Finance & Accounting

Artificial Intelligence in Finance and Open Banking

This course explores the integration of artificial intelligence (AI) within finance and open banking, focusing on practical applications, regulatory considerations, and risk management. Participants will develop skills to leverage AI for data analysis, customer insights, and decision-making in financial services.

5 days Classroom or live online English

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Download the full standard outline: why attend, methodology, objectives, audience, competencies, the daily outline and current plans.

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Why attend

From knowledge to real-world impact.

Financial institutions face increasing pressure to innovate and enhance customer experiences while managing risks. Understanding AI's role in finance and open banking is crucial for making informed decisions about technology adoption and compliance with regulations. This course addresses the challenges of integrating AI into existing systems and processes, ensuring participants can navigate these complexities effectively. Participants will engage in practical exercises that simulate real-world scenarios, allowing them to apply AI tools and techniques to financial data analysis, risk assessment, and customer engagement strategies. By developing these capabilities, attendees will be better equipped to drive innovation within their organisations and enhance their competitive edge in the evolving financial landscape.

The implementation of AI in finance is not merely a technological shift; it requires a deep understanding of regulatory frameworks, ethical considerations, and risk management strategies. Participants will learn to evaluate the implications of AI on customer privacy and data security while developing strategies to mitigate potential risks. This course will empower professionals to make data-driven decisions that align with regulatory requirements and organisational goals. By mastering AI applications in finance, participants will enhance their ability to optimise operations, improve customer satisfaction, and drive sustainable growth in their organisations. The skills gained will be directly applicable to their roles, fostering a culture of innovation and compliance.

Workplace challenges

Professional Problems This Course Is Designed to Solve

The programme explores practical approaches to the following challenges. Results depend on how the learning is applied in your organisation.

Difficulty translating subject knowledge into practical workplace decisions.
Inconsistent approaches to the activities covered in the course outline.
Lack of a structured plan for applying and reviewing new skills.

Course methodology

How you will learn.

This course employs a blend of case studies, hands-on workshops, and simulations to facilitate learning. Participants will analyse real-world financial datasets using AI tools, engage in group discussions to explore regulatory frameworks, and work on projects that require the application of AI techniques to solve specific financial problems. The outputs will include data analysis reports, risk assessment frameworks, and customer engagement strategies, demonstrating their practical understanding of AI in finance and open banking. Additionally, participants will collaborate in teams to develop solutions that address real-world challenges, enhancing their problem-solving and critical-thinking skills in a supportive learning environment. This interactive approach ensures that participants can apply their learning effectively in their professional contexts.

Course objectives

What you'll take away.

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

Analyse the impact of AI on financial services and open banking practices, focusing on customer experience.
Evaluate regulatory frameworks affecting AI implementation in finance, ensuring compliance and risk management.
Design AI-driven customer engagement strategies for financial institutions to enhance service delivery.
Assess risks associated with AI technologies in financial decision-making, identifying potential pitfalls.
Construct data analysis reports using AI tools for financial datasets, showcasing insights and trends.
Justify the adoption of AI solutions in enhancing operational efficiency in finance, aligning with business goals.
Compare traditional banking methods with AI-enhanced approaches in open banking, highlighting advantages and challenges.
Prioritise AI applications based on business needs and regulatory compliance, ensuring strategic alignment.

Target audience

Who should attend.

This course is suitable for:

Financial analysts seeking to enhance their data analysis skills with AI.
Compliance officers needing to understand AI regulations in finance.
Product managers in banking looking to innovate customer engagement.
Data scientists focusing on financial applications of AI.
Risk managers assessing AI-related risks in financial services.

Target competencies

Capabilities you will build.

Participants will develop competencies in:

Utilising AI tools for financial data analysis.
Understanding regulatory implications of AI in finance.
Developing customer-centric AI solutions in banking.
Evaluating the risks of AI technologies in finance.
Creating actionable insights from financial datasets.
Integrating AI strategies into financial decision-making.

Course outline

Your learning journey.

A focused, practical outline with time to explore, apply and reflect.

Day 1Introduction to AI in Finance and Open Banking

Applied activity: Participants will analyse a case study on a bank's AI implementation strategy, identifying key benefits and challenges faced during the process. They will then propose solutions to overcome identified barriers, considering both technological and organisational aspects.

Output: A summary report outlining the case study findings and recommendations for successful AI integration.

09:00–09:54Overview of AI technologies relevant to finance and banking.
09:54–10:48Understanding open banking and its implications for financial services.
10:48–11:00Key benefits of AI in enhancing customer experiences and operational efficiency.
11:00–11:30Coffee break
11:30–12:12Key benefits of AI in enhancing customer experiences and operational efficiency. (continued)
12:12–13:06Challenges in adopting AI in finance, including integration and cultural shifts.
13:06–14:00Regulatory landscape for AI in open banking, focusing on compliance requirements.

