span style=\"font-weight: 400;\">At the microentity level, the main purpose of AI is the optimization of operations\/transactions without compromising the quality of service. This includes business goals such as minimizing costs and improving user experience.<\/span><\/li>\n<li aria-level=\"1\"><b>User level: <\/b><span style=\"font-weight: 400;\">At user level, the goal is to increase the user value (e.g. by keeping him engaged with the service) and control risk in the services provided to the user.<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-level=\"1\"><b>Company level: <\/b><span style=\"font-weight: 400;\">At company level, we aim to optimize the company portfolio and return on investments.<\/span><\/li>\n<\/ul>\n<p><i><span style=\"font-weight: 400;\">What kind of data do I need?<\/span><\/i><\/p>\n<p><span style=\"font-weight: 400;\">Although most of the use cases described below require specific data sources, we can define a few general data points for each entity. If you work in this area and see a data source you haven\u2019t started acquiring in a structured way yet, get to it!<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Microentity: Transactions can be characterized by a value, channel, date, involved parties and other characterization\/description (e.g. food, electricity, \u2026)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">User: All users should have contractual data (contract start date, contract conditions) as well as behavioral data (customer service tickets, cash flow, \u2026).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Company: A company is characterized by its portfolio size and distribution, external market indicators and economic context, and comparison with competitors.<\/span><img decoding=\"async\" class=\"alignnone wp-image-2795\" src=\"https:\/\/nilg.ai\/wp-content\/uploads\/2022\/12\/Diagram-300x113.png\" alt=\"\" width=\"808\" height=\"304\" srcset=\"https:\/\/nilg.ai\/wp-content\/uploads\/2022\/12\/Diagram-300x113.png 300w, https:\/\/nilg.ai\/wp-content\/uploads\/2022\/12\/Diagram-768x290.png 768w, https:\/\/nilg.ai\/wp-content\/uploads\/2022\/12\/Diagram-600x227.png 600w, https:\/\/nilg.ai\/wp-content\/uploads\/2022\/12\/Diagram.png 850w\" sizes=\"(max-width: 808px) 100vw, 808px\" \/><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Companies can also be grouped by its main mission\/objective. In each of them, we can further detail use cases for each entity type.\u00a0<\/span><\/p>\n  \n\n <div class=\"author-cta\">\n\t\t<div class=\"author-cta-img\">\n\t\t    \n\t\t    <img decoding=\"async\" width=\"1024\" height=\"906\" src=\"https:\/\/nilg.ai\/wp-content\/uploads\/2022\/07\/Web-Rafael.png\" class=\"attachment-full size-full\" alt=\"Rafael Cavalheiro NILG.AI\" srcset=\"https:\/\/nilg.ai\/wp-content\/uploads\/2022\/07\/Web-Rafael.png 1024w, https:\/\/nilg.ai\/wp-content\/uploads\/2022\/07\/Web-Rafael-300x265.png 300w, https:\/\/nilg.ai\/wp-content\/uploads\/2022\/07\/Web-Rafael-768x680.png 768w, https:\/\/nilg.ai\/wp-content\/uploads\/2022\/07\/Web-Rafael-600x531.png 600w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t    <\/div>\n\n<div class=\"author-cta-content\">\n\t<h3>Do you want to further discuss this idea?<\/h3><p>Book a meeting with <strong>Rafael Cavalheiro<\/strong><\/p>\t<a class=\"cta_btn\" onclick=\"Calendly.showPopupWidget('');return false;\"  \n\">Meet Rafael<\/a>\n\t\t\t\n\t<a href=\"https:\/\/nilg.ai\/pt\/?post_type=team&p=1650\" class=\"author-cta-link\">Saber mais<\/a>\n\t\t\t<\/div>\n\t<\/div>\n\n<p><span style=\"font-weight: 400;\">Now, let\u2019s review some specific use cases per company type in the financial services industry. Overall, the use cases focus on using AI for mitigating risks, providing a better experience for the user and guaranteeing business sustainability.<\/span><\/p>\n<h3><b>Banking and Money Transfer<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Banking and Money Transfer companies transfer money from entity to entity. Companies within this group include Revolut, N26 and Monzo.