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29 Jan 2025
RCR - Regulatory Compliance & Risk
AML - Anti-Money Laundering
Compliance
Banking
Private Banking & Wealth Management
Artificial Intelligence in Compliance: From Use Cases to Audit‑Proof Implementation
Generative AI is rapidly reshaping financial services, and Compliance is one of the functions where the impact can be most immediate. Banks are exploring how AI can improve efficiency and effectiveness across anti‑money laundering, fraud prevention, and broader compliance controls, while boards and executives focus on strategy, readiness, and regulatory expectations. Synpulse supports financial institutions in turning AI ambition into governed, measurable outcomes, without compromising risk management discipline.
Written by Tina Freund
Why AI in Compliance matters
Adopting AI in Compliance can significantly improve both operational efficiency and control effectiveness, particularly in:
- Anti‑Money Laundering (AML), including Know Your Customer (KYC) and transaction monitoring
- Fraud detection and prevention
- General compliance controls and reporting
At the same time, AI introduces new risks and regulatory considerations. Successful adoption requires more than selecting tools. It requires embedding AI into the institution’s governance, controls, and risk management framework in a way that stands up to internal audit and supervisory scrutiny.
Typical questions banks are asking
- How can AI best improve Compliance controls and processes?
- Which AI use cases deliver the highest benefit in AML, fraud, and compliance operations?
- What are key risks and regulatory expectations when applying AI in Compliance?
- How can those risks be mitigated while meeting supervisory expectations?
- What does robust AI governance look like in practice?
- Should AI capabilities be built in‑house or sourced from third‑party providers, and what does the market offer?
Our approach
1) Baselining Assess front‑to‑back Compliance processes and controls to identify where AI use cases create measurable value and where risk constraints apply.
2) Strategic options Develop and evaluate build‑and‑buy options, including structured market scanning and supplier engagement where helpful.
3) Target operating model and governance Define the target governance model and embed it into the bank’s risk management framework, grounded in a comprehensive:
- model risk assessment
- data risk assessment
- cyber risk assessment
- third‑party risk assessment …and defined mitigation measures.
4) Sustainable implementation Ensure audit‑proof design and delivery, including updates to policies, procedures, controls, roles and responsibilities, training, and the rollout of the chosen AI solution.
5) Continuous improvement Embed the solution into ongoing risk management and oversight, supported through periodic reviews, calibration, and enhancements.
What you can expect from Synpulse
- A clear view of how banks are adopting AI and where value is already being realised
- A structured assessment of benefits, risks, and challenges for AI in Compliance
- A use‑case and business case analysis identifying where AI fits best across first and second line controls
- A pragmatic build vs. buy evaluation, considering strategic value, risks, skills, technology, cost, control, flexibility, and time to market
- Transparency on relevant solution providers and delivery models
- End‑to‑end support to design, build, and implement AI solutions, including integration into the bank’s risk management governance and controls
Our Experts In This Topic
OUR EXPERT
Tina Freund
Associate Partner • Banking
Tina Freund is an Associate Partner at Synpulse Schweiz AG, specialising in Banking and Compliance. She leads initiatives across AML, AI‑driven compliance innovation, and regulatory analysis, including contributions to the 2026 AI in Compliance Study and AMLR assessments. Tina drives impactful content, campaigns, and client‑focused solutions that elevate Synpulse’s compliance capabilities.
OUR EXPERT
Johannes Pfister
Managing Director • Banking
Johannes Pfister is a Managing Director, shaping banking transformation with a strong focus on AI strategy and enablement. As an AI enthusiast, evangelist, and translator, he bridges technical complexity and business value to help financial institutions innovate, enhance efficiency, and prepare for an AI‑driven future.
OUR EXPERT
Morgan Lee
Senior Data & AI Engineer
Morgan Lee is a Senior Data & AI Engineer at Synpulse UK, specialising in enterprise‑grade AI engineering and agentic platform development. He plays a key role in shaping PULSE8.ai, delivering scalable orchestration architectures and advancing AI capabilities across financial‑services clients. Morgan brings deep expertise in data engineering, LLM systems, and production‑ready AI solutions.
