AI for Finance Training in Radom

AI in Financial Services is transforming the way institutions manage risk, strategy, compliance, and customer experience.

This instructor-led, live training (online or onsite) is aimed at intermediate-level financial and technology professionals who wish to understand how AI impacts strategy, ethics, and regulation in the financial sector.

By the end of this training, participants will be able to:

• Understand the strategic applications of AI in financial services.
• Evaluate the ethical and regulatory implications of AI adoption.
• Develop frameworks for responsible AI governance and oversight.
• Align AI strategies with organizational goals and compliance requirements.

Format of the Course

• Interactive lecture and discussion.
• Case studies and group exercises.
• Practical analysis of real-world AI regulatory frameworks.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Radom (online or onsite) is aimed at intermediate-level financial professionals, risk analysts, and AI engineers who wish to develop and deploy AI-driven solutions for financial automation and fraud detection.

By the end of this training, participants will be able to:

• Understand the role of AI in financial automation and fraud detection.
• Build AI models for detecting fraudulent transactions.
• Leverage machine learning for real-time risk assessment.
• Deploy AI-powered financial monitoring systems.

Ollama is a lightweight platform for running large language models locally.

This instructor-led, live training (online or onsite) is aimed at intermediate-level finance practitioners and IT personnel who wish to implement, customize, and operationalize Ollama-based AI solutions in financial environments.

By completing this training, participants will gain the skills needed to:

• Deploy and configure Ollama for secure use in financial operations.
• Integrate local LLMs into analytical and reporting workflows.
• Adapt models to finance-specific terminology and tasks.
• Apply security, privacy, and compliance best practices.

Format of the Course

• Interactive lecture and discussion.
• Hands-on financial data exercises.
• Live-lab implementation of finance-focused scenarios.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Artificial Intelligence is a technology that enables the development of digital products with advanced data-driven capabilities and personalized customer experiences.

This instructor-led, live training (online or onsite) combined with asynchronous activities and in-person workshops is aimed at intermediate-level banking professionals who wish to design, develop, and deliver AI-powered digital products effectively.

By the end of this training, participants will be able to:

• Identify customer needs and define a clear product vision.
• Apply AI technologies to enhance digital banking products.
• Use agile and design thinking methods to create user-centered solutions.
• Measure, iterate, and optimize product performance for sustained value.

Format of the Course

• 50% synchronous classes (virtual or in-person).
• 25% asynchronous activities (videos, readings, forums).
• 25% in-person practical workshop with case studies.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Copilot for Microsoft 365 is an AI-powered assistant that integrates with Microsoft Office applications to help finance and accounting professionals enhance productivity, streamline reporting, and support decision-making.

This instructor-led, live training (online or onsite) is aimed at finance and accounting professionals with limited experience with AI who wish to leverage Microsoft Copilot to improve accuracy, efficiency, and insight in financial tasks.

By the end of this training, participants will be able to:

• Create financial reports quickly and accurately using Copilot.
• Automate repetitive accounting and data-entry tasks.
• Generate financial summaries and forecasts with AI assistance.
• Use natural language prompts to extract insights from large financial datasets.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Applied AI introduces new levels of efficiency and intelligence to the way finance professionals analyze and report on business performance.

This instructor-led, live training (online or onsite) is aimed at intermediate-level finance professionals who wish to integrate AI tools into their financial statement workflows to enhance accuracy, automate repetitive tasks, and gain forward-looking insights.

By the end of this training, participants will be able to:

• Automate data extraction from financial documents using AI tools.
• Apply machine learning models to analyze trends and anomalies in financial statements.
• Use generative AI to assist in variance commentary, narrative reports, and scenario simulation.
• Interpret AI-generated outputs responsibly in the context of finance reporting and planning.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

WrenAI provides finance teams with the ability to model KPIs, integrate standardized metrics, and design dashboards that align with regulatory requirements and audit standards.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level finance professionals who wish to use WrenAI for building compliant financial data models and dashboards that support decision-making and risk management.

By the end of this training, participants will be able to:

• Model financial KPIs and metrics in WrenAI.
• Build dashboards aligned with regulatory and audit requirements.
• Integrate WrenAI with finance data sources for real-time reporting.
• Apply best practices for financial analytics and risk monitoring.

