Thirteen courses to understand artificial intelligence and put it to work across your business: generative AI, management and leadership, regulation, banking, insurance and operational excellence.
The Generative Artificial Intelligence Ecosystem
Understand the technologies, players, uses and framework of generative AI to make informed decisions
Description
Generative artificial intelligence produces text, images, sound or code from a simple instruction. It has spread through companies in a few years, often faster than the internal rules meant to govern it.
This two-day course gives the reference points needed to find your way: how models work, who the players are, which uses create value, which risks must be controlled and which rules apply. It is designed for those who choose, steer or oversee projects, with no technical skills required.
Learning objectives
By the end of the course, participants will be able to:
- Explain how a large language model works and its limits
- Place the main players, models and types of tools on the market
- Identify high-value uses in their organisation
- Distinguish assistants, retrieval based on company documents and agents
- Assess the risks: errors, bias, confidentiality, intellectual property, security
- Place the applicable framework, including the EU Artificial Intelligence Act and the GDPR, and the basics of AI governance
Teaching methods and resources
Presentations with live demonstrations, tool comparisons on practical cases, group workshops and experience sharing. Materials provided: ecosystem map, use case assessment grid, risk matrix and an AI acceptable use policy template.
Course programme
1. Understanding generative AI
- Place generative AI in the history of artificial intelligence
- Understand training, tokens and the context window
- Explain why a model can be confidently wrong
- Distinguish text, image, audio, video and code
Practical work: annotated demonstration of several models answering the same question, and analysis of the differences.
2. Players and models
- Place the major US, European and Chinese providers
- Distinguish proprietary and open-weight models
- Compare consumer, business and API offerings
- Identify selection criteria: quality, cost, hosting, sovereignty
Practical work: mapping the tools already used in participants’ organisations.
3. Uses and architectures in the enterprise
- Identify uses: drafting, summarising, research, analysis, code
- Understand retrieval based on company documents
- Place agents that can chain actions
- Measure the value and cost of a use case
Practical work: selecting and prioritising three use cases for a company department.
4. Risks and regulatory framework
- Control errors and invented content
- Prevent bias and protect confidential data
- Place intellectual property and security issues
- Place the EU AI Act, its risk-based approach and phased timetable, and the GDPR
Practical work: risk analysis of a use case and definition of the related controls.
5. Governing and deploying
- Draft an AI acceptable use policy
- Define roles: business, IT, compliance, legal
- Place ISO/IEC 42001 on AI management systems
- Build a deployment roadmap
Practical work: drafting an acceptable use policy and a six-month roadmap.
Participants
Executives, managers, project managers, business leads, and compliance, legal, risk and IT teams who decide on or oversee generative AI use.
Prerequisites
No technical knowledge is required. Some experience using an AI assistant helps but is not essential.
Trainer credentials
The experts who deliver our courses are specialists in the subjects covered. They are validated by our teaching teams on both subject knowledge and teaching ability, for each course they deliver. They have at least ten years of experience in banking or consulting and hold, or have held, positions of responsibility.
Assessment
The trainer assesses progress throughout the course through quizzes, case studies and simulations. Participants also complete a positioning test before and after the course to validate the skills acquired.
Teaching resources and materials
- Audiovisual aids, documentation and course materials, applied practical exercises with worked solutions, case studies and presentation of real cases.
- At the end of each course, Helios Advisory provides participants with a course evaluation questionnaire, which is then analysed by our teaching teams.
- An attendance sheet per half-day is provided at the end of the course, together with a certificate of completion where the participant has attended the full session.
Enrolment and lead times
Enrolment is finalised with our teaching team. Send us your request through the contact form: we will confirm the next available sessions and the enrolment arrangements suited to your situation.
Accessibility
For any question or requirement relating to accessibility, contact us through the contact form. Each situation is reviewed with you so that the course arrangements can be adapted.
Prompt Engineering: Working Effectively with Artificial Intelligence
Write precise instructions to get reliable, useful answers from AI assistants
Description
The quality of an AI answer depends largely on how it is asked. A vague instruction gets a generic answer; a precise instruction, with context and an expected format, gets a result that can be used straight away.
This two-day course is mainly hands-on. Participants learn to structure their requests, iterate, check answers and build a library of reusable instructions. Exercises use assistants available on the market, and the techniques taught apply to every tool.
Learning objectives
By the end of the course, participants will be able to:
- Structure an instruction: context, role, task, constraints and output format
- Use examples to guide the answer
- Break a complex task down into steps
- Improve an answer through successive iterations
- Check the reliability of an answer and spot errors
- Use AI responsibly, without exposing confidential data
Teaching methods and resources
Hands-on workshops with AI assistants, based on cases drawn from participants’ daily work. Systematic comparison between a simple instruction and a structured one. Materials provided: instruction-building method, library of standard instructions, answer assessment grid and a confidentiality good-practice memo.
Course programme
1. The basics of a good instruction
- Understand how an assistant interprets a request
- Provide context and a role
- Specify the task, audience and constraints
- Define the output format: list, table, email, summary
Practical work: rewriting five vague requests as structured instructions and comparing the results.
2. Advanced techniques
- Guide the answer with examples
- Break a task down into steps
- Ask the AI to check or critique its own answer
- Iterate and refine until the expected result
Practical work: building a comparative analysis of three supplier offers step by step.
3. Business uses
- Draft and rephrase: emails, minutes, notes
- Summarise a long document and extract key points
- Analyse data and produce tables
- Prepare a presentation or a pitch
Practical work: handling a full file, from summarising the source document to the follow-up email.