Day 2Data Analytics and AI Tools for Financial Insights

Applied activity: Using a provided financial dataset, participants will apply AI tools to perform customer segmentation and generate insights. They will present their findings to the group, discussing the implications of their analysis on marketing strategies and customer engagement.

Output: A data analysis report detailing customer segments and insights derived from the analysis, including visualisations.

09:00–09:54Introduction to data analytics in finance, focusing on key concepts and tools.
09:54–10:48AI tools for predictive analytics in banking, including machine learning techniques.
10:48–11:00Techniques for customer segmentation using AI, enhancing targeted marketing efforts.
11:00–11:30Coffee break
11:30–12:12Techniques for customer segmentation using AI, enhancing targeted marketing efforts. (continued)
12:12–13:06Data visualisation methods for financial data, improving stakeholder communication.
13:06–14:00Ethical considerations in financial data usage, ensuring responsible AI practices.

Day 3Regulatory Frameworks and Compliance in AI

Applied activity: Participants will work in groups to evaluate a hypothetical AI implementation against regulatory requirements. They will identify compliance gaps and propose corrective actions to ensure adherence, considering both legal and ethical dimensions.

Output: A compliance assessment report with recommendations for addressing identified gaps and ensuring regulatory alignment.

09:00–09:54Understanding GDPR and its impact on AI in finance, focusing on data protection.
09:54–10:48Key regulations governing open banking practices, including PSD2 and their implications.
10:48–11:00Risk management frameworks for AI technologies, ensuring compliance and safety.
11:00–11:30Coffee break
11:30–12:12Risk management frameworks for AI technologies, ensuring compliance and safety. (continued)
12:12–13:06Best practices for compliance in AI implementations, including documentation and auditing.
13:06–14:00Case studies of regulatory challenges in AI, analysing lessons learned.

Day 4Risk Assessment and Management of AI Technologies

Applied activity: Participants will create a risk assessment framework for an AI project in finance, considering various risk factors and mitigation strategies. They will present their framework to peers for feedback, fostering collaborative learning and improvement.

Output: A comprehensive risk assessment document for the proposed AI project, including identified risks and mitigation plans.

09:00–09:54Identifying risks associated with AI in finance, including operational and reputational risks.
09:54–10:48Developing risk assessment frameworks for AI applications, ensuring thorough evaluation.
10:48–11:00Mitigating operational risks in AI implementations, focusing on best practices.
11:00–11:30Coffee break
11:30–12:12Mitigating operational risks in AI implementations, focusing on best practices. (continued)
12:12–13:06Evaluating the impact of AI on financial stability, considering systemic risks.
13:06–14:00Case studies of AI-related failures in finance, analysing causes and prevention strategies.

Day 5Integrating AI into Financial Decision-Making

Applied activity: In teams, participants will design a comprehensive AI integration plan for a financial institution, addressing technology, people, and processes. They will present their plan to the class for evaluation, receiving constructive feedback from peers and instructors.

Output: A detailed AI integration plan that outlines steps, resources, and timelines for implementation, ready for organisational use.

09:00–09:54Strategies for implementing AI solutions in finance, focusing on change management.
09:54–10:48Measuring the impact of AI on business performance, using key performance indicators.
10:48–11:00Aligning AI initiatives with organisational goals, ensuring strategic fit.
11:00–11:30Coffee break
11:30–12:12Aligning AI initiatives with organisational goals, ensuring strategic fit. (continued)
12:12–13:06Future trends in AI and open banking, preparing for upcoming challenges and opportunities.
13:06–14:00Developing a roadmap for AI integration in finance, outlining key steps and milestones.

The timings provided are estimates and may vary depending on the number of participants and the discussions taking place during the sessions. They are indicative only and do not constitute a binding commitment.

Dates & venues

Find a session that works for you.

Confirmed dates accept registration requests. Tentative dates are enquiry only.

Good to know

A few things to know.

How is the course delivered?

Our classroom programmes combine facilitated discussion, case studies, exercises and reflection. The precise format will be confirmed before enrolment.

Can you deliver this programme for our team?

Yes, you can enquire about a tailored programme. Share your goals, group size and preferred dates with our learning team.

Will I receive a certificate?

Participants who complete the programme receive a Smart Horizon certificate of attendance. Smart Horizon programmes are not externally accredited and do not award a professional qualification, licence or designation. Where a course title or outline refers to a professional certification (for example, AACE International's Certified Cost Professional), the programme supports learning and preparation only; the credential itself is awarded solely by the relevant professional body under its own application and examination requirements.

What should I confirm before booking travel?

Please confirm the session, exact venue, accessibility arrangements, fees and any visa requirements with the team before arranging travel. Tentative dates are not confirmed sessions.

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