<\/span><\/p>\n<p><i><span style=\"font-weight: 400;\">Microentity Level:<\/span><\/i><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Transaction categorization: User transactions can be categorized by business name, location and business type, for instance. Take as an example one product by one of our clients, <\/span><a href=\"https:\/\/www.pentadatainc.com\/products\/merchant-id\/\"><span style=\"font-weight: 400;\">Pentadata<\/span><\/a><span style=\"font-weight: 400;\">, for merchant identification given a transaction description.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Fraudulent transaction detection: Detecting and blocking fraudulent transactions is critical to increase the confidence of customers in the services.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Money Laundering Detection: Machine learning has been replacing rule-based models for anti-money laundering operations, to <\/span><a href=\"https:\/\/feedzai.com\/blog\/machine-learning-rules-vs-models-in-anti-money-laundering-platforms\/\"><span style=\"font-weight: 400;\">minimize the number of false positives<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Predicting recurrent transactions: User transactions can be grouped into recurrent transactions to later on create visualizations on the fixed expenses.\u00a0<\/span><\/li>\n<\/ul>\n<p><i><span style=\"font-weight: 400;\">User level<\/span><\/i><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Optimizing spending habits \/ forecasting months spendings: Based on the historical data of the transactions, it is possible to forecast month spends and make personalized suggestions of spending habits to the user, per category.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Churn Prediction (CRM): Predicting which users are going to churn, and the best action to prevent them from churning, is one of the most common marketing use cases, and can help significantly increase the customer lifetime value.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Upselling (CRM): Determining which services we should upsell the user to, depending on the user behavioral patterns.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Chatbots\/Automations: Automating the most frequent questions the users have will lead to reduced spendings in customer service.<\/span><\/li>\n<\/ul>\n<p><i><span style=\"font-weight: 400;\">Company level<\/span><\/i><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Optimize physical store locations:\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Forecasting business and market indicators: user growth, net working capital, \u2026<\/span><\/li>\n<\/ul>\n<h3><b>Payments<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Payment companies transfer money from a person to a company, such as Paypal and Stripe.\u00a0<\/span><\/p>\n<p><i><span style=\"font-weight: 400;\">Microentity Level:<\/span><\/i><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Fraudulent transaction detection: detecting and blocking fraudulent transactions &#8211; e.g. values out of the ordinary &#8211; can help reduce the occurrence of fraudulent transactions.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Predicting card declines: Payments can be declined by the issuing banks for various reasons, such as the card exceeding its credit limit. Predicting and addressing this is one of the use cases Paypal focused on the most using AI (<\/span><a href=\"https:\/\/medium.com\/paypal-tech\/using-machine-learning-to-improve-payment-authorization-rates-bc3b2cbf4999\"><span style=\"font-weight: 400;\">link<\/span><\/a><span style=\"font-weight: 400;\">).<\/span><\/li>\n<\/ul>\n<p><i><span style=\"font-weight: 400;\">User level:<\/span><\/i><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Optimizing user experience: <\/span><a href=\"https:\/\/medium.com\/paypal-tech\/explainable-artificial-intelligence-xai-using-ai-to-minimize-risks-and-improve-customer-fb2bde845cce\"><span style=\"font-weight: 400;\">explaining to the users<\/span><\/a><span style=\"font-weight: 400;\"> the reason for certain actions in an automated manner (e.g. suspected fraud) leads to increased confidence in the services.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">CRM promoting certain behaviors or feature-usage.