The IDAAHub Podcast: AI Healthcare & Finance

Part 3 of our conversation with Kelly Cochran, Research Director at FinRegLab

As AI systems in financial services grow increasingly sophisticated—from traditional machine learning to generative AI and agentic systems—the question of guardrails becomes critical. In this episode, Kelly Cochran breaks down what responsible AI deployment really means in credit underwriting and financial services.

In This Episode:

Kelly explains two essential categories of AI guardrails: protecting data throughout its lifecycle and ensuring analytics models perform as expected. While machine learning models for credit underwriting have established transparency tools, generative AI models present entirely new challenges.

We explore the fundamental differences between ML models trained on curated financial data and large language models trained on vast swaths of the internet. Kelly discusses the transparency challenges of non-deterministic models—systems that might give slightly different outputs for the same inputs—and the serious implications of AI "hallucinations" in financial contexts where consistency is paramount.

The conversation turns to practical safeguards, particularly human-in-the-loop approaches. Kelly shares insights on the delicate balance required: keeping human reviewers engaged without becoming overly reliant on or skeptical of AI recommendations. We discuss how these safeguards must evolve as systems mature, balancing thoroughness with efficiency.

Perhaps most exciting is Kelly's vision for personal financial agents—AI assistants that could democratize access to quality financial planning. Currently, only 40% of U.S. adults work with a financial planner, dropping to just 20% among low-to-moderate income households. AI agents could provide personalized, accurate financial guidance to millions who can't afford traditional advisory services. From day-to-day expense management to long-term goal planning—even filing taxes—these tools could transform financial inclusion.

But Kelly emphasizes the critical importance of getting this right. When serving financially vulnerable populations, malfunctioning AI agents could make situations worse, not better. This requires rigorous testing, thoughtful design, and appropriate guardrails to ensure reliability.

Kelly also offers practical advice for consumers looking to improve their credit access, highlighting the growing use of cash flow data by lenders as an alternative or supplement to traditional credit scores.

Guest Bio: Kelly Cochran is Research Director at FinRegLab, a nonprofit research organization fostering data-driven dialogue about financial innovation. Her work focuses on ensuring technological advances in financial services benefit all consumers equitably and safely.

Resources Mentioned:

• FinRegLab research on cash flow underwriting
• Studies on AI transparency in credit decisions
• Consumer guidance on alternative credit data

Host: Deepti Kalghatgi, Chief Curator at IDAA Hub and VP of Sales & Partnerships at Cogniquest AI

The IDAA Hub Podcast explores the intersection of AI, finance, and healthcare, featuring conversations with industry leaders driving intelligent automation in enterprise operations.

Episode Timestamps: 00:00:24 - What guardrails do complex AI systems need? 00:00:40 - Two types of guardrails: data and analytics 00:01:18 - Difference between ML models and Generative AI 00:02:22 - Non-deterministic models explained 00:02:59 - The hallucination problem in financial services 00:03:42 - Transparency challenges with GenAI 00:04:02 - Human-in-the-loop strategies and limitations 00:15:14 - Personal financial agents: game-changing opportunity 00:15:50 - Access to financial planning statistics 00:17:25 - Financial stability and economic participation 00:18:03 -  Closing thoughts 

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Should AI be allowed to move your money without asking first? It's not a hypothetical question anymore—agentic AI systems are already operating inside financial institutions, and consumer-facing applications aren't far behind.

In Part 2 of our conversation with Kelly, we dive deep into the world of agentic AI systems in financial services. These aren't simple chatbots or standalone AI models—they're sophisticated hybrid platforms combining large language models, machine learning algorithms, and autonomous software agents that can pull data, analyze patterns, and take action in real-time.

Kelly breaks down what makes these systems fundamentally different and why financial institutions are deploying them aggressively for internal use (particularly fraud detection and deep fake mitigation) while taking a much more cautious approach with consumer-facing applications.

EPISODE TIMESTAMPS:

[00:21] Introduction: What are agentic AI systems?

