Helping you make sense of going Cashless | Best-selling author of "Cashless" and "Innovation Lab Excellence" | Consultant | Speaker | Top media source on China's CBDC, the digital yuan | China AI and tech

As a best-selling author and consultant, I combine my passion for fintech, AI, and innovation with my extensive experience in the banking industry and the Asian market. I wrote two No. 1 international bestsellers, "Cashless: China's Digital Currency Revolution" and "Innovation Lab Excellence," that provide insights, strategies, and practical solutions for the digital transformation of the financial sector. I have also been a top media source and speaker on China's digital yuan and mobile payments, sharing my firsthand knowledge and analysis of the world's most advanced cashless society.

With over 25 years of experience in risk management, structured credit, and debt financing, I deeply understand fintech and AI's challenges and opportunities for the banking industry. I have worked with start-ups and established insurance and asset management players, helping them navigate the uncharted waters of the latest cognitive, AI, blockchain, and analytic technologies. I have also led and contributed to multiple innovation labs and projects across Asia, applying the latest IBM technologies to real-world problems our banking clients face. I am a tireless evangelist for using technology to democratize access to financial markets and to enhance customer experience and value.

Richard Turrin

Stablecoins’ $33T transaction volume grabs headlines, but the true battle isn’t volume — it’s who controls institutional flows, which is why banking licenses have become the new battleground in the stablecoin economy.

The new bank rush includes stablecoin issuers, asset managers, and even a Japanese multinational, all of whom are striving to crack the stablecoin economy by providing institutional access through a bank.

Despite the massive volume hype, stablecoins still lack a clear path to broad institutional adoption. 

Becoming a chartered bank offers a practical solution — normalizing their use by providing the oversight, trust, and infrastructure corporations expect. 

Consider how unlikely it is for large companies to run payroll, supplier payments, or treasury operations on tokens issued by lightly regulated entities and third-party exchanges.  

Becoming a regulated bank changes that equation.

👉Key trends:

↳ In the US, at least 15 applications have been submitted for banking charters overseen by the Office of the Comptroller of the Currency (OCC), 

↳ 11 of the 15 are from stablecoin issuers. 

↳ Payment companies like Stripe, Bridge, Ripple, and Connectia Trust NA, backed by Sony Financial Group.  

↳ Crypto native firms like Coinbase Global Inc. and National Digital Trust are both seeking banks.  

↳ Full-service bank Erebor and even the new US neobank, Brazil’s NuBank

↳ Master account access to Fedwire, ACH, and FedNow would transform issuance, redemption, and on-chain settlement

For incumbent banks, which are, for the most part, lagging with stablecoin adoption, the shift presents both a threat and an opportunity: 

↳ Threat: regulated stablecoin issuers could disintermediate thin-margin wholesale and cross-border payments

↳ Opportunity: partner or replicate to embed stablecoins in cash management, custody, and treasury services 

What is key is that the prize is not retail wallets but control over institutional payment flows.

The race is on. Who will win control of the massive flows associated with asset managers, broker-dealers, and market makers? 

♻️Repost to share this great read on the stablecoin bank rush!
#Fintech, #AI, and Tech at the speed of Asia and China.

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The Race Is On to Build a Stablecoin Bank and Cash-in!

Fintech and AI from a uniquely Asian and China-centric perspective | Best-selling author of “Cashless” and “Innovation Lab Excellence” | Consultant | Speaker | Top media source on China’s digital yuan |

Weekend Read:  Tokenized Money Explained: What You Need to Know in 2026 – Deposit tokens, CBDC, Stablecoins & More

This is a fabulous Weekend Read for fans of tokenized money and tokenization! 

While all of the document is a solid read, here are the chapters you simply can't miss:

🔹 Chapter 1  The Taxonomy
Learn how the different forms of digital money are classified based on: 

• the nature of the claim;
• the backing;
• the form in which it is represented on a ledger;
• and the access conditions guiding who can hold
and transfer it.

🔹Chapter 3  Interoperability and Programmability
Read this to learn why these are so important to EVERY form of digital money, and the 5 key challenges to scalability: 

• Cross-border efficiency
• Cross-platform connectivity
• Cross-asset connectivity
• Regulatory alignment
• Governance coordination

Read this, and you will be ready for the big changes coming to how we pay in 2026! 


♻️Repost to share this fabulous read on Tokenized Money! 
#Fintech, #AI, and Tech at the speed of Asia and China.

Tokenized Money: What you need to know! 

Harvard Business Review drops a bombshell by claiming that AI isn’t reducing anyone’s workload, but is actually intensifying it and increasing burnout.

The results of an eight-month study at a tech company with 200 employees may at first sound like every manager’s dream: 

“We found that employees worked at a faster pace, took on a broader scope of tasks, and extended work into more hours of the day, often without being asked to do so.”  

...and it gets better.... 

“Workers did more because AI made ‘doing more’ feel possible, accessible, and in many cases intrinsically rewarding.”

The problem for workers is burnout:

“Once the excitement of experimenting fades, workers can find that their workload has quietly grown and feel stretched from juggling everything that’s suddenly on their plate."