<\/span>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Paypal for business \/ stripe for business fees<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><i><span style=\"font-weight: 400;\">Company level:<\/span><\/i><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Optimizing flows of money: money transfers or currency exchange can be optimized to be performed at the optimal time period, to reduce transaction costs.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Internal investments of funds: deciding on the best way to invest funds.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Compliance detection in reports.<\/span><\/li>\n<\/ul>\n<h3><b>Financing<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Finance companies &#8211; such as Cetelem, Klarna and <\/span><a href=\"https:\/\/cashea.app\/\"><span style=\"font-weight: 400;\">Cashea<\/span><\/a><span style=\"font-weight: 400;\">, make loans to individuals and businesses.\u00a0<\/span><\/p>\n<p><i><span style=\"font-weight: 400;\">Microentity Level:<\/span><\/i><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Claims approval or denial: Automating and detecting errors in claims can reduce manual work and improve the claim processing speed. If you\u2019re curious on how this could be applied in the healthcare domain, take a look at<\/span><a href=\"https:\/\/nilg.ai\/pt\/202005\/detecting-errors-in-insurance-claims\/\"><span style=\"font-weight: 400;\"> our blogpost <\/span><\/a><span style=\"font-weight: 400;\">we wrote a while back!<\/span><\/li>\n<\/ul>\n<p><i><span style=\"font-weight: 400;\">User level:<\/span><\/i><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Credit scoring: credit scoring uses Artificial Intelligence to predict the likelihood of default based on demographic factors, payment history and other financial indicators. We also have <\/span><a href=\"https:\/\/nilg.ai\/pt\/202107\/insights-in-ai-applied-to-credit-scoring\/\"><span style=\"font-weight: 400;\">a blogpost<\/span><\/a><span style=\"font-weight: 400;\"> on more details on Artificial Intelligence applied to credit scoring. Similar use cases consider the<\/span><b> renegotiation of payment conditions <\/b><span style=\"font-weight: 400;\">e<\/span><b> default prediction.<\/b><\/li>\n<\/ul>\n<p><i><span style=\"font-weight: 400;\">Company level:<\/span><\/i><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Portfolio optimization: at a company level, companies can aim to best determine the optimal risk level for credits (e.g. spread, short-term, long-term).<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">If you\u2019re curious about some of these use cases but aren\u2019t sure how beneficial it will be for your company, worry not! In our Data Ignite course, you can find out how to realize potential risks and mitigation strategies at the project conception stage, and learn a common language to discuss AI projects between technical and non-technical teams.\u00a0<\/span><\/p>\n<div class=\"course-cta\">\n\t\t<div class=\"course-cta-img\"><img decoding=\"async\" width=\"582\" height=\"903\" src=\"https:\/\/nilg.ai\/wp-content\/uploads\/2022\/08\/4-194x301@3x.png\" class=\"attachment-full size-full\" alt=\"\" srcset=\"https:\/\/nilg.ai\/wp-content\/uploads\/2022\/08\/4-194x301@3x.png 582w, https:\/\/nilg.ai\/wp-content\/uploads\/2022\/08\/4-194x301@3x-193x300.png 193w, https:\/\/nilg.ai\/wp-content\/uploads\/2022\/08\/4-194x301@3x-300x465.png 300w\" sizes=\"(max-width: 582px) 100vw, 582px\" \/><\/div>\n\t\t<div class=\"course-cta-content\"><h6>Course, Templates<\/h6><h3>Data Ignite<\/h3>\n\t\t\t<p>Find out how to realize potential risks and mitigation strategies<\/p>\n\t\t\t<a href=\"https:\/\/nilg.ai\/pt\/product\/data-ignite\/\" class=\"cta_btn\">Saber mais<\/a>\n\t\t<\/div>\n\t<\/div>\n<h3><b>Investments and Brokers<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Within the investments and brokers groups, we consider companies that facilitate transactions between traders, sellers, or buyers. Examples include DeGiro, trading212, xtb and multiple P2P lending companies (PeerBerry, Mintos, estateguru, GoParity, etc).\u00a0<\/span><\/p>\n<p><i><span style=\"font-weight: 400;\">Microentity Level:<\/span><\/i><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><i><span style=\"font-weight: 400;\">Default or delays forecasts:<\/span><\/i><span style=\"font-weight: 400;\"> forecasting payment delays\/defaults is useful to take action ahead of time.