4. Making answers reliable
- Spot errors and invented information
- Ask for sources and check them
- Assess an answer with a grid
- Know when not to use AI
Practical work: cross-checking answers containing deliberately introduced errors.
5. Building on results and using AI responsibly
- Build and share a library of instructions
- Protect personal and confidential data
- Follow the organisation’s acceptable use policy
- Measure time saved and quality achieved
Practical work: building a library of ten instructions tailored to each participant’s job.
Participants
Any employee who uses an AI assistant at work: managers, project managers, analysts, and sales, legal, finance, HR and support teams.
Prerequisites
No technical prerequisites. A laptop with Internet access is needed for the practical work.
Trainer credentials
The experts who deliver our courses are specialists in the subjects covered. They are validated by our teaching teams on both subject knowledge and teaching ability, for each course they deliver. They have at least ten years of experience in banking or consulting and hold, or have held, positions of responsibility.
Assessment
The trainer assesses progress throughout the course through quizzes, case studies and simulations. Participants also complete a positioning test before and after the course to validate the skills acquired.
Teaching resources and materials
- Audiovisual aids, documentation and course materials, applied practical exercises with worked solutions, case studies and presentation of real cases.
- At the end of each course, Helios Advisory provides participants with a course evaluation questionnaire, which is then analysed by our teaching teams.
- An attendance sheet per half-day is provided at the end of the course, together with a certificate of completion where the participant has attended the full session.
Enrolment and lead times
Enrolment is finalised with our teaching team. Send us your request through the contact form: we will confirm the next available sessions and the enrolment arrangements suited to your situation.
Accessibility
For any question or requirement relating to accessibility, contact us through the contact form. Each situation is reviewed with you so that the course arrangements can be adapted.
Transforming an operational process with AI: deploying a Lean Six Sigma project
Run a DMAIC project end to end, using artificial intelligence to measure, analyse and improve a process
Description
Many organisations hold large amounts of data on their processes without managing to use it to improve them. Artificial intelligence can now speed up every stage of an improvement project: mapping the real process, measuring its performance, identifying the causes of malfunctions and testing solutions.
This three-day course follows the DMAIC approach (Define, Measure, Analyse, Improve, Control) on a common case study. At each stage, participants combine proven Lean Six Sigma tools with the contributions of AI to run a concrete, measurable transformation project.
Learning objectives
By the end of the course, participants will be able to:
- Frame an improvement project and assess its financial stakes
- Map a real process from data, including through process mining
- Measure the performance and capability of a process
- Identify root causes using statistical tools and AI
- Design, test and deploy improvement solutions
- Sustain the gains through process monitoring and control
Teaching methods and resources
Methodological input, demonstrations of AI and process mining tools, a common case study run in small groups from definition to control, and group debriefs. Materials provided: project charter, DMAIC templates, statistical toolkit and an AI use-case selection grid.
Course programme
1. Define: framing the project
- Capture the voice of the customer and critical requirements
- Write the project charter and scope
- Estimate the expected gains
- Use AI to analyse complaints and customer feedback
Practical work: drafting the project charter for the case study.
2. Measure: objectifying performance
- Map the process with SIPOC and VSM
- Discover the real process through process mining
- Collect and validate data
- Calculate performance and capability indicators
Practical work: mapping the real process from an event log.
3. Analyse: finding root causes
- Identify waste and bottlenecks
- Use the Ishikawa diagram and the 5 Whys
- Test hypotheses with statistics
- Detect explanatory factors with machine learning
Practical work: analysing the causes of longer processing times.
4. Improve: designing solutions
- Generate and prioritise improvement options
- Simulate the impact of solutions
- Automate repetitive tasks
- Prepare the pilot and change management
Practical work: designing and simulating three solutions for the case study.
5. Control: sustaining the gains
- Implement statistical process control
- Build a monitoring dashboard
- Detect drifts with AI
- Standardise and share good practices
Practical work: building the control plan and dashboard for the improved process.
Participants
Project managers, process owners, quality and continuous improvement managers, operational managers and members of transformation teams.
Prerequisites
Prior project management experience is helpful. No prior knowledge of statistics or artificial intelligence is required. A laptop is needed for the practical work.
Trainer credentials
The experts who deliver our courses are specialists in the subjects covered. They are validated by our teaching teams on both subject knowledge and teaching ability, for each course they deliver. They have at least ten years of experience in banking or consulting and hold, or have held, positions of responsibility.
Assessment
The trainer assesses progress throughout the course through quizzes, case studies and simulations. Participants also complete a positioning test before and after the course to validate the skills acquired.
Teaching resources and materials
- Audiovisual aids, documentation and course materials, applied practical exercises with worked solutions, case studies and presentation of real cases.
- At the end of each course, Helios Advisory provides participants with a course evaluation questionnaire, which is then analysed by our teaching teams.
- An attendance sheet per half-day is provided at the end of the course, together with a certificate of completion where the participant has attended the full session.
Enrolment and lead times
Enrolment is finalised with our teaching team. Send us your request through the contact form: we will confirm the next available sessions and the enrolment arrangements suited to your situation.
Accessibility
For any question or requirement relating to accessibility, contact us through the contact form. Each situation is reviewed with you so that the course arrangements can be adapted.
Artificial intelligence and managerial excellence
Use AI to make better decisions, organise work more effectively and support your teams
Description
Artificial intelligence is profoundly changing managers’ daily work: preparing decisions, organising work, monitoring activities and communicating with teams. Used well, it frees up time for what matters; poorly framed, it creates new risks and new concerns.
This two-day course helps managers integrate AI into their practices in a concrete and responsible way. It combines practical workshops on everyday uses with a reflection on the manager’s role in an organisation transformed by AI.