Format of the Course

• Interactive lecture and discussion.
• Hands-on exercises with financial data models.
• Practical labs on dashboard design and compliance reporting.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AI is transforming FinTech by enabling intelligent automation, hyper-personalization, and secure real-time financial services.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level FinTech professionals who wish to explore the convergence of AI, APIs, and Open Banking innovations to design next-generation financial products.

By the end of this training, participants will be able to:

• Understand how AI and machine learning are applied across FinTech use cases.
• Leverage Open Banking APIs and data aggregation for product innovation.
• Design AI-driven features for digital wallets, neobanks, and financial assistants.
• Align innovation with regulatory, ethical, and security considerations.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AI is transforming how financial institutions detect fraud and combat money laundering through intelligent, real-time analysis of vast transaction datasets.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to apply machine learning and AI tools to automate and enhance financial crime detection, compliance monitoring, and operational governance.

By the end of this training, participants will be able to:

• Understand AI use cases in fraud detection and AML monitoring.
• Design and implement models for anomaly detection and transaction scoring.
• Leverage graph-based AI for network risk detection.
• Ensure ethical, explainable, and regulatory-compliant model deployment.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AI is transforming WealthTech by enabling highly personalized financial services, intelligent advisory platforms, and enhanced user experiences.

This instructor-led, live training (online or onsite) is aimed at intermediate-level finance and technology professionals who wish to design, evaluate, or implement AI-driven solutions for personalized wealth management and robo-advisory services.

By the end of this training, participants will be able to:

• Understand how AI is applied in wealth management and digital advisory platforms.
• Design intelligent systems for personalized portfolio recommendations.
• Incorporate behavioral finance data and preferences into advisory algorithms.
• Evaluate ethical and regulatory concerns in automated investment advice.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

LangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and control over execution.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to design, implement, and operate LangGraph-based finance solutions with proper governance, observability, and compliance.

By the end of this training, participants will be able to:

• Design finance-specific LangGraph workflows aligned to regulatory and audit requirements.
• Integrate financial data standards and ontologies into graph state and tooling.
• Implement reliability, safety, and human-in-the-loop controls for critical processes.
• Deploy, monitor, and optimize LangGraph systems for performance, cost, and SLAs.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Radom (online or onsite) is aimed at intermediate-level finance professionals, data analysts, risk managers, and AI engineers who wish to leverage multimodal AI for risk analysis and fraud detection.

By the end of this training, participants will be able to:

• Understand how multimodal AI is applied in financial risk management.
• Analyze structured and unstructured financial data for fraud detection.
• Implement AI models to identify anomalies and suspicious activities.
• Leverage NLP and computer vision for financial document analysis.
• Deploy AI-driven fraud detection models in real-world financial systems.

AI is transforming how financial institutions assess creditworthiness, price risk, and optimize lending decisions.

This instructor-led, live training (online or onsite) is aimed at intermediate-level finance professionals who wish to apply artificial intelligence to enhance credit scoring models, manage risk more effectively, and improve lending operations.

By the end of this training, participants will be able to:

• Understand key AI methods used in credit scoring and risk prediction.
• Build and evaluate credit scoring models using machine learning algorithms.
• Interpret model outputs for regulatory compliance and transparency.
• Apply AI techniques to improve underwriting, loan approval, and portfolio management.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Radom (online or onsite) is aimed at finance professionals who wish to use ChatGPT to streamline their workflows and improve their data analysis and reporting capabilities.

By the end of this training, participants will be able to:

• Understand the basics of ChatGPT and how it works.
• Use ChatGPT to automate financial tasks such as data entry and report generation.
• Analyze financial data with ChatGPT to gain insights and make informed decisions.
• Develop custom ChatGPT models for specific financial use cases.

Generative AI is a class of artificial intelligence techniques used to generate new content or predictions from existing data, including Large Language Models (LLMs) and Generative Adversarial Networks (GANs).

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level finance professionals who wish to apply generative AI for forecasting, anomaly detection, and compliance in financial services.

