Sydney AI in FSI Summit 2027

Beat the rush and join us early for complimentary barista-made coffee and breakfast.

As financial institutions move from AI that recommends to AI that autonomously executes; approving loans, rebalancing portfolios, settling claims, this keynote examines what genuinely changes when decisions once made by humans start being made by agents, and sets the tone for a day exploring what that shift demands of governance, culture, and trust.

• Tracing the real trajectory of AI's role in financial institutions over the past few years, and why 2027 marks a genuine inflection point rather than incremental progress.
• The governance, audit trail, and human-oversight infrastructure that needs to exist before an institution should let an AI agent act without a human in the loop.
• Where does agentic AI genuinely create value versus introduce unacceptable risk, and how do institutions build the muscle to tell the difference?

As Australian regulators continue to develop their approach to AI oversight in financial services, this keynote looks at what recent guidance, enforcement activity, and public statements reveal about where regulatory expectations are heading, and how institutions should be positioning themselves ahead of formal requirements rather than reacting to them.

• What APRA and ASIC's existing statements on AI governance signal about future formal requirements, even where specific rules haven't yet been finalised.
• What lessons Australian institutions can draw from how AI regulation is developing in comparable financial markets, without assuming Australia will follow the same path.
• Practical approaches institutions are taking to build AI governance robust enough to satisfy requirements that don't yet formally exist, rather than waiting for certainty before acting.

Despite years of investment in data infrastructure, many financial institutions still find their AI ambitions constrained by fragmented, poor-quality, or siloed data. This panel examines why the data foundation problem persists and what's genuinely closing the gap.

• Understanding why data fragmentation remains a persistent constraint even at institutions that have invested heavily in modernisation.
• How leaders are deciding where to invest limited data engineering capacity when multiple AI initiatives are all constrained by the same underlying data problems.
• How leading institutions are reframing data governance work to directly support AI deployment speed, rather than treating it as a separate compliance obligation.

As AI models proliferated across the organisation faster than the model risk team could individually review them, one risk leader rebuilt their governance framework around tiered, risk-proportionate oversight, maintaining rigour for high-stakes models without creating a bottleneck for lower-risk use cases.

• How the team identified that applying the same validation rigour to every model, regardless of stakes, was slowing adoption without improving safety.
• The practical criteria used to categorise models by potential impact, and how those tiers determined the depth of review and monitoring required.
• How automated monitoring and documentation tools let a stable risk team keep pace with a rapidly growing model inventory.

A hands-on, interactive session working through a real AI in financial services scenario as a room. Details announced soon.

Facing the challenge of deploying a sophisticated machine learning credit model while maintaining genuine explainability across very different audiences, one lending leader built an explainability layer that satisfied regulatory scrutiny, customer transparency requirements, and legal defensibility simultaneously.

• How the team designed distinct explanation outputs for regulators, customers, and internal dispute resolution, rather than a single generic explanation.
• The trade-offs made between model complexity and explainability, and where the team drew the line.
• How the team stress-tested their explanation framework against real dispute scenarios before deploying the model into production.

After an initial AI-driven claims automation project succeeded in speed but drew criticism for depersonalised handling of complex cases, one insurance leader rebuilt the system to combine automation's efficiency with better outcome measurement and appropriate human escalation.

• How customer feedback and complaint data revealed that speed gains were masking a decline in outcome quality for complex claims.
• How the team redesigned the routing logic to identify which claims genuinely needed human judgement, regardless of dollar amount.
• The shift from processing-time metrics to outcome and satisfaction measures that better reflected genuine customer experience.

As AI agents move from experimentation into live financial services operations, this panel brings together leaders from banking, insurance, and superannuation to discuss where they've drawn genuine lines around autonomous decision-making, and what's shaping those decisions in practice.

• Concrete examples of where agentic AI has been given genuine autonomy versus where human sign-off remains non-negotiable, and the reasoning behind each line.
• How institutions are building oversight mechanisms robust enough that humans genuinely intervene when something looks wrong, rather than rubber-stamping AI decisions by default.
• Lessons from early incidents involving autonomous AI decisions, and how institutions are building incident response processes specifically suited to agentic failures.

