{"id":12411,"date":"2024-08-02T09:17:54","date_gmt":"2024-08-02T07:17:54","guid":{"rendered":"https:\/\/www.contovista.com\/products\/enrichment-engine\/ai-principles\/"},"modified":"2026-03-06T12:06:10","modified_gmt":"2026-03-06T11:06:10","slug":"ai-principles","status":"publish","type":"page","link":"https:\/\/www.contovista.com\/en\/products\/enrichment-engine\/ai-principles\/","title":{"rendered":"AI Principles"},"content":{"rendered":"<section height=\"710\" class=\"hero hero--diagonal   logo-color logo-color-white no-lazy\">\n  <div class=\"container container--small\">\n    <div class=\"hero__wrapper flex row jscb\">\n\n                          <div class=\"hero__info hero__info--no-image hero__info--small hero__info--for-small-image\">\n\n        <!-- Article data  -->\n                <!-- Article data  -->\n\n        <!-- Titles  -->\n                              <h1 class=\"white\">AI Principles<\/h1>\n                   \n\n                <!-- Titles  -->\n\n        <!-- Description  -->\n                  <p class=\"white hero__info-description-desktop\">For a responsible use of AI.<\/p>\n                          <p class=\"white hero__info-description-mobile\">For a responsible use of AI.<\/p>\n                <!-- Description  -->\n\n        <!-- Button  -->\n         \n        <!-- Button  -->\n\n        <!-- Clients slider  -->\n                <!-- Clients slider  -->\n\n\n      <\/div>\n\n      <!-- Image \/ Lottie  -->\n            <!-- Image \/ Lottie  -->\n\n    <\/div>\n  <\/div>\n<\/section> \n\n\n \n\n\r\n\r\n<div class=\"breadcrumbs\">\r\n  <div class=\"container\">\r\n    <div class=\"breadcrumbs__wrapper flex row aic\">\r\n      <nav aria-label=\"breadcrumbs\" class=\"rank-math-breadcrumb\"><p><span class=\"last\">Home<\/span><\/p><\/nav>    <\/div> \r\n  <\/div>\r\n<\/div>\r\n\r\n\n\n\r\n\n\n\r\n\n\n\r\n<section class=\"title-copy section logo-color logo-color-dark\"  >\r\n  <div class=\"container\">\r\n    <div class=\"title-copy__wrapper\">\r\n          <h2>Data-driven Banking through responsible AI<\/h2>\r\n          <div class=\"textbox\">\r\n        <p>We shape the financial industry into a seamless and secure ecosystem to unlock its full potential. Therefore, we&#8217;re committed to leveraging the power of data and AI for the benefit of our customers. To ensure ethical and responsible development and use of AI, we have established clear AI principles which guide our approach.<\/p>      <\/div>\r\n    <\/div>\r\n  <\/div>\r\n<\/section>\n\n \r\n<section class=\"section ol\" >\r\n\t<div class=\"container\">\r\n\t\t<ol>\r\n\t\t\t\t<li><a href=\"# aiprinciples\">Our AI Principles<\/a><\/li>\r\n\t\t\t\t\t<li><a href=\"#customerexperience\">AI for a better customer experience<\/a><\/li>\r\n\t\t\t\t\t<li><a href=\"#ai_faq\">Frequently Asked Questions<\/a><\/li>\r\n\t\t\t\t<\/ol>\r\n\t\t<\/div>\r\n<\/section>\r\n<script>\r\n\tlet h = 0;\r\n\tfor(let a of document.querySelectorAll(\".section.ol a\")){\r\n\t\th = Math.max(h, a.offsetHeight);\r\n\t}\r\n\tfor(let a of document.querySelectorAll(\".section.ol a\")){\r\n\t\ta.style.minHeight = h + \"px\";\r\n\t}\r\n<\/script>\n\n\r\n<section class=\"title-copy section logo-color logo-color-dark\" id=\" aiprinciples\" >\r\n  <div class=\"container\">\r\n    <div class=\"title-copy__wrapper\">\r\n          <h2>Our AI Principles<\/h2>\r\n          <div class=\"textbox\">\r\n        <p>These principles serve as a foundation for building trust and transparency, allowing us to develop and implement responsible AI solutions across our data-driven banking offerings.<\/p>      <\/div>\r\n    <\/div>\r\n  <\/div>\r\n<\/section>\n\n\r\n<section class=\"image section logo-color logo-color-dark\" >\r\n  <div class=\"container\">\r\n    <div class=\"image__wrapper\">\r\n\r\n  \r\n      <img decoding=\"async\" \r\n        src=\"https:\/\/www.contovista.com\/wp-content\/uploads\/2024\/08\/20240723-Contovista-Blog-Post-AI-Principles_3_EN-e1722585245778.jpg\" \r\n        alt=\"\"\r\n        caption=\"\"\r\n        description=\"\">\r\n\r\n              <p class=\"text14 dark-sky-80\">Contovista\u00b4s AI Principles at a Glance.