Learning objectives
By the end of the course, participants will be able to:
- Identify the uses of AI that improve managerial effectiveness
- Prepare decisions using summaries and analyses produced by AI
- Organise team work by integrating AI tools
- Manage performance with richer indicators
- Support employees as their jobs evolve
- Set clear and responsible usage rules within the team
Teaching methods and resources
Practical workshops on real management situations, tool demonstrations, case studies and experience sharing. Materials provided: guide to managerial uses of AI, prompt library for managers and a template of team usage rules.
Course programme
1. The manager and AI
- Understand what AI changes in team work
- Distinguish tasks to automate, to assist and to preserve
- Identify opportunities and risks for your team
- Lead by example
Practical work: diagnosing team activities and identifying priority uses.
2. Making better decisions with AI
- Quickly summarise complex information
- Explore scenarios and alternatives
- Keep a critical eye on AI proposals
- Take responsibility for the decision
Practical work: preparing a decision from a complete file.
3. Organising work and saving time
- Prepare meetings and write minutes
- Plan and prioritise activities
- Automate follow-up and reporting
- Measure the time actually saved
Practical work: building a manager’s AI-assisted weekly routine.
4. Managing performance
- Build relevant indicators
- Analyse team activity data
- Prepare individual reviews objectively
- Avoid surveillance excesses
Practical work: analysing a dashboard and preparing a follow-up review.
5. Supporting teams
- Explain the change and address concerns
- Develop employees’ skills
- Define shared usage rules
- Encourage experimentation and sharing
Practical work: drafting the AI usage rules for your team.
Participants
Team leaders, middle managers, department heads and team managers in any sector.
Prerequisites
No technical knowledge is required. Prior use of an AI assistant is helpful but not mandatory.
Trainer credentials
The experts who deliver our courses are specialists in the subjects covered. They are validated by our teaching teams on both subject knowledge and teaching ability, for each course they deliver. They have at least ten years of experience in banking or consulting and hold, or have held, positions of responsibility.
Assessment
The trainer assesses progress throughout the course through quizzes, case studies and simulations. Participants also complete a positioning test before and after the course to validate the skills acquired.
Teaching resources and materials
- Audiovisual aids, documentation and course materials, applied practical exercises with worked solutions, case studies and presentation of real cases.
- At the end of each course, Helios Advisory provides participants with a course evaluation questionnaire, which is then analysed by our teaching teams.
- An attendance sheet per half-day is provided at the end of the course, together with a certificate of completion where the participant has attended the full session.
Enrolment and lead times
Enrolment is finalised with our teaching team. Send us your request through the contact form: we will confirm the next available sessions and the enrolment arrangements suited to your situation.
Accessibility
For any question or requirement relating to accessibility, contact us through the contact form. Each situation is reviewed with you so that the course arrangements can be adapted.
Putting artificial intelligence at the service of leadership
Set a vision, lead the transformation and bring the organisation into the age of AI
Description
Artificial intelligence is not just a technology topic: it is a matter of strategy, organisation and culture. Leaders must set a direction, decide on investments, manage risks and give meaning to a transformation that affects every function.
This two-day course is designed for executives and members of management committees. It gives them the keys to carry an AI vision, choose the right projects, lead change and embody leadership suited to an AI-augmented organisation.
Learning objectives
By the end of the course, participants will be able to:
- Understand the strategic stakes of AI for your sector
- Define an AI vision and ambition for your organisation
- Select and prioritise high-value projects
- Establish governance and a culture of responsible AI
- Lead change and bring teams on board
- Develop your own use of AI as a leader
Teaching methods and resources
Strategic input, company case studies, small-group reflection workshops, executive role-plays and peer exchanges. Materials provided: AI roadmap template, project prioritisation matrix and organisational maturity assessment grid.
Course programme
1. The strategic stakes of AI
- Assess the impact of AI on jobs and business models
- Analyse competitor and market moves
- Identify sources of value creation
- Assess your organisation’s maturity
Practical work: maturity diagnosis of the participants’ organisations.
2. Defining a vision and a roadmap
- Formulate a clear, shared ambition
- Choose between building, buying or partnering
- Prioritise projects by value and feasibility
- Allocate resources and skills
Practical work: building an 18-month AI roadmap.
3. Governing AI responsibly
- Define roles and decision-making bodies
- Integrate regulatory and ethical requirements
- Manage operational and reputational risks
- Report to the board
Practical work: designing the AI governance framework.
4. Leading the transformation
- Give meaning and communicate the vision
- Support changes in jobs and skills
- Overcome resistance and build buy-in
- Measure progress and adjust course
Practical work: drawing up the communication plan for a transformation project.
5. Augmented leadership
- Use AI to prepare decisions and speeches
- Preserve judgement and the human dimension
- Lead by example in the use of AI
- Develop a culture of continuous learning
Practical work: role-play: presenting the AI vision to an executive committee.
Participants
Executives, members of management and executive committees, business unit heads, transformation and strategy leaders.
Prerequisites
No technical knowledge is required. Management or leadership experience is recommended.
Trainer credentials
The experts who deliver our courses are specialists in the subjects covered. They are validated by our teaching teams on both subject knowledge and teaching ability, for each course they deliver. They have at least ten years of experience in banking or consulting and hold, or have held, positions of responsibility.
Assessment
The trainer assesses progress throughout the course through quizzes, case studies and simulations. Participants also complete a positioning test before and after the course to validate the skills acquired.
Teaching resources and materials
- Audiovisual aids, documentation and course materials, applied practical exercises with worked solutions, case studies and presentation of real cases.
- At the end of each course, Helios Advisory provides participants with a course evaluation questionnaire, which is then analysed by our teaching teams.