Small-group, discussion-based sessions where you'll work through real AI in financial services challenges with peers in similar roles. Roundtable topics will be announced soon.

Put your knowledge to the test in this fast-paced quiz covering real-world trivia, key concepts, and emerging trends. Compete for bragging rights — and a voucher — as the top scorer takes the crown.

Facing board pressure to accelerate AI deployment while risk and compliance teams pushed for more rigorous governance, one AI leader built a framework that satisfied both imperatives; demonstrating that proper governance could accelerate safe deployment rather than slow it down.

• How the team repositioned governance conversations around reducing costly rework and failed deployments, rather than simply adding caution.
• How proportionate governance let low-stakes AI applications move quickly while reserving deeper scrutiny for genuinely high-risk deployments.
• How a handful of well-governed early deployments built the board's confidence to support continued investment in the framework.

As AI models move from static, well-understood scoring tools into adaptive, self-updating systems making higher-stakes decisions, this keynote examines why model risk management frameworks built for traditional statistical models are proving inadequate, and what Australian financial institutions are building instead.

• How continuously learning and fine-tuned models break the assumptions traditional model risk frameworks were built on.
• Practical approaches institutions are taking to maintain genuine explainability for regulators and customers as models become more complex and adaptive.
• How risk teams are restructuring themselves and their tooling to keep pace with the sheer number of AI models now embedded across the institution, rather than treating each one as a bespoke review.

With the AI vendor landscape evolving faster than most institutions' sourcing and procurement processes can track, this Think Tank examines how financial services leaders are making build-versus-buy-versus-partner decisions in a market where today's cutting-edge vendor capability may be commoditised within a year.

• How institutions are managing dependency risk when core AI capability is built on a third-party platform whose direction they can't dictate.
• Where institutions are finding genuine competitive advantage in proprietary AI development versus where off-the-shelf or partnered solutions are the smarter allocation of resources.
• How procurement and contracting approaches are evolving to avoid multi-year lock-in with AI vendors in a market changing this quickly.

Unwind with your peers for a couple of drinks on us!

Beat the rush and join us early for complimentary barista-made coffee and breakfast.

As financial institutions move from AI that recommends to AI that autonomously executes; approving loans, rebalancing portfolios, settling claims, this keynote examines what genuinely changes when decisions once made by humans start being made by agents, and sets the tone for a day exploring what that shift demands of governance, culture, and trust.

• Tracing the real trajectory of AI's role in financial institutions over the past few years, and why 2027 marks a genuine inflection point rather than incremental progress.
• The governance, audit trail, and human-oversight infrastructure that needs to exist before an institution should let an AI agent act without a human in the loop.
• Where does agentic AI genuinely create value versus introduce unacceptable risk, and how do institutions build the muscle to tell the difference?

As Australian regulators continue to develop their approach to AI oversight in financial services, this keynote looks at what recent guidance, enforcement activity, and public statements reveal about where regulatory expectations are heading, and how institutions should be positioning themselves ahead of formal requirements rather than reacting to them.

• What APRA and ASIC's existing statements on AI governance signal about future formal requirements, even where specific rules haven't yet been finalised.
• What lessons Australian institutions can draw from how AI regulation is developing in comparable financial markets, without assuming Australia will follow the same path.
• Practical approaches institutions are taking to build AI governance robust enough to satisfy requirements that don't yet formally exist, rather than waiting for certainty before acting.

Despite years of investment in data infrastructure, many financial institutions still find their AI ambitions constrained by fragmented, poor-quality, or siloed data. This panel examines why the data foundation problem persists and what's genuinely closing the gap.

• Understanding why data fragmentation remains a persistent constraint even at institutions that have invested heavily in modernisation.
• How leaders are deciding where to invest limited data engineering capacity when multiple AI initiatives are all constrained by the same underlying data problems.
• How leading institutions are reframing data governance work to directly support AI deployment speed, rather than treating it as a separate compliance obligation.

As AI models proliferated across the organisation faster than the model risk team could individually review them, one risk leader rebuilt their governance framework around tiered, risk-proportionate oversight, maintaining rigour for high-stakes models without creating a bottleneck for lower-risk use cases.

