NextWave & SS&C Algorithmics: AI & automation – myth vs reality

On 11th June, SS&C hosted its annual Algorithmics Risk Conference, bringing together senior financial services leaders to explore how the industry is evolving in the face of AI, cyber threats, and regulatory change. Tony Clark, Founder & CEO of NextWave and host of The London Fintech Podcast, delivered the keynote speech, “AI and Automation: Myth vs Reality",  tackling one of the industry’s most urgent challenges: how to scale AI and automation responsibly across financial services.

We've rounded up some key takeaways from his speech and the event below, as well as what vital steps you can make to stay ahead of the curve. 

From hype to impact: The AI imperative in financial services

Tony opened with a clear message—while AI adoption is accelerating across financial institutions, real value creation requires moving beyond isolated pilots and myths to scalable, enterprise-wide transformation.

He identified three areas of opportunity:

• Growth: Expanding through new markets, M&A, and AI-driven service innovation.

• Efficiency: Streamlining onboarding, client operations, and compliance with Agentic AI and automation.

• Risk & Control: Strengthening GRC, regulatory response, ESG, AML, and fraud detection with AI-enhanced solutions

Debunking the Myths of AI Adoption

He continued by breaking down common misconceptions:

• Myth 1: “Everyone else is further ahead” – In reality, most firms are still early in their AI maturity.

• Myth 3: “AI is a black box” – With proper governance, explainability and oversight are achievable.

• Myth 4: “AI is plug-and-play” – Success hinges on foundational data readiness, governance, and internal alignment

Strategic AI Adoption: A 6-Pillar Framework

Tony introduced NextWave’s 6 pillars of AI strategy, aligning AI with business value and vision:

1. Organisation & skills: Reskilling teams to work with AI.

2. Governance & controls: Ensuring oversight and compliance.

3. Technology platforms: Selecting scalable, fit-for-purpose tools.

4. Data readiness: Building robust, clean datasets.

5. Execution roadmaps: Coordinated delivery across business units

Generative vs agentic AI

Tony made a clear distinction between Generative AI (creating content) and Agentic AI (autonomous decision-making and action). He argued that Agentic AI will eclipse Generative AI in enterprise impact, especially for FS firms seeking to transform complex processes like product control, collateral management, and compliance

Real-World Use Cases

Two case studies demonstrated the power of automation:

• Collateral agent automation: Replacing weeks of manual processing with a real-time AI workflow.

• Product control agents: Digital workers sourcing and validating front office data to streamline reporting

The state of AI adoption in financial services

AI is rapidly transforming the financial services (FS) industry, driving operational efficiency, delivering more personalised customer experiences, and enhancing risk and compliance capabilities. However, responsible development, ethical governance, and regulatory alignment are critical for success.

Tony highlighted several key trends and challenges we're seeing across our client base:

Trends:

• Accelerating adoption: Most FS firms are piloting AI in isolated use cases and now targeting rapid enterprise-wide expansion.

• Front-to-back integration: While customer-facing AI tools grab headlines, the biggest gains are happening behind the scenes—optimising risk, compliance, finance, and operations.

• Generative AI: LLMs are gaining traction in document review, client comms, and real-time risk analysis.

• Agentic AI: There’s growing interest in autonomous AI agents capable of decision-making and action across core banking and insurance processes.

Challenges:

• Data quality & bias: Inaccurate, incomplete, or biased data threatens to undermine performance and regulatory compliance.

• Explainability: Firms must understand and articulate how AI models make decisions to satisfy internal controls and regulators.

• Cybersecurity: As AI models access more data, the security of sensitive financial and customer information is paramount.

• Governance & regulation: The regulatory landscape is evolving rapidly with new expectations for responsible AI use, transparency, and risk management.

Tony closed with a strong call to action: AI is not just about tech, but operating model transformation. Firms must focus on building enterprise AI maturity with clear strategies, governance frameworks, and skilled teams.