"That workload creep can in turn lead to cognitive fatigue, burnout, and weakened decision-making.”

The study found that companies will need to adopt an AI practice:

"a set of intentional norms and routines that structure how AI is used, when it is appropriate to stop, and how work should and should not expand in response to newfound capability."

Three creators of AI intensity:

↳ Task Expansion

AI automates routine work, but instead of reducing workload it expands the scope — leaving people to handle more complex, judgment-heavy tasks and exceptions at scale.

↳ Blurred Boundaries

AI enables constant availability and real-time monitoring, blurring the line between work and personal time — making it harder to disconnect and increasing burnout risk.

↳ Multitasking

AI speeds up task-switching and creates more simultaneous workflows, raising cognitive load — leading to fragmented attention, reduced deep focus, and higher error rates.


This isn’t to say that humans and AI won’t reach the critical balance allowing humans to work comfortably with AI.  

They most certainly will, but it will only come if companies have policies that help foster this balance.  

Companies need to be proactive in adopting an AI practice for employee well-being, just as they do with adopting AI  risk management and governance programs.

Expecting this balance to happen magically by itself is a fundamental error. 

Or will companies just accept employee burnout and make humans sacrificial?

♻️Repost to share how AI increases work intensity
#Fintech, #AI, and Tech at the speed of Asia and China.

Bombshell:  AI doesn't reduce work but fosters burnout

Harvard Business Review’s survey of 623 senior execs reveals the surreal state of hype for agentic AI, as a stunning 84% believe Agentic AI will transform their organization’s business.  

Yet backing this belief is the harsh reality that:

↳ Only 26% say that their organization is very effective at leveraging ANY type of AI 

↳ 66% say their organizations aren’t even using agentic AI, as they are in pilot or a lower level of activity. 

So execs are making big predictions on agentic AI’s transformative power with little success using AI and no direct experience. 

But then, as though we are entering a painting by Salvador Dali, most can’t even define what success is for their agentic systems: 

↳ Only 5% of respondents say that their organization has well-defined success metrics for agentic AI implementation

↳ 19% say their organization has some success metrics in place. 

↳ 55% say their organization is defining or planning such metrics 

↳ 17% say their organization has not considered success metrics 

So, exactly what is a successful agentic AI when most have no clue as to the metrics to measure success?! 

But wait, as if this isn’t surreal enough: 

↳ 72% fully expect that agentic AI will deliver a return on investment, but wait for it….

↳ 46% don’t even have an agentic AI roadmap or strategy!

You can’t make this stuff up! 

That execs are positive about agentic AI isn’t bad, and one may argue that they are making predictions about its future use.  

That may be true, but execs should be a bit more skeptical. 

"Transforming a business" is a high bar, and the gap between their reality and aspiration is too great.

If this isn’t a surrealist Salvador Dali painting, what is? 

♻️Repost to share how hyped agentic AI has become!
#Fintech, #AI, and Tech at the speed of Asia and China.

Hopium: The Huge Gap Between AI Hype and Reality 

🔥Weekend Read: McKinsey's "The State of Organizations 2026," should have been titled: "The AI 'Jobpocalypse' Begins and Block Shows Why."

McKinsey's nine strategic "shifts" are a great read, and yes, all will transform organizations. 

The problem is that McKinsey makes them all sound happy, when many will be very hard on employees! 

As Block's layoff of 40% of staff shows, the real "state of organizations in 2026" will not be as employee-friendly as McKinsey suggests.

↳ Where McKinsey happily sees companies "reaching the next productivity frontier," Block's Jack Dorsey confirms that this frontier doesn't need as many humans: 

“We’re already seeing that the intelligence tools we’re creating and using, paired with smaller and flatter teams, are enabling a new way of working which fundamentally changes what it means to build and run a company.”

↳ While McKinsey sees humans and AI agents working together and collaborating, this isn't what happened at Block. 

40% of staff were let go, and none were deemed worthy of agent collaboration? Nearly half the company won't be happily collaborating with agents. 

↳ McKinsey wants companies to "Aim higher with a new performance edge."

Block's CEO, Jack Dorsey, agrees and says other companies will get the same "edge" soon: 

"I think most companies are late. Within the next year, I believe the majority of companies will reach the same conclusion and make similar structural changes.” 

McKinsey's 9 shifts transforming organizations all sound happy, but read between the lines for the outlook on jobs: 

 1. Unlocking the Al-enabled organization

2. Humans and Al agents: Building a new world of collaboration

3. Leveraging Al to rewrite the future of shared services

4. Finding value in a new geopolitical context

5. From structure to flow: Reaching the next productivity frontier

6. Focusing on the core: Doing the right thing with more intensity

7. Aiming higher with a new performance edge

8. Sharpening the focus on diversity and inclusion

9. Reinventing leadership: Leading from the inside out

The State of Organizations in 2026 may be fine; look how Block's stock went up on Friday. 

The state of employment, however, is very different, and I don't think employees or society is fully prepared. 

♻️Repost this great read that predicts the jobpocalypse.
hashtag#Fintech, hashtag#AI, and Tech at the speed of Asia and China.

The AI ‘Jobpocalypse’ Begins and Block Shows Why.