[00:28] Breaking down the technology: Why we say "agentic AI systems" not just "AI agents"

[00:40] The architecture: Multiple elements, software agents, and different tasks

[00:58] Beyond financial services: Where else agentic systems are deployed

[01:04] The role of large language models in orchestration and user interface

[01:14] Machine learning for deep quantitative analytics

[01:21] Why hybrid systems can do things individual models can't

[01:37] The autonomy difference: From reactive to dynamic real-time action

[01:52] Fraud detection use case: Fighting deep fakes and emerging threats

[02:08] Appropriate human oversight: Finding the right balance

[02:15] Personal financial assistants: The "pocket advisor" vision

[02:34] Beyond advice: AI executing transactions and managing daily finances

[02:45] The trust question: How reliable are these systems?

[02:57] Current deployment reality: Building with heavy oversight first

[03:17] The exciting potential: Greater dynamism and active support

[03:45] Where we're seeing adoption: Internal vs. consumer-facing

[03:58] Direct-to-consumer caution: Why banks are moving slower

[04:08] Shopping agents: E-commerce AI and the payment connection

[04:31] The "final yes" question: Why consumers still click to buy

[04:46] The future: Computer-to-computer interactions and changing parameters

[20:27] Credit underwriting deep dive: Data quality concerns

[20:39] Transparency and explainability challenges in AI models

[20:55] Comparing systems: Rules-based vs. machine learning vs. human judgment

[21:04] The human "black box": Subjective decisions are hard to pinpoint too

[21:18] Pros and cons: What machine learning improves (and what it doesn't)

[21:46] When things go wrong: Impact on customers, lenders, and the economy

[22:03] Human biases vs. human intuition: The complicated trade-offs

[22:18] Mission-based lending: Working with underserved borrowers

[22:37] High-touch meets high-tech: Marrying traditional relationships with AI

[22:52] The leverage effect: Data and technology expanding credit access

[23:09] Important limitations: What AI alone can't solve

[23:39] Processing benefits: Speed, efficiency, and consistency

[23:56] Machine learning nuances: Capturing patterns simple models miss

[24:10] Reliability concerns: Sensitivity to data changes

KEY TAKEAWAYS:

✅ Agentic AI systems combine LLMs, ML models, and software agents for autonomous action
 ✅ Internal fraud detection deployment is moving fast; consumer apps more cautiously
 ✅ Shopping AI exists now, but humans s

The Credit Access Crisis You Didn't Know Existed

In Part 1 of this conversation, Kelly Cochran drops a truth bomb that changes everything: Millions of creditworthy people are getting rejected for loans right now—not because they won't pay them back, but because traditional credit systems can't evaluate them efficiently.

Kelly Cochran is Deputy Director and Chief Program Officer at FinRegLab, but her journey to this role gives her a perspective almost no one else has. She was at US Treasury during the 2008 financial crisis, helped draft the Dodd-Frank Act, led the effort to stand up the Consumer Financial Protection Bureau (CFPB), and now leads research on responsible AI deployment in financial services.

What You'll Discover in Part 1:

🎯 AI in Banking Is NOT New "It's been used in things like fraud detection for decades." Most people think AI in finance started with ChatGPT in 2022. Kelly explains why this misconception is causing real problems for innovation and regulation.

💳 The Real Credit Underwriting Problem "There are a lot of applicants who don't have traditional credit records. And it's not necessarily that they're really actually less likely to pay the loan back. It's that traditional underwriting systems can't really evaluate them easily."

The issue? Evaluation COST, not borrower RISK.

Traditional systems reject creditworthy people because:

• Processing alternative data is expensive
• Models are built for conventional credit histories
• Per-applicant evaluation costs are too high
• The system defaults to "can't predict = reject"

Machine learning is changing this equation—and the implications for financial inclusion are massive.

🤖 Generative AI vs. Traditional ML: Why It Matters Kelly breaks down the critical distinction between ChatGPT-style generative AI and the machine learning that's been transforming financial services for decades. They're "different and substantially more complex" from each other, requiring different approaches and risk management.

⚖️ The "Move Fast and Break Things" Problem "In financial services, there's a really high value placed on figuring out what's going to work, making sure things are reliable, not just launching something and fixing it on the fly."