Related Contents
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-min read • 29 Jan 2025
RCR - Regulatory Compliance & Risk
AML - Anti-Money Laundering
Compliance
Banking
Private Banking & Wealth Management
Artificial Intelligence in Compliance: From Use Cases to Audit‑Proof Implementation
Generative AI is rapidly reshaping financial services, and Compliance is one of the functions where the impact can be most immediate. Banks are exploring how AI can improve efficiency and effectiveness across anti‑money laundering, fraud prevention, and broader compliance controls, while boards and executives focus on strategy, readiness, and regulatory expectations. Synpulse supports financial institutions in turning AI ambition into governed, measurable outcomes, without compromising risk management discipline.
Written by Tina Freund
Why AI in Compliance matters
Adopting AI in Compliance can significantly improve both operational efficiency and control effectiveness, particularly in:
- Anti‑Money Laundering (AML), including Know Your Customer (KYC) and transaction monitoring
- Fraud detection and prevention
- General compliance controls and reporting
At the same time, AI introduces new risks and regulatory considerations. Successful adoption requires more than selecting tools. It requires embedding AI into the institution’s governance, controls, and risk management framework in a way that stands up to internal audit and supervisory scrutiny.
Typical questions banks are asking
- How can AI best improve Compliance controls and processes?
- Which AI use cases deliver the highest benefit in AML, fraud, and compliance operations?
- What are key risks and regulatory expectations when applying AI in Compliance?
- How can those risks be mitigated while meeting supervisory expectations?
- What does robust AI governance look like in practice?
- Should AI capabilities be built in‑house or sourced from third‑party providers, and what does the market offer?
Our approach
1) Baselining Assess front‑to‑back Compliance processes and controls to identify where AI use cases create measurable value and where risk constraints apply.
2) Strategic options Develop and evaluate build‑and‑buy options, including structured market scanning and supplier engagement where helpful.
3) Target operating model and governance Define the target governance model and embed it into the bank’s risk management framework, grounded in a comprehensive:
- model risk assessment
- data risk assessment
- cyber risk assessment
- third‑party risk assessment …and defined mitigation measures.
4) Sustainable implementation Ensure audit‑proof design and delivery, including updates to policies, procedures, controls, roles and responsibilities, training, and the rollout of the chosen AI solution.
5) Continuous improvement Embed the solution into ongoing risk management and oversight, supported through periodic reviews, calibration, and enhancements.
What you can expect from Synpulse
- A clear view of how banks are adopting AI and where value is already being realised
- A structured assessment of benefits, risks, and challenges for AI in Compliance
- A use‑case and business case analysis identifying where AI fits best across first and second line controls
- A pragmatic build vs. buy evaluation, considering strategic value, risks, skills, technology, cost, control, flexibility, and time to market
- Transparency on relevant solution providers and delivery models
- End‑to‑end support to design, build, and implement AI solutions, including integration into the bank’s risk management governance and controls
Our Experts In This Topic
OUR EXPERTTina Freund
Associate Partner • Banking
Tina Freund is an Associate Partner at Synpulse Schweiz AG, specialising in Banking and Compliance. She leads initiatives across AML, AI‑driven compliance innovation, and regulatory analysis, including contributions to the 2026 AI in Compliance Study and AMLR assessments. Tina drives impactful content, campaigns, and client‑focused solutions that elevate Synpulse’s compliance capabilities.
OUR EXPERTJohannes Pfister
Managing Director • Banking
Johannes Pfister is a Managing Director, shaping banking transformation with a strong focus on AI strategy and enablement. As an AI enthusiast, evangelist, and translator, he bridges technical complexity and business value to help financial institutions innovate, enhance efficiency, and prepare for an AI‑driven future.
OUR EXPERTMorgan Lee
Senior Data & AI Engineer
Morgan Lee is a Senior Data & AI Engineer at Synpulse UK, specialising in enterprise‑grade AI engineering and agentic platform development. He plays a key role in shaping PULSE8.ai, delivering scalable orchestration architectures and advancing AI capabilities across financial‑services clients. Morgan brings deep expertise in data engineering, LLM systems, and production‑ready AI solutions.