16 Oct, 2025
Brief Agenda US
GenAI & Hyperautomation in Finance Summit08:00
08:30
09:00
09:00
09:10
10:10
- 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?
-
Christopher Lacour
Mentormate
-
Darlyne Keller
Rock Valley Credit Union
-
Anthony Pili
Orange Bank & Trust Company
-
Lizzie Pine
JPMorganChase
-
Dimple Thakkar
PradimeAI
10:00
10:10
10:35
- 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?
-
Matt Cashatt
Polyrific
10:35
10:55
- 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
-
Preeti Gupta
CIBC
11:00
11:40
12:00
- People: preparing people and ensuring compliance through change management, upskilling, and governance.
- Platforms: applying integrating platforms to modernize the core, preparing data for AI, and leveraging AI for application-level innovation.
- Why explainability and trust in AI systems are no longer optional—but critical for adoption
- Value: identifying and prioritizing high-value AI scenarios that align with business needs while balancing transformation costs and technical debt.
-
Nicholas
Mentormate
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.
-
Senior Executive
Blue Prism
12:00
12:00
12:45
- 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?
-
Jonas Ng
KeyBank
-
Tipu V. Swaran
Discover Financial Services
-
Anamika Roy
Old Republic Specialty Insurance GroupSenior
-
Paul O'Donovan
BMO
-
Jay Thurston
Thales
-
Dimple Thakkar
PradimeAI
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?
-
Jacob Ingerslev
Tokio Marine HCC
-
Kay Solomon-Okoli
CNA Insurance
-
Kader Sakkaria
Gallagher
-
Tanmay P.
Blue Cross Blue Shield Association
-
Meltem Singh
Gartner (Moderator)
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.
-
Senior Executive
TBC (Reserved for Fibr.AI)
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?
-
Chase Larson
St. Cloud Financial Credit Union
-
Aanand Kapadia
PenFed Credit Union
-
Puneet Malhotra
Personetics
-
Meltem Singh
Independent (Moderator)
15:00
15:35
16:05
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?
-
Arijit Das
Northern Trust
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?
-
Prathap Raghavan
Santander Bank
-
Mo Haghighi
Discover Financial Services
-
Jason Libasci
Bank of America
-
Sandra Molina
Umbrella ( Moderator)
17:15
17:25
17:25
18:25
17:25 – 18:25