When things break in banking, real people lose homes, businesses, and economic opportunity. This creates unique tension between innovation speed and responsible deployment.

Topics Covered in Part 1:

✓ Kelly's remarkable journey: UNC Chapel Hill → Treasury → CFPB → FinRegLab ✓ FinRegLab's mission: Making data and technology work for financial inclusion ✓ Why AI in financial services has a 30+ year history ✓ The credit underwriting transformation and its impact on consumers and small businesses ✓ Expanding AI applications: fraud detection, customer service, back office operations, financial advice ✓ The privacy, security, and data management challenges ✓ Is AI overhyped? "I think there's a little of both" ✓ The "muddy language" problem: How AI gets described differently to investors, regulators, and consumers ✓ ChatGPT's 2022 launch and the generative AI inflection point ✓ Why generative AI systems are fundamentally different from traditional machine learning ✓ The cultural tension in financial services between excitement and reliability

Episode Tags: AI, artificial intelligence, fintech, financial services, credit underwriting, machine learning, generative AI, ChatGPT, financial inclusion, CFPB, consumer financial protection bureau, regulatory technology, banking innovation, FinRegLab, Kelly Cochran, credit scores, loan underwriting, fraud detection, Dodd-Frank, financial regulation, responsible AI, enterprise AI, banking technology, credit access, economic development, part 1

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$2,500 for a painkiller. $10,000 for a routine procedure. All because nobody told you the ONE question to ask. This episode could save you thousands—or help your hospital recover millions.

🎯 THE GAME-CHANGING CONVERSATION:

Have you ever opened a medical bill and thought, "Wait, WHAT?"

You're not alone. And here's what makes it worse: The strategies hospitals use to recover MILLIONS in denied claims? They're the EXACT same strategies you can use to save THOUSANDS on your medical bills.

But nobody's connecting the dots.

In this explosive episode, healthcare revenue cycle expert Elizabeth Daily reveals how one hospital went from a 30% denial rate and $18 million in losses to recovering that money within 6 months—and how YOU can apply these same strategies to your own bills.

🔥 WHAT YOU'LL DISCOVER:

THE WRONG QUESTION vs. THE RIGHT QUESTION
Most people ask: "Do you take my insurance?"
You should ask: "Are you in-network with my SPECIFIC PLAN?"
That one word—"specific"—is worth thousands of dollars.

THE $18 MILLION RECOVERY STORY
• How one hospital reduced denials by 40%
• The exact playbook they used
• Why centralizing pre-authorizations was the game-changer
• How they turned burned-out staff into empowered teams

PATIENT SURVIVAL STRATEGIES
• Why your EOB (Explanation of Benefits) is intentionally confusing
• How to request written estimates (you're legally entitled!)
• The No Surprise Act and what it really protects
• Financial assistance programs hiding in plain sight
• When and how to appeal denied claims
• The unexpected benefit of getting patients involved in the process

INDUSTRY SECRETS EXPOSED
• Why hospitals have to learn every insurance company's "playbook" while insurers only follow their own rules
• The real reason more hospitals are going out-of-network
• High-deductible plans: what's actually covered before you hit your deductible
• AI in medical billing: what works, what doesn't, and why you still need humans

⏱️ EPISODE TIMESTAMPS:

0:00 - Introduction: The $2,500 Painkiller Nightmare
1:00 - In-Network vs Out-of-Network: What's Really Going On?
3:00 - The "Handshake" Between Hospitals and Insurance Companies
7:00 - Why Your Medical Bills Are Designed to Confuse You
11:00 - Healthcare Costs Are Skyrocketing - Here's Why It Matters
13:00 - The Top Reasons Your Claims Get Denied
21:00 - SUCCESS STORY: How One Hospital Recovered $18 Million
25:00 - What Every Patient Can Do RIGHT NOW to Protect Themselves
32:00 - AI in Healthcare Billing: Hype vs Reality
38:00 - THE CRITICAL QUESTION That Changes Everything
42:00 - Your Legal Rights: The No Surprise Act Explained
45:00 - Financial Assistance Programs (Why Aren't More People Using These?!)
48:00 - Industry Shift: Why Hospitals Are Going Out-of-Network
53:00 - The Hidden Insurance Benefits You Don't Know About
56:00 - Final Takeaways: Ask Before the Service, Not After the Bill


