Day One - 10/9Thursday, October 9, 2025 - PST

• Which are the key areas that are being transformed? What is the next one on the list?
• What are the current capabilities of AI in mortgage?
• What kind of labor costs can be saved? Errors that can be prevented?
• Industry legal and regulatory blowback
• What is holding back the industry from further adaption?
• Risks and risk management including data quality and protection
• Impacts on origination, servicing, investment and other key functions

• How are you integrating AI into the everyday life of your loan process?
• Are you going from reducing cost per file to delivering intelligence, speed and strategy at scales?
• Running leaner and meaner using AI
• What is your role in AI development?
• How is AI changing your organizational strategy?
• How are you measuring and quantifying the ROI of your AI investments across different business units?
• What governance frameworks and ethical guidelines have you implemented for responsible AI deployment?
• How are you managing the cultural transformation and change management aspects of AI integration?
• What strategies are you employing to upskill and reskill your workforce to work alongside AI systems?
• Which areas do you see AI being most impactful in going forward?

• How many people does it take to build a model vs. running an off-the-shelf application?
• What do you look for in an off-the-shelf solution?
• Building out a data warehouse… Build or buy?
• Is it worth building a model with the next generation always right around the corner?
• What are the competing models out there?
• Finding talent when you can’t
• Should you use a 3^rd-party to help you select?
• What is your evaluation process?
• Approaches that use both buy and build

• Evaluating your current technology and the tools you are using
• Garbage in, garbage out: Training best practices you need to be aware of
• What are common errors and pain points that eat up valuable time and resources when implementing?
• Integrating with existing mortgage systems
• How do you determine when to launch?
• Evaluating what is being promised vs. what is practical
• Origination, servicing or investments: Where is the most bang for the buck?

• When would you use a start-up?
• Are you going from cost center to creating value?
• ROI of current projects
• Are you creating AI tools for decision making, efficiency or insight?
• How are you getting compliance involved?
• How are you evaluating and mitigating AI-related risks in mortgage underwriting and servicing processes?
• What role does AI play in improving loan portfolio performance and predictive analytics?
• How are you balancing automation with human expertise in complex mortgage decisions?
• What strategies are you implementing to ensure AI models remain accurate across different market conditions and economic cycles?
• Impact on business models

• Streamlining post- close operations
• How much underwriter error can we save?
• What are the barriers to fully underwriting loans using AI?
• Employing AI to foster inclusive credit access
• How fast? Can AI halve your underwriting time?
• Pre-qualification and pulling information in
• Guardrails and system protections

Using AI for which part of origination and onboarding has the highest ROI?

• Which mortgage jobs are most likely to be replaced? Are there different tasks that can be done that will enhance productivity?
• How are you using VAs? The ROI of replacing them with AI solutions
• What AI means to the life of a loan officer?
• Should you have 3^rd parties train? Tools that assist in people development, training and education
• How do you mitigate the hallucinations?
• Cost of offshoring vs. AI
• On-boarding best practices
• Which roles do not see getting replaced?
• 2028 staffing predictions: What are the new jobs to be created; changing roles within customer relationship management and which jobs are most primed to be replaced by AI?

• Blending AI with your CRM tools
• Looking for refi’s and upsells in all the right places
• How have AI-driven predictive models are helping find customers?
• Social media and branding strategies
• What kind of tools do brokers and correspondents need?
• Multi-channel optimization: Identifying optimal communication channels and timing for different customer segments
• Harnessing predictive analytics for customer lifetime value assessment and prioritizing high-potential leads
• Automated lead engagement: Identifying customer lifecycle stage and personalized, appropriate content