By the end of this training, participants will be able to:

• Understand the foundational concepts behind generative AI models.
• Apply LLMs and GANs to use cases such as fraud detection and synthetic data generation.
• Design effective prompts for financial forecasting and reporting support.
• Evaluate ethical and regulatory considerations in generative AI applications.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Radom (online or onsite) is aimed at intermediate-level finance professionals and fintech developers who wish to use AI-powered prompt engineering techniques to enhance financial analysis, risk management, and decision-making.

By the end of this training, participants will be able to:

• Understand the fundamentals of prompt engineering in financial applications.
• Leverage AI models for financial forecasting and market sentiment analysis.
• Automate financial reporting and data extraction using AI prompts.
• Develop AI-driven risk assessment models through optimized prompts.
• Ensure compliance and ethical considerations when using AI in finance.

Artificial Intelligence is a powerful set of techniques used to develop intelligent trading systems that analyze market data, make predictions, and execute strategies autonomously.

This instructor-led, live training (online or onsite) is aimed at intermediate-level finance professionals who wish to apply AI techniques in trading and asset management, focusing on signal generation, portfolio optimization, and algorithmic strategies.

By the end of this training, participants will be able to:

• Understand the role of AI in modern financial markets.
• Use Python to build and backtest algorithmic trading strategies.
• Apply supervised and unsupervised learning models to financial data.
• Optimize portfolios using AI-driven techniques.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Machine Learning is a subset of Artificial Intelligence focused on creating systems that learn from data and make predictions or decisions without being explicitly programmed.

This instructor-led, live training (online or onsite) is aimed at intermediate-level finance professionals who wish to apply machine learning and AI techniques to real-world problems such as fraud detection, credit scoring, and risk modeling.

By the end of this training, participants will be able to:

• Understand the core machine learning concepts relevant to finance.
• Apply supervised and unsupervised learning algorithms to financial datasets.
• Build and evaluate predictive models for credit risk, fraud detection, and market analysis.
• Use Python and scikit-learn to implement machine learning pipelines.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AI in Financial Services is a strategic enabler for risk reduction, customer experience, and operational efficiency.

This instructor-led, live training (online or onsite) is aimed at financial services executives, fintech managers, and compliance officers with limited prior exposure to artificial intelligence who wish to understand how to responsibly and effectively implement AI solutions in their institutions.

By the end of this training, participants will be able to:

• Understand the strategic value of AI in financial services.
• Identify and mitigate ethical risks associated with AI models.
• Navigate the regulatory landscape for AI in finance.
• Design responsible AI governance and implementation frameworks.

Format of the Course

• Interactive lecture and discussion.
• Case study analysis and group exercises.
• Application of ethical frameworks to realistic financial scenarios.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AI is rapidly becoming the foundation of modern product management — transforming how we discover customer needs, define solutions, and make decisions. Just as electricity revolutionized industry, Artificial Intelligence is reshaping how products are built, validated, and evolved.

This instructor-led, live training (online or onsite) is aimed at Product Owners, Scrum Masters, and Agile Coaches who want to understand how AI can enhance product ownership and strengthen decision-making.

By the end of this training, participants will be able to:

• Understand the fundamentals of Artificial Intelligence and differentiate between traditional AI and Generative AI (GenAI).
• Apply the seven preferred stances of a Product Owner (including the new Orchestrator stance) and use AI to strengthen each stance — such as Visionary, Experimenter, or Customer Representative.
• Master effective prompting and treat AI tools as intelligent collaborators with specialized skills.
• Deepen customer understanding and create AI-supported personas for hypothesis testing and discovery, while maintaining authentic customer contact.
• Develop and communicate a clear product vision using structured frameworks like the 3x3 Framework, leveraging AI to shape and visualize narratives.
• Use Generative AI tools to accelerate prototyping and hypothesis testing, such as generating mock-ups or product concepts.
• Support product decisions by using AI tools to analyze, prioritize, and evaluate features based on value, feasibility, and user impact.
• Automate or delegate routine daily tasks (emails, stakeholder management, note-taking) using AI to focus on strategic and creative work.
• Lead discussions on AI ethics, bias, and data security, ensuring responsible and sustainable AI adoption.
• Identify and design valuable AI use cases tailored to the specific product and organizational context.
• Explore and experiment with around 20 different AI tools and models — from productivity assistants and prototyping platforms to generative video, image, and coding solutions.