<\/p>\r\n      \r\n      \r\n    <\/div>\r\n  <\/div>\r\n<\/section>  \n\n \n\n<section class=\"section columns logo-color logo-color-dark\" >\n  <div class=\"container\">\n      <div class=\"columns__wrapper flex row jcsb fww\">\n\n              \n          \n          <div class=\"columns__column\">\n\n            \n            \n            \n          <\/div>\n\n        \n          \n          <div class=\"columns__column\">\n\n            \n                          <h3 class=\"fw700 h4 columns__column-title flex\"> \n                                <span>Privacy by design<\/span>\n              <\/h3>\n            \n                          <p class=\"dark-sky-80\">All models are based on non-CID data. Processing is GDPR-compliant and prepared for future regulations such as the EU AI Act.<\/p>\n            \n          <\/div>\n\n        \n          \n          <div class=\"columns__column\">\n\n            \n                          <h3 class=\"fw700 h4 columns__column-title flex\"> \n                                <span>Shared Learning Effect<\/span>\n              <\/h3>\n            \n                          <p class=\"dark-sky-80\">The model quality is improved through continuous training with anonymized data. This results in more robust models that significantly reduce bias and overfitting.<\/p>\n            \n          <\/div>\n\n        \n          \n          <div class=\"columns__column\">\n\n            \n                          <h3 class=\"fw700 h4 columns__column-title flex\"> \n                                <span>Explainability &amp; Control<\/span>\n              <\/h3>\n            \n                          <p class=\"dark-sky-80\">Our AI is fully comprehensible. All models are documented, versioned and auditable. Banks manage the configuration and scope of application according to their own risk and compliance strategy.<\/p>\n            \n          <\/div>\n\n         \n      \n    <\/div>\n      \n        <!-- Button  -->\n             \n    <!-- Button  -->\n  <\/div>\n<\/section>\n\n\n\r\n<section class=\"title-copy section logo-color logo-color-dark\" id=\"customerexperience\" >\r\n  <div class=\"container\">\r\n    <div class=\"title-copy__wrapper\">\r\n          <h2>AI for a better customer experience: precise, scalable, self-learning<\/h2>\r\n          <div class=\"textbox\">\r\n        <p>At Contovista, AI is a production-grade capability. It is purpose built to deliver superior customer experiences at scale. Our models combine deterministic accuracy with adaptive learning. They transform raw transaction data into reliable financial intelligence.<\/p>\n<ul>\n<li><strong>Generative AI and LLMs for Transaction Enrichment<\/strong><br \/>\nLLM-based pipelines standardise transaction data and translate it into clear, human-readable information. They generate meaningful labels such as Pretty Names, enrich transactions with logos, and assign accurate merchant and counterparty categories. The result is a continuously evolving enrichment database. It forms the foundation for explainable analytics, high-quality insights, and downstream AI applications.<\/li>\n<li><strong>Generative AI and LLMs for Customer and User-Facing Solutions<\/strong><br \/>\nLLMs enable knowledge-intensive and language-driven use cases across Contovista Finance Management and customer solutions.<br \/>\nOn the bank side, agentic assistants support internal teams. They leverage proprietary transaction enrichment capabilities and insights while applying financial domain expertise, business logic, and contextual customer knowledge. On the end-user side, LLM-based workflows power the <a href=\"https:\/\/www.contovista.com\/en\/products\/personal-finance-manager\/\">AI Finance Manager<\/a>. Capabilities such as the AI Financial Analyst engage users in intelligent, contextual conversations about transactions, income, and expense patterns.<\/li>\n<li><strong>Supervised Learning for Predictive Financial Intelligence<\/strong><br \/>\nSupervised machine-learning models, including regression techniques, gradient boosting, and random forests, predict expenses, upcoming obligations, travel spend, and safety buffers with high reliability.\u00a0These predictions drive Finance Manager insights for end users. They also provide structured analytics for banks across advisory, personalisation, and risk-related use cases.<\/li>\n<li><strong>Unsupervised Learning for Segmentation and Pattern Discovery<\/strong><br \/>\nClustering algorithms identify customer segments based on behavioural, life-event, and financial patterns. They support internal data-quality improvements and enable customer targeting and product optimisation. In parallel, unsupervised NLP pipelines using n-grams, embeddings, and related techniques form a core component of the enrichment engine. They continuously refine categorisation and semantic understanding.