- An attendance sheet per half-day is provided at the end of the course, together with a certificate of completion where the participant has attended the full session.
Enrolment and lead times
Enrolment is finalised with our teaching team. Send us your request through the contact form: we will confirm the next available sessions and the enrolment arrangements suited to your situation.
Accessibility
For any question or requirement relating to accessibility, contact us through the contact form. Each situation is reviewed with you so that the course arrangements can be adapted.
Responsible artificial intelligence and regulation
Master the legal framework for AI and implement an operational compliance approach
Description
The European regulation on artificial intelligence imposes new obligations on organisations that develop, purchase or use AI systems. It adds to the GDPR, sector-specific rules and growing expectations from customers and authorities regarding ethics and transparency.
This two-day course presents the applicable framework in a clear, practical way. Participants learn to classify AI systems by risk level, identify their obligations and build a compliance approach suited to their organisation.
Learning objectives
By the end of the course, participants will be able to:
- Explain the principles of responsible artificial intelligence
- Describe the European AI regulation, its risk-based approach and its timeline
- Classify an AI system by risk level
- Identify the obligations of providers and deployers
- Align AI requirements with the GDPR and sector rules
- Build an AI compliance and governance approach
Teaching methods and resources
Legal and regulatory input illustrated by concrete cases, AI system classification workshops, small-group risk analyses and experience sharing. Materials provided: summary of the European regulation, risk classification grid, template AI system register and responsible AI policy template.
Course programme
1. Principles of responsible AI
- Understand transparency, fairness and explainability
- Identify biases and their consequences
- Describe the OECD and UNESCO principles
- Assess trust and reputational stakes
Practical work: analysis of AI-related incidents.
2. The European AI regulation
- Understand the scope and the actors concerned
- Identify prohibited practices
- Distinguish high-risk, limited-risk and minimal-risk systems
- Describe the rules for general-purpose AI models and the application timeline
Practical work: classifying ten AI systems by risk level.
3. Obligations and controls
- Identify the obligations of providers and deployers
- Manage risks, data and technical documentation
- Ensure transparency and human oversight
- Prepare for inspections and penalties
Practical work: identifying the obligations applicable to a high-risk AI system.
4. Interaction with other regulations
- Reconcile AI and the GDPR: legal bases, impact assessments, individuals’ rights
- Integrate sector rules: banking, insurance, healthcare
- Address intellectual property and cybersecurity
- Manage contracts with AI providers
Practical work: carrying out a combined impact assessment for a use case.
5. Building compliance
- Maintain the register of AI systems
- Define the responsible AI policy and roles
- Describe the ISO/IEC 42001 standard
- Set up monitoring and audit
Practical work: drawing up the organisation’s compliance roadmap.
Participants
Lawyers, compliance officers, data protection officers, risk managers, internal auditors, project managers and IT managers.
Prerequisites
No technical knowledge is required. Basic knowledge of the GDPR is helpful.
Trainer credentials
The experts who deliver our courses are specialists in the subjects covered. They are validated by our teaching teams on both subject knowledge and teaching ability, for each course they deliver. They have at least ten years of experience in banking or consulting and hold, or have held, positions of responsibility.
Assessment
The trainer assesses progress throughout the course through quizzes, case studies and simulations. Participants also complete a positioning test before and after the course to validate the skills acquired.
Teaching resources and materials
- Audiovisual aids, documentation and course materials, applied practical exercises with worked solutions, case studies and presentation of real cases.
- At the end of each course, Helios Advisory provides participants with a course evaluation questionnaire, which is then analysed by our teaching teams.
- An attendance sheet per half-day is provided at the end of the course, together with a certificate of completion where the participant has attended the full session.
Enrolment and lead times
Enrolment is finalised with our teaching team. Send us your request through the contact form: we will confirm the next available sessions and the enrolment arrangements suited to your situation.
Accessibility
For any question or requirement relating to accessibility, contact us through the contact form. Each situation is reviewed with you so that the course arrangements can be adapted.
AI and credit: from file analysis to decision
Integrate artificial intelligence into the lending process, from file review to decision
Description
The lending process involves a large volume of documents and data: supporting documents, bank statements, financial statements and external information. Artificial intelligence speeds up file review, makes analysis more reliable and improves the quality of decisions.
This two-day course presents the uses of AI at each stage of the credit process, for both individuals and businesses. It also covers the regulatory requirements that frame these uses, in particular the explainability of decisions and human oversight.
Learning objectives
By the end of the course, participants will be able to:
- Identify the uses of AI at each stage of the credit process
- Automate the collection and analysis of supporting documents
- Use transaction data to enrich the analysis
- Use scoring models and understand their limits
- Explain an AI-supported credit decision
- Comply with the applicable regulatory requirements
Teaching methods and resources
Business input illustrated by real cases, demonstrations of document analysis and scoring tools, small-group file reviews and experience sharing. Materials provided: map of AI uses in lending, file analysis grid and a summary of regulatory requirements.
Course programme
1. The credit process and AI
- Map the stages of the lending process
- Identify friction points and delays
- Spot high-value AI uses
- Understand customer and regulator expectations
Practical work: mapping a bank’s credit process.
2. File review
- Automatically extract and check supporting documents
- Detect falsified documents
- Analyse bank statements with AI
- Summarise a file for the analyst
Practical work: AI-assisted review of a consumer credit file.
3. Risk analysis
- Understand rating and scoring models
- Enrich analysis with transaction and external data
- Analyse a company’s financial statements with AI
- Identify weak signals
Practical work: risk analysis of a corporate credit file.
