The document outlines the contributions of 17 institutions in using AI for retail banking, insurance, and wealth management, with a focus on predictive and generative AI applications. It discusses the governance and resource allocation for AI initiatives, the differing levels of AI application across business units, and anticipated regulations from the AI Act, which aims to enforce compliance by mid-2026. Additionally, it highlights the potential limitations and ethical considerations associated with AI usage.

2. 17 institutions contributed to the
questionnaire
10 in-depth interviews
5 case studies
Together, they hold a significant share of the market*
*70% of Retail Banking, 70% of Insurance and 60% of Wealth Management & Private Banking 2

3. 3
Predictive AI Generative AI
Key objectives
Identify patterns, anomalies, correlations to
predict outcomes
Create new content based on learned
patterns and rules
Use cases
Forecast customer behavior, manage risk,
detect fraud, market trends
Generate personalised customer
interactions, financial reports, marketing
copy, synthetic data
Algorithms used
Regression, time series analysis, anomaly
detection, decision trees
Large language models, GPT models,
variational autoencoders
Training data
Historical transactions, market data,
customer profiles
Financial news, regulatory documents,
customer feedback, product descriptions
Limitations
Requires high-quality data, model
interpretability can be challenging
Risk of generating inaccurate or misleading
information, potential for misuse

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10. 10
Benefits Roadblocks
2.
RESOURCES

11. 11
2.
RESOURCES

12. Centralised
Centralised with
CoE
Coordinated Decentralised
Resources
All AI expertise concentrated
within the central AI team.
AI specialists report to both the
CoE team and business unit
leaders.
Business units have their own AI
teams or contract external
expertise.
Business units fully responsible
for resourcing their AI projects.
Governance
Strict central control over data,
algorithms, model selection, and
deployment.
CoE sets overall AI standards and
guidelines.
Central AI team provides tools,
platforms, and best practices
guidelines.
Minimal central guidelines, focus
on data privacy and basic ethical
standards.
Project Initiation
Business units submit project
proposals, central team prioritizes
and executes.
Collaboration between business
units and CoE to define use cases.
Business units drive their own AI
initiatives, a central "steering
committee" ensures alignment
and coordination.
Business units independently
identify and execute their AI
initiatives.
12
Centralised Decentralised
CXO
Business Units
AI team
Direct reporting
Functional reporting
2.
RESOURCES

13. 13
70% of employees rarely, if ever, use AI
applications in their daily tasks.*
*2024 survey - 1300 respondents

14. 14
Figure 15: AI Act’s Risk Based Approach
High risk
Ex: Credit scoring, algorithmic
trading
2
Limited risk
Ex: Chatbots, robo-advisors
3
Minimal risk
Ex: Budgeting tool, fraud
detection, etc.
4
Unacceptable risk
Ex: Social scoring
1
Risk level Obligation
Prohibited
Conformity assessment
Transparency
obligation
No obligation
3.
REGULATION
Source: European Commission, Sailpeak

15. 15
June 2023
Agreement on AI Act by
European Parliament
March 2024
AI Act adopted by European
Parliament
June 2024
Publication of the AI Act
End of 2024
Bans on certain AI practices
like social scoring will be
enforced
Mid 2025
High-risk AI systems (e.g.
credit scoring) need to
comply
Mid 2026
All remaining regulations of
the AI Act come into effect.
Time to prepare systems, processes, conformity assessment,
documentation, etc.
Figure 16: AI Act Timeline
Source: European Commission, Sailpeak
3.
REGULATION

16. 16
3.
REGULATION

17. 17
4.
ETHICS

18. 18
5.
ECOSYSTEM

19. 19
5.
ECOSYSTEM

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CONTACT
Alessandra Guion
alessandra@fintechbelgium.be
CONTACT
Nico Vincent
nico@sailpeak.com
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