The mortgage and protection broker’s newly formed AI ethics committee will give employees the opportunity to offer input on how AI is utilised, which ensures that innovation will be balanced with transparency, fairness and accountability.

Heron Financial said the AI training will be a “combination of technical learning with ethical oversight” to ensure the “responsible integration” of the technology into Everglades, its digital platform.

As part of its AI student cohort, a number of members from different teams will be upskilled with an AI development programme.

Matt Coulson (pictured), the firm’s co-founder, said: “I’m proud to be launching our first AI learning and development cohort. We see AI not just as a way to enhance our operations, but as an opportunity to enhance our people.

“By investing in their skills, we’re giving them knowledge they can use in their work and daily lives. Human and technology centricity are part of our core values; it’s only natural we bring the two together.”

‘It’s about combining technology and human expertise to raise the bar’

Alongside this, Heron Financial’s Everglades platform will be embedded with AI-driven innovations comprising an AI reporting assistant, an AI adviser assistant and a prediction model, which it said uses basic customer details to predict the likelihood of them gaining a mortgage.

The platform aims to support prospective and existing homeowners by simplifying processes, as well as providing a referral route for clients and streamlining the sales-to-mortgage process to help sales teams and housebuilders.

Also in the pipeline is a new generation of AI ‘agents’ within Everglades, which Heron Financial said will further automate and optimise the whole adviser journey.

Coulson added: “We expect these developments to save hours in the adviser servicing journey, enabling our advisers to spend more time with clients and less time on admin. It’s about combining technology and human expertise to raise the bar for the whole industry.”

At the beginning of the year, Coulson discussed the firm’s green retrofit process in an interview with Mortgage Solutions.

Greetings from early September, when fall classes have begun.  Today I’d like to share information about one of my seminars as part of my long-running practice of being open about my teaching.

It’s in Georgetown University’s Learning, Design, and Technology graduate program. I’m team teaching it with LDT’s founding director, professor Eddie Maloney, who first designed and led the class last year, along with some great guest presenters.  The subject is the impact of AI on higher education.

Every week students dive into one topic, from pedagogy to criticism, changes in politics and economics to using LLMs to craft simulations.  During the semester students lead discussions and also teach the class about a particular AI technology.  Each student will also tackle Georgetown’s Applied Artificial Intelligence Microcredential.

Almost all of the readings are online.  We have two books scheduled: B. Mairéad Pratschke, Generative AI and Education and De Kai, Raising AI: An Essential Guide to Parenting Our Future.

Here’s the syllabus:

Introduction to AI in Higher Education:
Overview of AI, its history, and current applications in academia

Signing up for tech sessions (45 min max) (pair up) and discussion leading spots

Delving deeper into LLMs
Guest speakers: Molly Chehak and Ella Csarno.
Readings:

1. AI Tools in Society & Cognitive Offloading
2. MIT study, Your Brain on ChatGPT. (overview, since the study is 120 pages)
3. Allie K. Miller, Practical Guide to Prompting ChatGPT 5 Differently

Macro Impacts: Economics, Culture, Politics

A broad look at AI’s societal effects—on labor, governance, and policy—with a focus on emerging regulatory frameworks and debates about automation and democracy.

Readings:

Optional: Daron Acemoglu, “The Simple Macroeconomics of AI”

Institutional Responses

This week, we will also examine how colleges and universities are responding structurally to the rise of AI, from policy and pedagogy to strategic planning and public communication.

Reading:

How Colleges and Universities Are Grappling with AI

We consider AI’s influence on teaching and learning through institutional case studies and applied practices, with guest insights on faculty development and student experience.

Guest speaker: Eddie Watson on the AAC&U Institute on AI, Pedagogy, and the Curriculum.

Reading: 

Pedagogy, Conversations, and Anthropomorphism

Through simulations and classroom case studies, we examine the pedagogical potential and ethical complications of human-AI interaction, academic integrity, and AI as a “conversational partner.”

Readings:

AI and Ethics/Humanities

This session explores ethical and philosophical questions around AI development and deployment, drawing on work in the humanities, global ethics, and human-centered design.

Guest speaker: De Kai

Readings: selections from Raising AI: An Essential Guide to Parenting Our Future (TBD)

Critiques of AI

We engage critical perspectives on AI, focusing on algorithmic bias, epistemology, and the political economy of data, while challenging dominant narratives of inevitability and neutrality.

Readings:

Human-AI Learning

This week considers how humans and AI collaborate for learning, and what this partnership means for workforce development, education, and a sense of lifelong fulfillment.

Guest Speaker: Dewey Murdick

Readings: TBD

Agentic Possibilities

A close look at emerging AI systems and agents, with attention to autonomy, instructional design, and how educational tools are integrating generative AI features.

Reading

• Pratschke, Generative AI and Education, chapters 5-6

Future Possibilities

We explore visions for the future of AI in higher education, including utopian and dystopian framings, and ask how ethical leadership and equity might shape what comes next.

Readings:

One week with topic reserved for emerging issues

Topic, materials, and exercises to be determined by instructors and students.

Student final presentations

I’m very excited to be teaching it.

Related

In fintech, moving fast and scaling smart guidance is the name of the game. But speed without trust is a losing strategy.

Ask anyone who’s tried to choose a health plan, adjust a retirement contribution or manage their finances through an employer portal: Financial decisions today are complicated and confusing. In fact, recent research found that more than one-third of full-time employees avoid thinking about benefits and retirement entirely — not because they don’t care, but because the process feels overwhelming. 