🏥 FOR HOSPITAL ADMINISTRATORS:

If you're dealing with:
- Sky-high denial rates
- Burned-out billing staff
- Millions left on the table
- Revenue cycle chaos


💰 FOR PATIENTS:

If you've ever:
- Been shocked by a surprise medical bill
- Assumed your insurance covered something (it didn't)
- Felt powerless against the healthcare billing system
- Wondered why a simple procedure cost thousands

This episode gives you the knowledge hospitals have—knowledge that can save you thousands.

🎙️ ABOUT OUR GUEST:

Elizabeth Daley brings two decades of healthcare revenue cycle management experience. She's helped hospital systems navigate the complex world of insurance claims, denials, and out-of-network billing—recovering millions in the process. Now she's sharing insider knowledge that every patient needs to know.

⚡ KEY TAKEAWAYS YOU CAN USE TODAY:

1.

AI in Healthcare Revenue Cycle Management: With Claims, Denial management and out-of-network nightmares, Will AI replace jobs?

Is AI really transforming healthcare revenue cycle management, or is it just another buzzword? In this episode, we sit down with Elizabeth Daley, VP of Out of Network at Revco Solutions, who brings 20 years of hands-on experience in healthcare RCM.

Elizabeth shares insider insights on:
- Why denials remain the #1 challenge (and how AI is helping)
- Real-world AI applications delivering measurable results
- The truth about AI "replacing" healthcare jobs (spoiler: it's not)
- Data security and HIPAA compliance in the age of AI
- When to trust AI vs. human decision-making
- Strategies for successful out-of-network reimbursement
- How to evaluate AI solutions for your organization

Key Takeaway: AI isn't here to replace healthcare professionals—it's here to eliminate busywork and enhance what humans do best. Elizabeth explains why the hybrid approach (combining human expertise with AI tools) is winning in healthcare organizations today.

Whether you're a healthcare administrator, RCM professional, or simply curious about AI's role in healthcare, this conversation offers practical insights you can apply immediately.

Guest: Elizabeth Daly, VP of Out of Network, Revco Solutions


Topics: AI in Healthcare, Revenue Cycle Management, Healthcare Administration, Claims Denials, Prior Authorization, Out-of-Network Reimbursement, Healthcare Technology, HIPAA Compliance, Healthcare Innovation


📧 Connect with IDAAHub 

Follow us on:

LinkedIn: https://www.linkedin.com/company/idaahub/

https://www.youtube.com/@IDAAHUB

https://open.spotify.com/show/3V8Vuhqkibej5fUwtwkUMx

https://podcasts.apple.com/us/podcast/the-idaa-hub-podcast-ai-in-finance-healthcare/id1848710327

📧 Connect with Host 

Host Deepti Kalghatgi : https://www.linkedin.com/in/deepti-kalghatgi/

🌐 Visit: https://idaahub.com

Visit https://revcosolutions.com/

Subscribe and leave a review to help other healthcare professionals discover practical AI insights!

#AIinHealthcare #RevenueCycleManagement #HealthcareTechnology #RCM #HealthcareInnovation

In Part 2 of our conversation on the IDA Podcast, we continue our compelling discussion with Dr. Neil about AI in healthcare. Dr. Neil doesn't hold back as he addresses the critical ethical questions facing the healthcare industry in the age of AI.

From accountability concerns to the need for explainable AI, Dr. Neil shares his insights on why technology companies must take responsibility for their algorithms' decisions—just as traditional media has done for centuries. He reveals that 70-80% of current medical decisions are based on pre-FDA era algorithms and century-old statistics, highlighting both the urgent need for AI innovation and the importance of implementing it ethically.

This episode explores the tension between technological capability and human-centered care, the difference between generalized and personalized AI, and why the sacred physician-patient relationship must remain at the heart of healthcare—even as AI transforms the field.