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><i><span style=\"font-weight: 400;\">Trading bots<\/span><\/i><span style=\"font-weight: 400;\">: automating trading decisions at scale<\/span><\/li>\n<\/ul>\n<p><i><span style=\"font-weight: 400;\">User level:<\/span><\/i><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">User investment recommendations depending on risk profile of the user, recommend financial products to invest on.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Default prediction: predicting loan default before it happens to better determine the established conditions.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Risk assessment for loan originators: loans typically have a risk level associated, depending with the expected gain\/risk trade off. Automating this based on historical data is important to make better decisions.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">All CRM-related use cases<\/span><\/li>\n<\/ul>\n<p><i><span style=\"font-weight: 400;\">Company level:<\/span><\/i><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Portfolio balancing: determine the best balance between risk and gain for the company\u2019s portfolio.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Lastly, FinTech companies are software companies that provide services to Financial Services, and build part of the use cases listed above. The crypto Industry is also fulfilling the roles of brokers, banking, money transfer, payments, using different technologies.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">There are some general use-cases related to KYC (Know your Customer), with general problems such as Legal Document Validation, knowledge tests\u2026\u00a0<\/span><\/p>\n<h2><b>Conclusion<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">AI in Financial Services is on the path to be a tool to revolutionize the provided services. At the scale of people using financial services, and the fact that most services are online now, facilitating the acquisition of data and the creation of value, there\u2019s a huge potential for innovation and growth in this area.<\/span><\/p>\n  \n\n <div class=\"author-cta\">\n\t\t<div class=\"author-cta-img\">\n\t\t    \n\t\t    <img decoding=\"async\" width=\"1024\" height=\"906\" src=\"https:\/\/nilg.ai\/wp-content\/uploads\/2022\/07\/Web-Rafael.png\" class=\"attachment-full size-full\" alt=\"Rafael Cavalheiro NILG.AI\" srcset=\"https:\/\/nilg.ai\/wp-content\/uploads\/2022\/07\/Web-Rafael.png 1024w, https:\/\/nilg.ai\/wp-content\/uploads\/2022\/07\/Web-Rafael-300x265.png 300w, https:\/\/nilg.ai\/wp-content\/uploads\/2022\/07\/Web-Rafael-768x680.png 768w, https:\/\/nilg.ai\/wp-content\/uploads\/2022\/07\/Web-Rafael-600x531.png 600w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t    <\/div>\n\n<div class=\"author-cta-content\">\n\t<h3>Do you want to further discuss this idea?<\/h3><p>Book a meeting with <strong>Rafael Cavalheiro<\/strong><\/p>\t<a class=\"cta_btn\" onclick=\"Calendly.showPopupWidget('');return false;\"  \n\">Meet Rafael<\/a>\n\t\t\t\n\t<a href=\"https:\/\/nilg.ai\/pt\/?post_type=team&p=1650\" class=\"author-cta-link\">Saber mais<\/a>\n\t\t\t<\/div>\n\t<\/div>","protected":false},"excerpt":{"rendered":"<p>In recent years, the financial services industry has been innovating technologically, supported by a complex ecosystem including banks, financial service providers, and start-ups (link). Within this blogpost, we showcase our vision of AI in Financial Services. AI in Financial Services From our point of view, we can group use cases in AI in three distinct [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":2805,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[52,80],"tags":[44,83,95],"class_list":["post-2794","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry-overview","category-opinion","tag-ai4business","tag-artificial-intelligence","tag-financial-services"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.8 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Our vision of AI in Financial Services - NILG.AI<\/title>\n<meta name=\"description\" content=\"In recent years, the financial services industry has been innovating technologically, supported by a complex ecosystem. 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