4. The decision and its explanation
- Combine the score with the analyst’s expertise
- Explain a decision to the customer and the committee
- Handle borderline cases and exceptions
- Ensure the traceability of the decision
Practical work: preparing a presentation to the credit committee.
5. Regulatory framework and governance
- Understand credit as a high-risk AI use
- Apply the European Banking Authority guidelines on loan origination
- Prevent bias and discrimination
- Validate and monitor models
Practical work: compliance review of a scoring model.
Participants
Relationship managers, credit analysts, heads of lending, credit committee members, risk managers and banking transformation project managers.
Prerequisites
Knowledge of the credit process is recommended. No technical knowledge of artificial intelligence is required.
Trainer credentials
The experts who deliver our courses are specialists in the subjects covered. They are validated by our teaching teams on both subject knowledge and teaching ability, for each course they deliver. They have at least ten years of experience in banking or consulting and hold, or have held, positions of responsibility.
Assessment
The trainer assesses progress throughout the course through quizzes, case studies and simulations. Participants also complete a positioning test before and after the course to validate the skills acquired.
Teaching resources and materials
- Audiovisual aids, documentation and course materials, applied practical exercises with worked solutions, case studies and presentation of real cases.
- At the end of each course, Helios Advisory provides participants with a course evaluation questionnaire, which is then analysed by our teaching teams.
- An attendance sheet per half-day is provided at the end of the course, together with a certificate of completion where the participant has attended the full session.
Enrolment and lead times
Enrolment is finalised with our teaching team. Send us your request through the contact form: we will confirm the next available sessions and the enrolment arrangements suited to your situation.
Accessibility
For any question or requirement relating to accessibility, contact us through the contact form. Each situation is reviewed with you so that the course arrangements can be adapted.
Assessing creditworthiness with artificial intelligence
Measure borrowers’ repayment capacity by combining financial analysis and AI models
Description
Assessing creditworthiness is at the heart of lending. It traditionally relies on analysing income, expenses, financial statements and repayment history. Artificial intelligence makes it possible to use more data and detect weaknesses earlier.
This two-day course presents methods for assessing the creditworthiness of individuals and businesses, and how AI complements them. Participants learn to read a model’s output, check its relevance and retain expert judgement.
Learning objectives
By the end of the course, participants will be able to:
- Apply traditional creditworthiness assessment methods
- Understand how AI scoring models work
- Use transaction and alternative data
- Detect early warning signs of default
- Interpret and explain a model’s output
- Meet validation and non-discrimination requirements
Teaching methods and resources
Methodological input, analysis of real individual and corporate cases, hands-on work with scoring models and small-group workshops. Materials provided: creditworthiness analysis grid, model validation checklist and guides to financial indicators.
Course programme
1. Creditworthiness fundamentals
- Analyse income, expenses and disposable income
- Read a company’s financial statements
- Calculate key debt and profitability ratios
- Understand probability of default and loss given default
Practical work: creditworthiness analysis of three borrowers.
2. AI scoring models
- Understand logistic regression and decision trees
- Describe more advanced machine learning models
- Compare model performance
- Identify their limits and biases
Practical work: comparing two models’ results on the same portfolio.
3. New data sources
- Use bank account data and open banking
- Use external and sector data
- Analyse unstructured documents
- Comply with the GDPR when using data
Practical work: enriching a file’s analysis with transaction data.
4. Early detection of difficulties
- Identify weak signals of deterioration
- Monitor a portfolio with automatic alerts
- Prioritise amicable collection actions
- Support vulnerable customers
Practical work: building an alert system for a loan portfolio.
5. Explain, validate and govern
- Explain a model’s output to the customer and to control functions
- Validate a model and monitor its performance
- Prevent discrimination
- Understand the requirements of the European AI regulation
Practical work: drafting the explanation note for an adverse decision.
Participants
Credit analysts, relationship managers, risk managers, modelling and validation teams, collection managers and auditors.
Prerequisites
Basic financial analysis knowledge is recommended. No programming knowledge is required.
Trainer credentials
The experts who deliver our courses are specialists in the subjects covered. They are validated by our teaching teams on both subject knowledge and teaching ability, for each course they deliver. They have at least ten years of experience in banking or consulting and hold, or have held, positions of responsibility.
Assessment
The trainer assesses progress throughout the course through quizzes, case studies and simulations. Participants also complete a positioning test before and after the course to validate the skills acquired.
Teaching resources and materials
- Audiovisual aids, documentation and course materials, applied practical exercises with worked solutions, case studies and presentation of real cases.
- At the end of each course, Helios Advisory provides participants with a course evaluation questionnaire, which is then analysed by our teaching teams.
- An attendance sheet per half-day is provided at the end of the course, together with a certificate of completion where the participant has attended the full session.
Enrolment and lead times
Enrolment is finalised with our teaching team. Send us your request through the contact form: we will confirm the next available sessions and the enrolment arrangements suited to your situation.
Accessibility
For any question or requirement relating to accessibility, contact us through the contact form. Each situation is reviewed with you so that the course arrangements can be adapted.
Artificial intelligence for compliance (KYC and AML-CFT)
Strengthen customer due diligence and the fight against money laundering with artificial intelligence
Description
Know Your Customer (KYC) and anti-money laundering and counter-terrorist financing (AML-CFT) frameworks generate a considerable volume of checks, alerts and documents. Many alerts turn out to be false positives, tying up teams at the expense of real risks.
This two-day course shows how AI improves the effectiveness of these frameworks: identity verification, screening, transaction monitoring, alert analysis and report drafting. It recalls the regulatory requirements and the conditions for controlled use.