That’s exactly where fintech has stepped in. AI-powered platforms have made it possible to democratize financial guidance, bringing tools once reserved for high-net-worth clients to everyday people. But as these systems grow from simply informing to autonomously guiding and acting on behalf of users, the stakes rise.

Opaque algorithms. Misaligned incentives. Trust gaps between what technology delivers and what users actually need. These aren’t abstract risks — they’re real-world consequences that shape lives, affecting everything from retirement security to household healthcare.

That’s why as fintech companies lean deeper into agentic AI, ethics can’t be an afterthought. Building systems that are human-centered and fiduciary-minded isn’t just a regulatory checkbox. It’s fundamental to ensuring scalable financial guidance serves the people it’s meant to help.

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From Information to Action: Why the Shift Matters

Early fintech platforms focused on providing users with better access to information: credit scores, budgeting tools, savings calculators. Today, we’ve moved into a new era where AI-driven platforms don’t just provide information; they automatically adjust contributions, rebalance portfolios and even select insurance or healthcare plans without direct user input.

This transition from passive tools to active guidance engines amplifies the importance of embedded ethics. An advisor’s fiduciary duty is well understood in the traditional financial services world. But as algorithms take on more of that advisory role, how do we ensure they’re acting in the user’s best interest?

The Risk Behind Fintech Convenience

At scale, small biases in AI models or misaligned business incentives can have big impacts. Financial technology companies often face pressure to keep user-facing products free, which means monetizing through third-party partnerships, affiliate revenue or product placement. While not inherently wrong, these structures can quietly nudge users toward outcomes that benefit the platform more than the individual.

It’s easy to imagine a situation where an AI engine suggests health plans or retirement options that prioritize profit rather than what’s best for the user. These aren’t hypothetical concerns. The complexity of financial products, combined with muddied recommendation logic, can erode user trust if people feel the system isn’t on their side.

Scaling Trust Alongside Technology

The antidote isn’t to slow down fintech innovation. The real opportunity is to scale trust alongside technology. That means embedding ethical principles into product design from the ground up, much like “shift-left” security in software development.

These principles include:

• Embedding fiduciary ethics directly into AI decision-making frameworks.
• Prioritizing user-first outcomes in algorithm design.
• Ensuring transparency and explainability in AI-driven recommendations.
• Structuring business models to align revenue with long-term user benefit, rather than short-term engagement.

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Human-Centered Design as an Ethical Foundation

At the core of ethical fintech lies human-centered design. Financial technology isn’t purely a numbers game. It’s about guiding people through complex, emotionally charged decisions that shape their health, wealth and future.

Human-centered design means starting with empathy and understanding that what’s mathematically optimal may not always be what’s emotionally reassuring or practically useful. For example, people navigating benefits enrollment might prioritize predictability over theoretical savings. Survey data supports this: Nearly 80 percent of employees say they’d be more engaged in benefits selection if they had year-round access to guidance. This suggests that people don’t just want automation. Instead, they want clarity and ongoing support. Good design respects those nuances.

It also means recognizing that trust isn’t just about the quality of recommendations. It’s also about how transparently they’re delivered. Users need to understand why the system is suggesting a particular option, especially in high-stakes areas like healthcare coverage or retirement planning.

Ethics as a Product Feature, Not a Footnote

One way to think about embedded fiduciary ethics is as a core product feature. Just as fintech platforms advertise speed, simplicity or personalization, they should also prioritize fairness, clarity and user-first outcomes as competitive advantages.

This is becoming a market expectation. Users increasingly understand that “free” services often come with hidden costs. Transparency about business models, meaning who pays and why, will be key to maintaining trust as AI-driven financial services scale.

Real-World Examples: What Good Looks Like

Examples of this shift toward ethics-driven design are emerging across the industry:

• Some benefits platforms now clearly explain why one health plan bundle is recommended over another, rather than simply presenting options.
• Investment tools increasingly highlight long-term risk and performance trade-offs, rather than pushing users toward higher-fee products.
• AI recommendation engines are being paired with explainability features, offering plain-language insights into what factors shaped a given suggestion.

And critically, some platforms are experimenting with agentic AI that comes with embedded user controls and override options, enhancing rather than removing autonomy.

These are small but meaningful steps in building a more ethical fintech ecosystem.

Preparing for the Future: Agentic AI and Beyond

Looking ahead, the industry faces new challenges and opportunities as fintech tools evolve. The rise of agentic AI means systems that don’t just offer choices but autonomously act on behalf of users.

Imagine a future where your retirement contribution automatically adjusts every month based on your spending patterns or where your benefits enrollment happens via natural language chat rather than a complex form. These innovations promise greater convenience and accessibility, but they also raise the stakes for ethical design.

When users delegate more control to AI, transparency, fairness, and human-centeredness become even more critical. That’s especially true as many workers are grappling with broader financial concerns: Recent data shows that 64 percent of respondents are worried about household financial resilience, and many report postponing major life milestones due to high out-of-pocket costs and confusing benefits decisions. Companies will need to invest in algorithmic explainability, clear user consent models and proactive monitoring to ensure outcomes remain aligned with user interests.

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The Path Forward for Fintech

Fintech has the power to transform financial services for the better. But as the tools we build become smarter and more autonomous, our responsibility grows. Scaling financial guidance without embedded ethics risks eroding the very trust fintech is meant to build.

The future belongs to platforms that combine technological sophistication with human-centered values, balancing automation with accountability, speed with clarity, and personalization with fairness.

Financial guidance at scale doesn’t just need to be smart. It needs to be ethical. That’s the challenge — and the opportunity — that lies ahead.