<\/li>\n<li><strong>Probabilistic and Stochastic Models for Robust Forecasting<\/strong><br \/>\nProbabilistic and stochastic models capture uncertainty and behavioural variability in real-world financial data.<br \/>\nThey enable robust forecasts of liquidity, income, expenses, and safety buffers. These models form the foundation for downstream intelligent algorithms used by financial institutions and end users.\u00a0 Shared learning effects also support cross-geographical profiling and benchmarking. The result is comparative insight with strategic value for banks and tangible relevance for end users.<\/li>\n<\/ul>      <\/div>\r\n    <\/div>\r\n  <\/div>\r\n<\/section>\n\n\n<section class=\"section faq logo-color logo-color-dark\" itemscope itemtype=\"https:\/\/schema.org\/FAQPage\" id=\"ai_faq\">\n  <div class=\"container\">\n          <header class=\"faq__header\">\n                  <h2 class=\"faq__title\" itemprop=\"name\">FAQ<\/h2>\n        \n              <\/header>\n    \n          <div class=\"faq__wrapper\">\n                  <div class=\"faq__item\" data-index=\"1\" itemprop=\"mainEntity\" itemscope itemtype=\"https:\/\/schema.org\/Question\">\n            <details class=\"faq__disclosure\">\n              <summary class=\"faq__question\">\n                <span class=\"faq__question-text\" itemprop=\"name\" role=\"heading\" aria-level=\"3\">Which AI technologies do you use?<\/span>\n                <span class=\"faq__icon\" aria-hidden=\"true\">\n                  <svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                    <path d=\"M6 9L12 15L18 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/>\n                  <\/svg>\n                <\/span>\n              <\/summary>\n              \n              <div class=\"faq__answer-wrapper\">\n                <div class=\"faq__answer\" itemprop=\"acceptedAnswer\" itemscope itemtype=\"https:\/\/schema.org\/Answer\">\n                  <div class=\"faq__answer-content\" itemprop=\"text\">\n                                        <div class=\"textbox\"><p><strong>We use a broad spectrum of AI technologies, selected based on the specific use case.<\/strong><\/p>\n<p>Traditional machine learning as well as probabilistic and stochastic models are used for forecasting, classification, segmentation, and risk-related analytics.<\/p>\n<p>LLMs and Generative AI are applied to language- and knowledge-intensive tasks. These include data-quality improvements, customer-facing solutions such as assistants for bank-internal risk functions, and end-user capabilities such as the AI Financial Analyst within the AI Finance Manager.<\/p>\n<p>In general, the technology stack is chosen based on the problem to be solved, whether internal, customer-facing, or end-user-facing.<\/p><\/div>\n                  <\/div>\n                <\/div>\n              <\/div>\n            <\/details>\n          <\/div>\n                  <div class=\"faq__item\" data-index=\"2\" itemprop=\"mainEntity\" itemscope itemtype=\"https:\/\/schema.org\/Question\">\n            <details class=\"faq__disclosure\">\n              <summary class=\"faq__question\">\n                <span class=\"faq__question-text\" itemprop=\"name\" role=\"heading\" aria-level=\"3\">Are your models based on big-tech LLM services?<\/span>\n                <span class=\"faq__icon\" aria-hidden=\"true\">\n                  <svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                    <path d=\"M6 9L12 15L18 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/>\n                  <\/svg>\n                <\/span>\n              <\/summary>\n              \n              <div class=\"faq__answer-wrapper\">\n                <div class=\"faq__answer\" itemprop=\"acceptedAnswer\" itemscope itemtype=\"https:\/\/schema.org\/Answer\">\n                  <div class=\"faq__answer-content\" itemprop=\"text\">\n                                        <div class=\"textbox\"><p><strong>No, not by default.<\/strong><\/p>\n<p>Model selection depends on the specific use case. We primarily rely on open-source models and libraries, particularly where on-premise deployment, performance, cost control, and data governance are critical.<\/p>\n<p>For highly language- or knowledge-intensive tasks, selected closed-source LLM services may be used where they provide a clear and measurable advantage.