Whether you're a healthcare professional, tech innovator, policymaker, or patient, this conversation will challenge your assumptions and provide valuable perspectives on the future of AI-enabled medicine.

Note: This is Part 2 of our conversation with Dr. Neil. Listen to Part 1 for the complete discussion on AI in healthcare.

Key Takeaways:

• Why AI companies must accept liability for algorithmic errors
• The importance of explainable AI in clinical decision-making
• How current medical statistics and clinical trials may not be ethically applied globally
• The need for personalized AI assistance rather than general intelligence in healthcare
• What patients should look for when choosing AI-enabled physicians
• Why humanity and ethics must come before efficiency and profit

Keywords/Tags: AI in healthcare, medical AI, healthcare technology, ethical AI, explainable AI, digital health, healthcare innovation, physician-patient relationship, medical ethics, clinical decision support, healthcare algorithms, personalized medicine, FDA approval, clinical trials, health insurance, medical technology, healthcare policy, future of medicine, AI accountability, healthcare AI

Guest Information: Dr. Neil Mukherjee

This episode contains frank discussions about medical decision-making, and healthcare system challenges.

Episode Type: Interview

Explicit Content: No

🚀 "IF AI WAS MY JUNIOR RESIDENT, I WOULD'VE FIRED HIM"

Brutal honesty from Dr. Neil Mukherjee—and exactly what healthcare needs to hear about AI right now.

In this unfiltered conversation, Dr. Mukherjee (Director at Northwell Health, MIT Med Hack winner, 15+ years as a surgeon) shares insights you won't find in any AI vendor pitch or industry report. From ambient scribing to clinical decision-making, computer vision to insurance AI—he breaks down what's actually working versus what's still just hype.


📊 ABOUT DR. NEIL MUKHERJEE:

Dr. Neil Mukherjee is a Director at Northwell Health and practicing surgeon specializing in advanced GI and minimally invasive surgery. His credentials include:

- 🏆 MIT Med Hack Winner (one of world's largest health hackathons)
- 🏆 Cleveland Clinic Hackathon Champion (First Prize with HIMSS)
- 🎤 Opened for VP Biden at the Cancer Moonshot Project
- 🏅 Intel's Creativity Award for Best Use of Technology
- 🏅 Best Design Award for Smart Shape
- 💼 Co-founder of early-stage healthcare startup
- 📚 Medical school in India, surgical training in Tampa & New York
- 🔬 Handles complex abdominal cases beyond typical surgeon capability


⏱️ EPISODE TIMESTAMPS:

0:00 - Introduction & Welcome to IDAA Hub Podcast
1:15 - Dr. Mukherjee's Background & Journey
2:15 - From Small Mining Town in India to New York Surgery
3:45 - Winning MIT Med Hack & Cleveland Clinic Hackathons
4:30 - Opening for VP Biden at Cancer Moonshot Project
5:20 - How AI Entered Healthcare
6:10 - The Calculator Analogy: Why We Fear New Technology
8:45 - Evolution of Surgery: Open to Laparoscopic to Robotic
10:30 - The Robotic Surgery Revolution
11:45 - Class 10 Student Matching 20-Year Surgeon's Finesse
12:20 - Is AI in Healthcare Hype or Reality?
14:30 - AI Is Already Here: Insurance & Hospital Operations
16:40 - Where AI Is Affecting Your Care Right Now
18:50 - The "Sacred" Doctor-Patient Relationship
20:15 - The Clinical AI Problem: Garbage In, Garbage Out
22:40 - Training AI on Administrative vs. Clinical Data
24:30 - How Doctors Actually Use AI Today
26:20 - Personal Use vs. Clinical Use of AI
28:45 - Northwell Health's AI Hub
30:15 - ChatGPT, Claude & Gemini: HIPAA Compliance
32:10 - AI in Radiology: Flagging Abnormalities
34:00 - Improving Sensitivity & Accuracy
35:20 - Computer Vision in Colonoscopies
37:10 - High Sensitivity vs. Low Specificity
38:50 - Ambient Scribing: The Biggest Impact
40:30 - How Ambient Scribes Actually Work
42:00 - Multi-Language Support: English & Spanish
43:15 - The Problem: AI Prioritizes Differently
44:15 - "If AI Was My Resident, I'd Fire Him"
46:30 - Missing Human Nuance: Tone & Urgency
48:30 - The Trust Factor: We're Not There Yet
50:00 - Future: 10-15 Different "Species" of AI Scribes
52:00 - Closing Thoughts & Next Steps