Learning objectives
By the end of the course, participants will be able to:
- Describe the AML-CFT regulatory framework and its European developments
- Identify the uses of AI in the KYC framework
- Automate onboarding and identity verification
- Improve transaction monitoring and reduce false positives
- Speed up alert analysis and the preparation of reports
- Govern the use of AI within a compliance framework
Teaching methods and resources
Regulatory and business input, tool demonstrations, real money laundering case studies and small-group alert analysis workshops. Materials provided: map of AI uses in compliance, alert analysis grid and tool governance checklist.
Course programme
1. The AML-CFT framework
- Recall due diligence and reporting obligations
- Describe the new EU AML package and the AMLA authority
- Understand the risk-based approach
- Identify supervisors’ expectations
Practical work: mapping the money laundering risks of a business line.
2. Augmented customer knowledge
- Automate remote onboarding
- Verify identity and detect fake documents
- Identify beneficial owners
- Keep customer files up to date continuously
Practical work: AI-assisted review of a corporate KYC file.
3. Screening and transaction monitoring
- Screen sanctions and politically exposed persons
- Detect unusual behaviour with machine learning
- Analyse relationship networks
- Reduce false positives
Practical work: analysing a network of suspicious transactions.
4. Alert analysis and reporting
- Prioritise alerts by risk
- Summarise an investigation file
- Draft a suspicious activity report with AI support
- Keep control of the decision
Practical work: handling an alert end to end up to a draft report.
5. Governing AI in compliance
- Validate models and document parameters
- Ensure explainability for supervisors
- Control data quality
- Understand the requirements of the European AI regulation
Practical work: preparing the validation file for a monitoring tool.
Participants
Compliance managers and analysts, AML-CFT and KYC teams, relationship managers, internal auditors, risk managers and compliance project managers.
Prerequisites
Basic knowledge of AML-CFT obligations is recommended. No technical knowledge is required.
Trainer credentials
The experts who deliver our courses are specialists in the subjects covered. They are validated by our teaching teams on both subject knowledge and teaching ability, for each course they deliver. They have at least ten years of experience in banking or consulting and hold, or have held, positions of responsibility.
Assessment
The trainer assesses progress throughout the course through quizzes, case studies and simulations. Participants also complete a positioning test before and after the course to validate the skills acquired.
Teaching resources and materials
- Audiovisual aids, documentation and course materials, applied practical exercises with worked solutions, case studies and presentation of real cases.
- At the end of each course, Helios Advisory provides participants with a course evaluation questionnaire, which is then analysed by our teaching teams.
- An attendance sheet per half-day is provided at the end of the course, together with a certificate of completion where the participant has attended the full session.
Enrolment and lead times
Enrolment is finalised with our teaching team. Send us your request through the contact form: we will confirm the next available sessions and the enrolment arrangements suited to your situation.
Accessibility
For any question or requirement relating to accessibility, contact us through the contact form. Each situation is reviewed with you so that the course arrangements can be adapted.
Transforming the claims journey with AI
Speed up claims handling and improve the policyholder experience with artificial intelligence
Description
Claims handling is the moment of truth in the relationship between insurer and policyholder. Turnaround times, clear communication and fair compensation drive customer satisfaction and loyalty. It is also a major cost item for insurers.
This two-day course presents the uses of AI at each stage of the journey: notification, qualification, assessment, fraud detection, settlement and customer relations. Participants learn to redesign the journey and manage a transformation project.
Learning objectives
By the end of the course, participants will be able to:
- Map the claims journey and its friction points
- Automate claim notification and qualification
- Use AI for damage assessment and expert appraisal
- Route files according to their complexity
- Improve communication with policyholders
- Manage a claims journey transformation project
Teaching methods and resources
Business input, tool demonstrations, insurer case studies and small-group journey design workshops. Materials provided: standard claims journey map, catalogue of AI use cases and project prioritisation grid.
Course programme
1. The claims journey today
- Map the stages and stakeholders
- Measure turnaround times and costs
- Identify policyholders’ expectations
- Spot friction points
Practical work: mapping the claims journey for one line of business.
2. Notification and qualification
- Simplify notification with conversational assistants
- Extract information from documents and photos
- Automatically qualify the claim
- Check the applicable cover
Practical work: designing an online claim notification journey.
3. Assessment and appraisal
- Assess damage from images
- Route to remote or on-site appraisal
- Estimate the cost of the claim
- Speed up the settlement of small claims
Practical work: analysing a motor claim file.
4. Routing and settlement
- Segment files by complexity
- Automate the settlement of simple cases
- Detect fraud signals
- Coordinate service providers
Practical work: defining file routing rules.
5. Policyholder experience and management
- Keep policyholders informed at each stage
- Measure satisfaction and turnaround times
- Support claims handlers as their jobs evolve
- Meet regulatory and ethical requirements
Practical work: drawing up the journey transformation roadmap.
Participants
Claims managers and handlers, customer experience managers, transformation project managers, fraud and insurance operations managers.
Prerequisites
Knowledge of how insurance works is recommended. No technical knowledge is required.
Trainer credentials
The experts who deliver our courses are specialists in the subjects covered. They are validated by our teaching teams on both subject knowledge and teaching ability, for each course they deliver. They have at least ten years of experience in banking or consulting and hold, or have held, positions of responsibility.
Assessment
The trainer assesses progress throughout the course through quizzes, case studies and simulations. Participants also complete a positioning test before and after the course to validate the skills acquired.
Teaching resources and materials
- Audiovisual aids, documentation and course materials, applied practical exercises with worked solutions, case studies and presentation of real cases.
- At the end of each course, Helios Advisory provides participants with a course evaluation questionnaire, which is then analysed by our teaching teams.
- An attendance sheet per half-day is provided at the end of the course, together with a certificate of completion where the participant has attended the full session.
