Brief Agenda US

GenAI & Hyperautomation in Finance Summit

16 Oct, 2025

09:00

09:00

09:00 – 09:05

09:05

10:05

09:05 – 10:05

  • What is the current state of adoption of GenAI and HyperAutomation across financial institutions?
  • What’s driving the acceleration from general AI to Generative AI and from basic automation to HyperAutomation?
  • Which areas in banking, credit unions, and financial services are most ripe for GenAI transformation today?
  • How are organizations achieving real operational efficiencies through GenAI-driven document handling, reporting, and support function optimization?
  • What are the top 3 practical use cases for GenAI + HyperAutomation currently being implemented?
  • How are institutions using GenAI to reshape loan origination, claims processing, onboarding, and compliance?
  • Can legacy systems realistically be integrated with modern automation stacks without disruption?
  • What risks and compliance functions are now being automated using AI? How do you maintain accuracy and auditability?
  • What guardrails are necessary to avoid “shadow AI” and uncontrolled automation by citizen developers?
  • How are companies balancing the democratization of AI with strong governance and oversight frameworks?
  • What frameworks are emerging for AI ethics, transparency, and explainability, especially in regulated environments?
  • How is Agentic AI reshaping decision-making autonomy in high-stakes environments like capital markets and credit scoring?
  • How are leaders preparing their workforce to operate alongside AI—upskilling, training, and evolving roles?
  • What impact is GenAI having on customer experience, especially with chatbots, NLP-based assistants, and first contact resolution?
  • Are current tools (like Microsoft Copilot) meeting real enterprise AI needs, or just scratching the surface?
  • How are executives measuring the ROI of AI/automation initiatives—what metrics matter most?
  • What are the common pitfalls FIs face when scaling AI across functions?
  • How is quantum computing likely to intersect with GenAI strategies in the next 5–10 years?
  • What’s the “end state” vision for the AI-powered financial enterprise by 2030—and how close are we?
  • How are smaller financial institutions—like credit unions—navigating the GenAI/automation journey with limited resources?
  • What does a successful AI/Automation roadmap look like for mid-sized or community-based financial institutions?
  • How can AI-driven personalization redefine customer journeys in banking, insurance, and credit unions, improving engagement and product adoption?
  • What role does continuous experimentation (A/B testing at scale) play in accelerating innovation and validating new digital experiences for BFSI customers?
  • How are institutions using AI-CRO platforms to reduce acquisition costs while increasing ROI from existing customer bases?
  • What early lessons, missteps, or breakthroughs have emerged that could benefit peers beginning their own AI journey?
  • Where are the most immediate opportunities for AI to improve service delivery, customer engagement, and operational growth in smaller institutions?

10:00

10:05

10:30

10:05 – 10:30

  • How do you evolve GenAI from tactical use cases to enterprise-wide strategic value?
  • What does it take to unify automation across silos without fragmenting data, governance, or trust?
  • Why is responsible AI not just a compliance issue—but a business differentiator in financial services?
  • How are forward-thinking institutions redesigning their operating models for an AI-native future?

10:30

10:50

10:30 – 10:50

  • How to modernize legacy systems to support intelligent automation across enterprise operations
  • What agile, AI-ready operating models look like in global, regulated financial institutions
  • How to build and scale robust AI governance frameworks that ensure control without killing innovation
  • Why aligning product management with data intelligence is key to sustainable GenAI deployment
  • Lessons from the frontlines: cultural, architectural, and leadership shifts required for success

11:00

11:35

11:55

11:35 – 11:55

  • How GenAI is transforming fraud detection—from anomaly spotting to automated triage
  • What it takes to build real-time AML and KYC compliance systems powered by intelligent automation
  • Why explainability and trust in AI systems are no longer optional—but critical for adoption
  • How leading institutions are integrating fraud prevention, compliance, and governance into a unified AI risk strategy

11:55

12:15

11:55 – 12:15

  • How to attain deeper audit insights by combining structured and unstructured data and analytic techniques in the LLM era.
  • Improving the detection of business process issues across banking operations with data, automation and analytics.
  • Enhancing operational and transactional fraud detection innovatively.
  • Getting ahead with governance and regulatory alignment with GenAI.
  • Building a scalable analytics roadmap for sustainable compliance innovation.