• How the team identified that applying the same validation rigour to every model, regardless of stakes, was slowing adoption without improving safety.
• The practical criteria used to categorise models by potential impact, and how those tiers determined the depth of review and monitoring required.
• How automated monitoring and documentation tools let a stable risk team keep pace with a rapidly growing model inventory.

A hands-on, interactive session working through a real AI in financial services scenario as a room. Details announced soon.

Facing the challenge of deploying a sophisticated machine learning credit model while maintaining genuine explainability across very different audiences, one lending leader built an explainability layer that satisfied regulatory scrutiny, customer transparency requirements, and legal defensibility simultaneously.

• How the team designed distinct explanation outputs for regulators, customers, and internal dispute resolution, rather than a single generic explanation.
• The trade-offs made between model complexity and explainability, and where the team drew the line.
• How the team stress-tested their explanation framework against real dispute scenarios before deploying the model into production.

After an initial AI-driven claims automation project succeeded in speed but drew criticism for depersonalised handling of complex cases, one insurance leader rebuilt the system to combine automation's efficiency with better outcome measurement and appropriate human escalation.

• How customer feedback and complaint data revealed that speed gains were masking a decline in outcome quality for complex claims.
• How the team redesigned the routing logic to identify which claims genuinely needed human judgement, regardless of dollar amount.
• The shift from processing-time metrics to outcome and satisfaction measures that better reflected genuine customer experience.

As AI agents move from experimentation into live financial services operations, this panel brings together leaders from banking, insurance, and superannuation to discuss where they've drawn genuine lines around autonomous decision-making, and what's shaping those decisions in practice.

• Concrete examples of where agentic AI has been given genuine autonomy versus where human sign-off remains non-negotiable, and the reasoning behind each line.
• How institutions are building oversight mechanisms robust enough that humans genuinely intervene when something looks wrong, rather than rubber-stamping AI decisions by default.
• Lessons from early incidents involving autonomous AI decisions, and how institutions are building incident response processes specifically suited to agentic failures.

Small-group, discussion-based sessions where you'll work through real AI in financial services challenges with peers in similar roles. Roundtable topics will be announced soon.

Put your knowledge to the test in this fast-paced quiz covering real-world trivia, key concepts, and emerging trends. Compete for bragging rights — and a voucher — as the top scorer takes the crown.

Facing board pressure to accelerate AI deployment while risk and compliance teams pushed for more rigorous governance, one AI leader built a framework that satisfied both imperatives; demonstrating that proper governance could accelerate safe deployment rather than slow it down.

• How the team repositioned governance conversations around reducing costly rework and failed deployments, rather than simply adding caution.
• How proportionate governance let low-stakes AI applications move quickly while reserving deeper scrutiny for genuinely high-risk deployments.
• How a handful of well-governed early deployments built the board's confidence to support continued investment in the framework.

As AI models move from static, well-understood scoring tools into adaptive, self-updating systems making higher-stakes decisions, this keynote examines why model risk management frameworks built for traditional statistical models are proving inadequate, and what Australian financial institutions are building instead.

• How continuously learning and fine-tuned models break the assumptions traditional model risk frameworks were built on.
• Practical approaches institutions are taking to maintain genuine explainability for regulators and customers as models become more complex and adaptive.
• How risk teams are restructuring themselves and their tooling to keep pace with the sheer number of AI models now embedded across the institution, rather than treating each one as a bespoke review.

With the AI vendor landscape evolving faster than most institutions' sourcing and procurement processes can track, this Think Tank examines how financial services leaders are making build-versus-buy-versus-partner decisions in a market where today's cutting-edge vendor capability may be commoditised within a year.

• How institutions are managing dependency risk when core AI capability is built on a third-party platform whose direction they can't dictate.
• Where institutions are finding genuine competitive advantage in proprietary AI development versus where off-the-shelf or partnered solutions are the smarter allocation of resources.
• How procurement and contracting approaches are evolving to avoid multi-year lock-in with AI vendors in a market changing this quickly.

Unwind with your peers for a couple of drinks on us!