Enrolment and lead times
Enrolment is finalised with our teaching team. Send us your request through the contact form: we will confirm the next available sessions and the enrolment arrangements suited to your situation.
Accessibility
For any question or requirement relating to accessibility, contact us through the contact form. Each situation is reviewed with you so that the course arrangements can be adapted.
Artificial intelligence and insurance fraud detection
Detect insurance fraud earlier and more effectively with artificial intelligence
Description
Insurance fraud is a significant cost for insurers and, ultimately, for all policyholders. It takes increasingly varied forms: exaggerated claims, fake documents, fictitious claims and organised networks.
This two-day course presents AI-based fraud detection methods: rules, scores, network analysis, and document and image analysis. It also covers how to organise investigations and the rules to follow, particularly regarding data protection.
Learning objectives
By the end of the course, participants will be able to:
- Identify the main types of insurance fraud
- Understand AI-based detection methods
- Detect fake documents and altered images
- Uncover organised fraud networks
- Organise alert handling and investigations
- Comply with the legal framework for fighting fraud
Teaching methods and resources
Business and technical input, real fraud case studies, tool demonstrations and small-group investigation workshops. Materials provided: fraud typology, suspicious file analysis grid and a summary of data protection rules.
Course programme
1. Types of fraud
- Distinguish opportunistic and organised fraud
- Identify fraud in motor, home, health and protection insurance
- Measure the cost of fraud
- Describe the role of industry bodies and shared databases
Practical work: classifying twenty suspicious files by type.
2. Detection methods
- Combine business rules and fraud scores
- Understand supervised and unsupervised machine learning
- Detect anomalies
- Measure the performance of a detection system
Practical work: analysing the results of a detection model.
3. Document and image analysis
- Detect falsified documents
- Spot altered or reused photos
- Analyse claim statements
- Cross-check external information
Practical work: checking a file containing falsified documents.
4. Network analysis
- Link policyholders, third parties and service providers
- Uncover organised fraud networks
- Visualise suspicious relationships
- Prioritise investigations
Practical work: uncovering a fraud network from claims data.
5. Investigation and legal framework
- Organise alert handling
- Conduct an investigation while respecting rights
- Comply with the GDPR and data protection authority rules
- Understand the requirements of the European AI regulation
Practical work: conducting an investigation through to a decision.
Participants
Claims handlers, fraud investigators and managers, compliance and risk managers, insurance data analysts.
Prerequisites
Knowledge of how insurance works is recommended. No technical knowledge is required.
Trainer credentials
The experts who deliver our courses are specialists in the subjects covered. They are validated by our teaching teams on both subject knowledge and teaching ability, for each course they deliver. They have at least ten years of experience in banking or consulting and hold, or have held, positions of responsibility.
Assessment
The trainer assesses progress throughout the course through quizzes, case studies and simulations. Participants also complete a positioning test before and after the course to validate the skills acquired.
Teaching resources and materials
- Audiovisual aids, documentation and course materials, applied practical exercises with worked solutions, case studies and presentation of real cases.
- At the end of each course, Helios Advisory provides participants with a course evaluation questionnaire, which is then analysed by our teaching teams.
- An attendance sheet per half-day is provided at the end of the course, together with a certificate of completion where the participant has attended the full session.
Enrolment and lead times
Enrolment is finalised with our teaching team. Send us your request through the contact form: we will confirm the next available sessions and the enrolment arrangements suited to your situation.
Accessibility
For any question or requirement relating to accessibility, contact us through the contact form. Each situation is reviewed with you so that the course arrangements can be adapted.
Artificial intelligence and banking fraud detection
Protect customers and the institution against fraud with artificial intelligence
Description
Banking fraud is evolving rapidly: payment fraud, identity theft, fake bank adviser scams, CEO fraud and fraudulent account openings. Fraudsters themselves use AI, making attacks more convincing and more frequent.
This two-day course presents AI-based detection systems, from real-time transaction analysis to the detection of fraudulent accounts. It covers the trade-off between security and customer experience, as well as institutions’ regulatory obligations.
Learning objectives
By the end of the course, participants will be able to:
- Identify the main types of banking fraud
- Understand how a real-time detection system works
- Use AI to detect fraudulent transactions and accounts
- Combat social engineering and AI-generated fraud
- Balance security and a smooth customer experience
- Meet regulatory and reimbursement obligations
Teaching methods and resources
Business and technical input, real case studies, demonstrations of detection tools and small-group alert analysis workshops. Materials provided: banking fraud typology, alert analysis grid and a summary of regulatory obligations.
Course programme
1. Types of banking fraud
- Identify card, transfer and cheque fraud
- Understand social engineering and fake adviser scams
- Describe CEO and fake supplier fraud
- Assess the impact of AI-generated fraud
Practical work: analysing real fraud cases by type.
2. Real-time detection
- Understand transaction fraud scores
- Analyse the customer’s usual behaviour
- Block or authenticate at the right time
- Reduce false alerts
Practical work: configuring the rules of a detection system.
3. Onboarding and fraudulent accounts
- Detect identity theft and fake documents
- Spot money mule accounts
- Analyse networks of linked accounts
- Coordinate with AML-CFT
Practical work: analysing a set of suspicious accounts.
4. Customer experience and relationship
- Balance security and smoothness
- Inform and raise customer awareness
- Support victims
- Measure satisfaction
Practical work: designing a prevention journey against fake adviser fraud.
5. Regulatory framework and governance
- Describe PSD2, strong customer authentication and reimbursement rules
- Anticipate European developments on payments
- Validate and monitor models
- Comply with the GDPR and the European AI regulation
Practical work: building the fraud management dashboard.