12:00

12:15

13:00

12:15 – 13:00

  • How are institutions using AI to detect fraud in real time while reducing false positives?
  • What are the emerging best practices for implementing AI in AML and regulatory reporting?
  • How do we ensure explainability, auditability, and transparency in black-box AI systems?
  • What role does GenAI play in streamlining compliance documentation and reporting?
  • How can AI-driven personalization engines ensure compliance while tailoring digital experiences to individual customers in banking and insurance?
  • What governance models are needed when deploying AI-based experimentation (A/B testing at scale) to
  • safeguard fairness, transparency, and customer trust?
  • How can CRO and personalization platforms balance data-driven targeting with regulatory requirements such
  • as GDPR/CCPA and financial industry compliance?
  • How can AI and automation help bridge gaps across siloed risk, IT, and compliance teams?
  • What regulatory changes are forcing a rethink in AI governance strategies?
  • How are financial organizations building trust internally and externally around AI in high-risk domains?
  • How can GenAI be leveraged to proactively monitor regulatory changes and adapt policies in real time?
  • What internal governance structures (e.g. AI risk committees, model validation teams) are proving most effective in high-stakes environments?
  • In what ways are institutions using AI to automate audit trails and strengthen defensibility during regulator reviews?

13:00

13:45

14:30

13:45 – 14:30

  • What are the practical lessons from operationalizing GenAI — moving beyond experimentation to real-world
  • impact across underwriting, claims, and policy administration?
  • How is GenAI improving speed and precision in underwriting decisions?
  • From slow, manual reviews to AI-accelerated underwriting: enhancing speed, accuracy, and compliance in real-world insurance operations.
  • What are the biggest opportunities and risks in using GenAI for claims and fraud detection?
  • How are insurers embedding automation into compliance, audit, and IT governance workflows?
  • From reactive monitoring to proactive risk detection: using AI to identify anomalies, streamline claims, and strengthen operational oversight.
  • How is AI being used to reduce back-office friction and enhance decision-making speed and accuracy?
  • How are insurers using AI to proactively assess emerging risks like climate, cyber, and systemic threats?
  • What are the top cyber threats today and the leading techniques used by ransomware groups?
  • What role does cyber threat intelligence play in shaping cyber insurance strategies?
  • How can cyber insurers help their policyholders mitigate the risk and impact of ransomware attacks?
  • What role does AI play in strengthening cyber underwriting and risk scoring?
  • What are the implementation challenges of AI in underwriting, and how are organizations overcoming them?
  • What does it take to modernize legacy platforms without compromising control environments?
  • What governance models are helping insurers monitor, audit, and validate GenAI outputs across departments?
  • How are internal controls evolving to manage AI-generated decisions within regulated insurance environments?
  • What are the key considerations in building trustworthy AI systems that align with regulatory, ethical, and data governance standards?

14:00

14:30

14:50

14:30 – 14:50

  • How AI-driven personalization is transforming digital banking and insurance experiences.
  • Moving beyond static channels: delivering real-time, tailored interactions that boost engagement.
  • Embedding continuous experimentation into customer journeys to accelerate product adoption.
  • Practical examples of AI-CRO in BFSI: reducing acquisition costs while improving conversion rates.
  • Turning data into measurable performance: aligning personalization with ROI, compliance, and trust.

14:50

15:35

14:50 – 15:35

  • What makes GenAI and HyperAutomation uniquely valuable to credit unions compared to large banks?
  • How are CUs using AI to improve member service, speed, and personalization?
  • From generic service to personalized engagement: automating routine interactions to boost member satisfaction and free staff for high-value tasks.
  • What constraints do smaller institutions face in adopting these technologies—and how are they overcoming them?
  • How do credit unions ensure governance, ethics, and transparency in automation?
  • What back-office processes (e.g. loan origination, onboarding, fraud, support) are being reimagined with automation?
  • From manual back-office bottlenecks to intelligent automation: streamlining loans, onboarding, and fraud checks to improve accuracy and operational efficiency.
  • How do CUs balance innovation with limited IT budgets and legacy infrastructure?

16:00

16:05

16:30

16:05 – 16:30

  • What does the AI-powered financial enterprise of 2030 look like—and how close are we now?
  • How can firms transition from digital adoption to AI-native operating models?
  • Why ethical, explainable, and human-centric AI will define long-term trust and success
  • What steps are needed today to prepare for convergence of GenAI, Agentic AI, and quantum computing?
  • How are forward-thinking institutions building adaptable, future-proof talent and infrastructure?

16:30

17:15

16:30 – 17:15

  • What will define the AI-native financial institution of 2030?
  • From manual processes to intelligent automation: embedding AI into core operations to enable real-time
  • decision-making, operational scalability, and innovation at speed.
  • How are firms preparing for the convergence of GenAI, Agentic AI, and quantum computing?
  • From reactive to predictive: using AI-driven analytics and workflow orchestration to align talent, technology, and strategy for the next wave of transformation.
  • What ethical, social, and regulatory challenges must we solve to deploy AI responsibly at scale?
  • What role will decentralized finance (DeFi), platform ecosystems, and open banking play in this future?
  • How are banks, insurers, and credit unions evolving talent and leadership to thrive in the next wave of transformation?
  • What’s the biggest threat to long-term innovation—and how do we stay resilient against it?

17:15

17:25

17:15 – 17:25

17:25

18:25

17:25 – 18:25

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