“2025 will be all about moving beyond experimentation and unlocking ROI.” — Financial Services Exec (NextWave Survey)

Resilience in practice: The SS&C executive command centre

One of the key highlights of the event was SS&C’s showcase of its Cyber Defence Operations Executive Command Center—a real-time dashboard providing visibility across security posture (rated at 98%) and digital operational resilience.

The SS&C Resilience Command Center featured:

• 100% DORA Compliance Readiness

• 94% NIS2 Framework Coverage

• 97% Operating Framework Alignment (EU-EU compliant)

• 99% Incident Response Performance (avg. 23 minutes)

Metrics like these underline the shift from reactive risk management to real-time, proactive operational oversight across FS institutions.

AI governance & model risk management: From theory to practice

A core theme of Tony Clark’s keynote was that for AI to scale safely, it must be underpinned by structured governance and model risk management.

SS&C presented a five-step framework for responsible AI implementation:

1. Inventory & Registration: Mapping AI systems, ownership, data usage, and purpose.

2. Risk Classification: Assigning risk tiers based on business impact (e.g., financial, regulatory, reputational).

3. Technical Documentation: Ensuring clear and transparent records of model functionality, assumptions, and design.

4. Risk Assessment: Systematically evaluating bias, explainability, fairness, and cybersecurity across all models.

5. Validation & Monitoring: Continuously testing model performance and adjusting to drift, failure, or bias.

From a model risk perspective, centralised oversight, comprehensive documentation, and clear accountability were flagged as foundational for AI’s long-term success.

 Building real AI systems in financial services

The discussion extended into what it takes to move AI from pilot to production.

Key lessons included:

• Specific AI agents outperform foundation models when embedded in well-designed corporate workflows, making business process engineering the new prompt engineering.

• Prompt design ≠ keywords. Effective Agentic AI requires persona-led prompt engineering and system architecture.

• Communication is key. Multi-agent AI teams need well-defined interaction protocols to collaborate and avoid logic failure.

• LLM choice isn’t just technical—it’s financial. Performance, cost, scalability, and operational fit must all be balanced.

 Opportunities & risks: Striking the right balance

The forum closed with a balanced look at the promises and pitfalls of AI in FS:

Opportunities

• Streamlining risk & compliance operations

• Reducing fraud and improving detection

• Enhancing customer experience with personalisation

• Scaling internal processes efficiently

Risks

• Model failure, bias, or hallucination

• Lack of explainability

• Security vulnerabilities and data misuse

• Regulatory non-compliance

The clear message conveyed was that innovation must go hand-in-hand with resilience, governance, and ethics.

Next Steps: Readiness for What’s Next

The discussions made it clear: financial institutions must not only be resilient in the face of cyber threats, but also future-ready for increasing regulatory scrutiny around AI usage.

Key priorities going forward include:

• Preparing for Q2 DORA Testing Reviews
• Enhancing ICT risk scoring capabilities
• Leveraging real-time incident response data for continuous improvement
• Expanding AI model validation frameworks company-wide

How NextWave can help

This year’s SS&C Algorithmics event made one thing clear: firms that invest in automation, AI governance, and risk-aware innovation today will lead the financial industry tomorrow.

At NextWave, we partner with leading financial institutions to define and deliver business transformation using AI, data, and automation—across the front, middle, and back office.

We support clients across the full lifecycle of AI maturity:

Strategy
We co-develop AI roadmaps that balance ambition with control—shaped by our sector-wide research and delivery experience.

Specialists
Our FS domain and AI engineers design and deploy AI operating models that deliver measurable business outcomes.

Solutions
We provide tested, AI-enabled solutions tailored to FS use cases—accelerating deployment and reducing cost and risk.

Whether you’re starting your AI journey or scaling enterprise-wide, we bring the expertise, frameworks, and tools to help you deliver responsibly.

To learn more about how NextWave helps institutions navigate AI risk, digital transformation, and regulatory readiness, schedule a discussion with us.