Participants
Fraud managers and analysts, payment managers, customer advisers, compliance, risk and security managers.
Prerequisites
Knowledge of how payment methods work is recommended. No technical knowledge is required.
Trainer credentials
The experts who deliver our courses are specialists in the subjects covered. They are validated by our teaching teams on both subject knowledge and teaching ability, for each course they deliver. They have at least ten years of experience in banking or consulting and hold, or have held, positions of responsibility.
Assessment
The trainer assesses progress throughout the course through quizzes, case studies and simulations. Participants also complete a positioning test before and after the course to validate the skills acquired.
Teaching resources and materials
- Audiovisual aids, documentation and course materials, applied practical exercises with worked solutions, case studies and presentation of real cases.
- At the end of each course, Helios Advisory provides participants with a course evaluation questionnaire, which is then analysed by our teaching teams.
- An attendance sheet per half-day is provided at the end of the course, together with a certificate of completion where the participant has attended the full session.
Enrolment and lead times
Enrolment is finalised with our teaching team. Send us your request through the contact form: we will confirm the next available sessions and the enrolment arrangements suited to your situation.
Accessibility
For any question or requirement relating to accessibility, contact us through the contact form. Each situation is reviewed with you so that the course arrangements can be adapted.
AI-augmented Lean Six Sigma
Combine Lean Six Sigma methods and artificial intelligence for faster, more precise continuous improvement
Description
Lean Six Sigma has proven its worth in reducing waste and process variability. Artificial intelligence brings new capabilities: using large volumes of data, automatic root cause analysis, predicting drifts and automating controls.
This three-day course presents the key Lean Six Sigma tools and how AI strengthens them. It is designed for continuous improvement practitioners who want to modernise their methods and for teams discovering the approach.
Learning objectives
By the end of the course, participants will be able to:
- Master the key principles and tools of Lean and Six Sigma
- Identify the contributions of AI at each stage of the approach
- Use process data through process mining
- Use machine learning for root cause analysis
- Predict and prevent process drifts
- Integrate AI into a continuous improvement programme
Teaching methods and resources
Methodological input, tool demonstrations, hands-on exercises on datasets, industrial and service case studies and small-group workshops. Materials provided: Lean Six Sigma toolkit, catalogue of AI uses and dashboard templates.
Course programme
1. Lean Six Sigma fundamentals
- Understand value, waste and flow
- Measure variability and sigma level
- Describe the DMAIC approach
- Identify roles: Green Belt, Black Belt, sponsor
Practical work: identifying waste in an administrative process.
2. Data for improvement
- Collect and prepare process data
- Discover the real process through process mining
- Visualise flows and lead times
- Measure data quality
Practical work: analysing an event log with a process mining tool.
3. Augmented root cause analysis
- Use classic statistical tools
- Identify explanatory factors with machine learning
- Analyse comments and complaints with AI
- Validate root causes
Practical work: finding the causes of a high defect rate.
4. Improvement and automation
- Design solutions and target processes
- Automate repetitive tasks
- Simulate gains before deployment
- Support change
Practical work: designing the target process and estimating the gains.
5. Predictive control
- Implement statistical process control
- Predict drifts with AI
- Build real-time dashboards
- Sustain a continuous improvement programme
Practical work: building a predictive control dashboard.
Participants
Continuous improvement practitioners (Green Belt, Black Belt), quality and operations managers, project managers, managers and process analysts.
Prerequisites
No Lean Six Sigma certification is required. Basic knowledge of statistics is helpful. A laptop is needed for the practical work.
Trainer credentials
The experts who deliver our courses are specialists in the subjects covered. They are validated by our teaching teams on both subject knowledge and teaching ability, for each course they deliver. They have at least ten years of experience in banking or consulting and hold, or have held, positions of responsibility.
Assessment
The trainer assesses progress throughout the course through quizzes, case studies and simulations. Participants also complete a positioning test before and after the course to validate the skills acquired.
Teaching resources and materials
- Audiovisual aids, documentation and course materials, applied practical exercises with worked solutions, case studies and presentation of real cases.
- At the end of each course, Helios Advisory provides participants with a course evaluation questionnaire, which is then analysed by our teaching teams.
- An attendance sheet per half-day is provided at the end of the course, together with a certificate of completion where the participant has attended the full session.
Enrolment and lead times
Enrolment is finalised with our teaching team. Send us your request through the contact form: we will confirm the next available sessions and the enrolment arrangements suited to your situation.
Accessibility
For any question or requirement relating to accessibility, contact us through the contact form. Each situation is reviewed with you so that the course arrangements can be adapted.
Exercises use AI assistants available on the market, chosen according to the tools authorised in participants’ organisations. Helios Advisory is not a partner of any AI provider.
Practical information
The following applies to every programme presented on this page.
Funding
Several funding routes exist and depend on your professional situation: skills development plan, sector funding bodies, or direct company funding. Contact us so we can work out the most suitable option together.
Timetable
Courses run from 9.00 to 17.30, with a lunch break of one hour to an hour and a half. Participants are welcomed from 8.45. Breaks and lunches are provided. For four or five day courses, sessions finish at 16.00 on the final day.
Dates and locations
In-company and open sessions are arranged on request, at our premises, at yours, or remotely. Contact us for the next available dates and to agree the format best suited to your teams.
A question or a specific requirement?
Every programme can be adapted to your context, your sector and the level of your teams. We will come back to you with a tailored proposal, a schedule and a price matching your request.
This programme is an original work designed by the teaching teams of Helios Advisory. Any reproduction, representation, adaptation or exploitation, in whole or in part, without prior written authorisation, is strictly prohibited.