💡 KEY INSIGHTS COVERED:

WHAT'S ACTUALLY WORKING TODAY:

✓ Computer Vision in Procedures
- Colonoscopy AI detecting polyps with high sensitivity
- Catching what humans might miss due to fatigue
- First colonoscopy vs. 10th colonoscopy consistency
- "We're humans—before coffee, after coffee accuracy differs. AI only gets better."

✓ Radiology AI Flagging
- Detecting abnormalities in lungs, brain, and other organs
- Improving clinician sensitivity and accuracy
- Reducing turnaround time
- Human verification still required (AI flags, human decides)

✓ Ambient Clinical Scribing
- Recording physician-patient conversations automatically
- Generating clinical notes without manual typing
- Saving 1-2 hours of documentation time per day
- Multi-language support (English, Spanish, others)
- Patient, family member, and doctor speech differentiation

✓ Administrative Efficiency
- AI deciding insurance coverage (alread

🚀 HEALTHCARE AI JUST EXPLODED—AND 80% OF THE MARKET IS STILL UNTAPPED

Healthcare organizations are now deploying AI at 2.2X the rate of other industries. In just 12 months, adoption jumped from 3% to 22%—a 7X increase. $1.4 billion in spending. Eight new unicorns. And procurement cycles that dropped from 12-18 months to just 4.7 months.

In this episode, I break down Menlo Ventures' comprehensive State of AI in Healthcare report—based on surveys of 700+ healthcare executives and dozens of industry conversations. This is the most detailed analysis of where healthcare AI is today and where it's headed next.


📊 KEY FINDINGS COVERED:

• Why healthcare went from "digital laggard" to AI adoption leader
• Where the $1.4 billion is actually flowing (category by category)
• How top health systems like Mayo Clinic and Kaiser evaluate AI solutions
• Why startups are capturing 85% of spending (and how long that window stays open)
• The provider-payer AI arms race that's brewing right now
• What pharma's 66% proprietary model buildout means for drug discovery
• The massive services-to-software conversion opportunity ($740B available)


⏱️ EPISODE TIMESTAMPS:

0:00 - Introduction: Healthcare's Shocking AI Reversal
1:30 - The Great Reversal: How Healthcare Took the Lead
5:00 - How Leading Organizations Choose AI (Mayo, Cleveland, Kaiser Framework)
7:30 - The Speed Revolution: Procurement in 4.7 Months
10:00 - Where the $1.4 Billion Is Really Going
13:30 - The Empire Strikes Back: Startups vs. Giants
16:30 - The Provider-Payer AI Arms Race
18:30 - Pharma & Biotech's Quiet Revolution
20:00 - What This Means for You (Actionable Takeaways)


💡 YOU'LL LEARN:

→ Why healthcare adoption jumped 700% in 12 months
→ The three-part framework top health systems use to evaluate AI
→ Which categories are growing fastest (prior auth: 10X, patient engagement: 20X)
→ Why ambient clinical documentation hit $600M but is already plateauing
→ How the $98B prior authorization market is only 3% software (massive opportunity)
→ Why customers are buying from startups but say they prefer incumbents
→ What the provider-payer AI standoff means for both sides
→ Where the next 5 healthcare AI unicorns will come from


📈 MARKET INSIGHTS:

ADOPTION & SPENDING:
• 22% of healthcare orgs have implemented domain-specific AI tools
• $1.4 billion in healthcare AI spending (nearly 3X 2024's investment)
• Health systems: $1B (75% of total spending)
• Outpatient providers: $280M (20%)
• Payers: $50M (5%)