<\/p><\/div>\n                  <\/div>\n                <\/div>\n              <\/div>\n            <\/details>\n          <\/div>\n                  <div class=\"faq__item\" data-index=\"3\" itemprop=\"mainEntity\" itemscope itemtype=\"https:\/\/schema.org\/Question\">\n            <details class=\"faq__disclosure\">\n              <summary class=\"faq__question\">\n                <span class=\"faq__question-text\" itemprop=\"name\" role=\"heading\" aria-level=\"3\">Do you use open-source, closed-source, or proprietary models?<\/span>\n                <span class=\"faq__icon\" aria-hidden=\"true\">\n                  <svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                    <path d=\"M6 9L12 15L18 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/>\n                  <\/svg>\n                <\/span>\n              <\/summary>\n              \n              <div class=\"faq__answer-wrapper\">\n                <div class=\"faq__answer\" itemprop=\"acceptedAnswer\" itemscope itemtype=\"https:\/\/schema.org\/Answer\">\n                  <div class=\"faq__answer-content\" itemprop=\"text\">\n                                        <div class=\"textbox\"><p><strong>We use all three, depending on the problem:<\/strong><\/p>\n<ul>\n<li>Open-source models form the foundation of many AI capabilities<\/li>\n<li>Proprietary models are developed in-house for traditional machine learning and domain-specific tasks<\/li>\n<li>Closed-source LLMs are selectively used where they deliver clear benefits<\/li>\n<\/ul>\n<p>Decisions are driven by accuracy, security, deployment constraints such as on-premise requirements, latency, and cost.<\/p><\/div>\n                  <\/div>\n                <\/div>\n              <\/div>\n            <\/details>\n          <\/div>\n                  <div class=\"faq__item\" data-index=\"4\" itemprop=\"mainEntity\" itemscope itemtype=\"https:\/\/schema.org\/Question\">\n            <details class=\"faq__disclosure\">\n              <summary class=\"faq__question\">\n                <span class=\"faq__question-text\" itemprop=\"name\" role=\"heading\" aria-level=\"3\">Do you fine-tune or adapt models for your use cases?<\/span>\n                <span class=\"faq__icon\" aria-hidden=\"true\">\n                  <svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                    <path d=\"M6 9L12 15L18 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/>\n                  <\/svg>\n                <\/span>\n              <\/summary>\n              \n              <div class=\"faq__answer-wrapper\">\n                <div class=\"faq__answer\" itemprop=\"acceptedAnswer\" itemscope itemtype=\"https:\/\/schema.org\/Answer\">\n                  <div class=\"faq__answer-content\" itemprop=\"text\">\n                                        <div class=\"textbox\"><p><strong>Yes. Models are trained, fine-tuned, or adapted depending on the task, either during development or continuously over time.<\/strong><\/p>\n<p>This includes domain and data adaptation, model training and fine-tuning, as well as prompt and pipeline optimisation.<\/p><\/div>\n                  <\/div>\n                <\/div>\n              <\/div>\n            <\/details>\n          <\/div>\n                  <div class=\"faq__item\" data-index=\"5\" itemprop=\"mainEntity\" itemscope itemtype=\"https:\/\/schema.org\/Question\">\n            <details class=\"faq__disclosure\">\n              <summary class=\"faq__question\">\n                <span class=\"faq__question-text\" itemprop=\"name\" role=\"heading\" aria-level=\"3\">Do you deliver AI as SaaS or on-premise?<\/span>\n                <span class=\"faq__icon\" aria-hidden=\"true\">\n                  <svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                    <path d=\"M6 9L12 15L18 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/>\n                  <\/svg>\n                <\/span>\n              <\/summary>\n              \n              <div class=\"faq__answer-wrapper\">\n                <div class=\"faq__answer\" itemprop=\"acceptedAnswer\" itemscope itemtype=\"https:\/\/schema.org\/Answer\">\n                  <div class=\"faq__answer-content\" itemprop=\"text\">\n                                        <div class=\"textbox\"><p>Both options are supported.\u00a0AI capabilities can be provided as a SaaS offering or deployed via customer-side installations, depending on regulatory, security, and infrastructure requirements.