PROCUREMENT ACCELERATION:
• Health systems: 8.0 months → 6.6 months (18% faster)
• Outpatient providers: 6.0 months → 4.7 months (22% faster)
• Payers: 9.4 months → 11.3 months (slowing, still experimenting)

TOP SPENDING CATEGORIES:
• Ambient clinical documentation: $600M (largest category)
• Coding & billing automation: $450M
• Prior authorization: Growing 10X YoY
• Patient engagement: Growing 20X YoY
• Payer operations: $50M (growing 5X YoY)

STARTUP vs. INCUMBENT:
• Startups capture 85% of all generative AI spending
• Even in ambient scribing (where Nuance had 77% of hospitals), startups captured 70% of new market
• Customer paradox: Buying from startups but prefer EHR vendors

Full Description:

In this episode of AI in Finance talks, we dive deep into the intersection of AI and financial services with Shaun Neal, a technology executive with an impressive 30-year career spanning Merrill Lynch, credit card companies, and FinTech ventures.

Shaun is building Quantari, an innovative AI-powered platform that provides fractional CFO services to startups and small businesses. What makes this conversation fascinating is not just what he's building, but HOW he's building it - leveraging GPT-5 to generate up to 20,000 lines of code per day.

What You'll Learn:

✅ The Genesis Story: How a bookkeeper's vision to "put himself out of business" led to an AI startup

✅ Product Evolution: Why their first iteration failed and how customer feedback drove a complete UX redesign

✅ Building with AI: Real insights on using GPT-5 for rapid development - from losing context at 300 lines to handling 10-15 files with 5,000+ lines each

✅ Trust & Security: How to handle the sensitive challenge of accessing customers' bank accounts and financial data

✅ Customer Validation: Why startups seeking VC funding became an unexpected primary market

✅ The "Am I Fundable?" Feature: How VCs are showing interest in using the platform to validate investments

✅ Go-to-Market Strategy: Timeline, beta testing, and the role of accelerators like Conscious Venture Labs

Key Quotes:

"With GPT-5, I can feed it 10 to 15 files of five or 600 lines of code each. It will spit back five or 600 lines of code after 10 or 15 seconds. I can literally generate 20,000 lines of code a day."

"When you hire a CFO, they look at your books, they look at your bank accounts, they understand your business, and then they TELL you. We've completely changed the paradigm to be much more of a push."

"Customer service jobs in financial services? They won't exist in 18 months."

About the Guest:

Shaun Neal is a technology executive with 30 years of experience.

Connect with Shaun:

• LinkedIn: [Add Shaun's LinkedIn URL]
• Quantary Website: quantary.ai
• Interested in beta testing? Reach out via LinkedIn!

Timestamps: 00:00 - Introduction & Welcome 01:00 - Shaun's Background: From Merrill Lynch to CTO 03:00 - What is Quantary? The Fractional AI CFO Concept 05:00 - Building with AI: GPT-5 Development Experience 07:00 - How the Product Works: Conversational Financial Analysis 09:00 - Customer Validation & Early Insights 11:00 - Product Iterations: From Chat Interface to Push Notifications 13:00 - The "Am I Fundable?" Feature 15:00 - Market Differentiation & Unique Value Proposition 16:00 - Timeline to GA & Beta Testing Opportunities 18:00 - The Future: Scaling Companies with 10x Fewer Employees 20:00 - Concerns About the Next Generation of Developers 22:00 - Closing Thoughts

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In the inaugural episode of The IDAA Hub Talks Podcast, hosts Deepti and Deepak introduce the show and discuss its unique focus on navigating the AI marketplace to find effective solutions. Unlike other tech podcasts, IDAA Hub aims to provide insights on practical applications of AI rather than theoretical discussions. This introductory episode examines the State of AI , including a significant MIT research finding that 95% of AI Pilots fail, concerns about AI reaching a valuation bubble, and the critical role of data in AI success,  Every podcast will feature guests from Finance & Healthcare industry Ledaers and startups can showcase/talk about their AI Innovation. The aim is to explore enterprise AI adoption and innovation from startups