<\/p><\/div>\n                  <\/div>\n                <\/div>\n              <\/div>\n            <\/details>\n          <\/div>\n                  <div class=\"faq__item\" data-index=\"6\" itemprop=\"mainEntity\" itemscope itemtype=\"https:\/\/schema.org\/Question\">\n            <details class=\"faq__disclosure\">\n              <summary class=\"faq__question\">\n                <span class=\"faq__question-text\" itemprop=\"name\" role=\"heading\" aria-level=\"3\">Where is AI hosted, and how do you ensure data security?<\/span>\n                <span class=\"faq__icon\" aria-hidden=\"true\">\n                  <svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                    <path d=\"M6 9L12 15L18 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/>\n                  <\/svg>\n                <\/span>\n              <\/summary>\n              \n              <div class=\"faq__answer-wrapper\">\n                <div class=\"faq__answer\" itemprop=\"acceptedAnswer\" itemscope itemtype=\"https:\/\/schema.org\/Answer\">\n                  <div class=\"faq__answer-content\" itemprop=\"text\">\n                                        <div class=\"textbox\"><p>Depending on the customer setup, deployments run on Swiss cloud infrastructure or fully on-premise.<\/p>\n<p>All solutions comply with GDPR, FINMA requirements, and the EU AI Act. We apply state-of-the-art cryptographic standards to ensure data protection and security at all times.<\/p><\/div>\n                  <\/div>\n                <\/div>\n              <\/div>\n            <\/details>\n          <\/div>\n              <\/div>\n      <\/div>\n<\/section>\n\n\n\n\n\r\n<section class=\"title-copy section logo-color logo-color-dark\"  >\r\n  <div class=\"container\">\r\n    <div class=\"title-copy__wrapper\">\r\n          <div class=\"textbox\">\r\n              <\/div>\r\n    <\/div>\r\n  <\/div>\r\n<\/section>\n\n\r\n<section \r\n  style=\"background: linear-gradient(90deg, rgba(34, 236, 187, 0.5) 0%, rgba(6, 78, 96, 0.5) 28.12%, rgba(0, 41, 75, 0.5) 100%),url(https:\/\/www.contovista.com\/wp-content\/uploads\/2022\/10\/Meeting-scaled-e1728900134849.jpg);background-position: center; background-repeat:no-repeat; background-size: cover;\"\r\n  class=\"section contact contact--diagonal-down contact--footer-stick logo-color logo-color-white\"  >\r\n  <div class=\"container\">\r\n    <div class=\"contact__wrapper\">\r\n          <h2 class=\"white\">Let\u2019s talk about responsible AI in banking<\/h2>\r\n          <p class=\"fw700 white\">Want to understand how our AI solutions deliver real value while meeting regulatory requirements? Talk to our experts and explore what this means for your organisation.<\/p>\r\n\r\n      <!-- Button  -->\r\n       \r\n                  <a \r\n        href=\"https:\/\/www.contovista.com\/en\/contact\/\" \r\n        class=\"btn btn--gradient\"\r\n        data-modal=\"modal-contacts\"\r\n        target=\"_self\">\r\n        Get in touch \r\n        <span class=\"btn--gradient--span1\"><\/span>\r\n        <span class=\"btn--gradient--span2\"><\/span>\r\n      <\/a>\r\n      \r\n            <!-- Button  -->\r\n\r\n\r\n    <\/div>\r\n  <\/div>\r\n<\/section>","protected":false},"excerpt":{"rendered":"","protected":false},"author":4,"featured_media":6402,"parent":2623,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-12411","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.contovista.com\/en\/wp-json\/wp\/v2\/pages\/12411","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.contovista.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.contovista.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.contovista.com\/en\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.contovista.com\/en\/wp-json\/wp\/v2\/comments?post=12411"}],"version-history":[{"count":17,"href":"https:\/\/www.contovista.com\/en\/wp-json\/wp\/v2\/pages\/12411\/revisions"}],"predecessor-version":[{"id":15501,"href":"https:\/\/www.contovista.com\/en\/wp-json\/wp\/v2\/pages\/12411\/revisions\/15501"}],"up":[{"embeddable":true,"href":"https:\/\/www.contovista.com\/en\/wp-json\/wp\/v2\/pages\/2623"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.contovista.com\/en\/wp-json\/wp\/v2\/media\/6402"}],"wp:attachment":[{"href":"https:\/\/www.contovista.com\/en\/wp-json\/wp\/v2\/media?parent=12411"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}