Neobanks are the new kings of banking. With almost 40% of the new banking relationships worldwide, 52% satisfied with their bank, and 72% of consumers now having two or more banks.

A few years ago, there was a serious debate about whether neobanks were just a passing trend or here to stay.

These numbers don’t lie with big neobank gains worldwide:

↳ 39% of new banking relationships
↳ 19% of all bank accounts
↳ 5% of revenue share

While the 5% revenue share figure above may raise eyebrows, don’t expect this low figure to last. 

There are clear signals of increased profitability, with revenue per customer jumping to USD 100 per account per year, up from USD 75 just 24 months ago.

Critics were correct in that initially, there were far too many neobanks, and the overall number of neobanks has declined.  

But the global leaders that remain are a real threat with rates and low fees that incumbents can’t match.

↳ Nubank + Revolut added 50+ million users in the past two years alone (the world's fastest growing banks)
↳ 20+ neobanks now exceed 10 million customers; ~100 have passed 1 million

The few areas where incumbents still lead are trust, fraud protection, and customer support. 

Trust was always neobank’s “Achilles heel,” but even this gap is shrinking. 

I’ll use myself as an example. I went from four primary incumbent banking relationships to seven, with three neobanks added in recent years. 

Yes, the incumbents have more of my money, but that is changing by the month. 

Can incumbents keep up?  

Don’t count on it.  

Hampered by legacy tech and mentality, they will have a hard time besting fast-moving neobanks with new products, gamification, low-fee monetization, and leveraging AI. 

This doesn’t mean incumbents are doomed, as they have deep societal connections that will keep them afloat for years.  

That said, we can expect them to have dwindling relevance, particularly to clients in their 30’s (or older ones like me!) who are comfortable with digital relationships. 

The new banking king?

Neobanks and their rise is unstoppable.

Who’s winning the banking battle for your money right now?

♻️Repost to share this great read on neobanks! 
#Fintech, #AI, and Tech at the speed of Asia and China

Neobanks the new kings of banking

Stanford’s Emerging Technology Review is out — and it’s a must-read “Top 10 List” for anyone tracking the tech reshaping our world.

For fintech followers: dive into the AI, Quantum, and Cryptography sections — these directly impact payments, security, and the future of digital finance.

For those wanting a break from fintech, the Neuroscience and Materials Science chapters offer a refreshing, eye-opening shift to completely different frontiers.

The report aims to secure US tech leadership, but don't let that scare you away.

It is a clear, insightful guide to tomorrow's breakthroughs, and is relevant no matter what country you live in or your focus.

Download it here and see what’s coming next. 

What section caught your eye?

👉The Ten Technologies:

01. Artificial Intelligence
02. Biotechnology and Synthetic Biology
03. Cryptography and Computer Security
04. Energy Technologies 
05. Materials Science
06. Neuroscience
07. Quantum Technologies
08. Robotics
09. Semiconductors
10. Space

Thank you to Walter Sepp Aigner for sharing this PDF with me!  

Got a fresh PDF report that you think our community would like?   
Send it over, I'm always on the lookout for good reads to share! 

⛷️Holiday post!  Back next week with longer reads. 
♻️Repost and share these future tech advances. 
#Fintech, #AI, and Tech at the speed of Asia and China.

uninterrupted innovation is key - even if too many hope to ignore innovation - I facilitate hands-on innovativeness with leading-edge champions in Europe as innovation facilitator and curator of (non-)knowledge inflows

The Top 10 Tech Emerging Technologies

The IMF has a MUST-READ report covering Southeast Asia’s (SEA’s) QR-based digital payment revolution that is transforming cross-border payments, promoting local currency usage, and boosting trade and inclusion.  

Humble QR payments, born in China back in 2014, are now changing all of SEA and especially Thailand, a local leader in digital payments, including CBDC.  

The IMF reports domestic QR payments are exploding throughout the region: 

↳ +300% growth in Thailand, 
↳ +550% in Malaysia, 
↳ +467% in the Philippines, all for 2023-24.

But what is happening now is even bolder as QR payments have just begun to go cross-border to reduce cost and time.

To do this, Asia is connecting QR payment systems to allow bilateral real-time transfers in a collaborative effort between governments and local private payment providers.  

The drive for these connections comes from tourism, with intra-ASEAN tourism accounting for 42% of all visits, and the region’s financial backbone, SME businesses.

SMEs benefit from cross-border QR payments in two ways: 

↳ Expansion of their market reach into new countries. 

↳ Establishment of a digital payment footprint that can be used to assess creditworthiness and bring greater inclusion.

Another key point is that these cross-border transactions use local currency and avoid the risk of using US dollars and currency shocks. 

A fact that USD stablecoin fans would do well to take note of. 

The IMF shows QR payments increase with: 

↳ Higher volatility of the local currency against the US dollar. 

↳ Substitution for traditional banking and credit card payments when they are difficult to access. 

↳ Increased tourism receipts.

↳ High cost and slow speed of traditional cross-border bank transfers. 

↳ The number of banks participating in the linkages.

Given the fame of SEA scam centers, it's no surprise that QR cross-border payments are not immune. 

Still, what we are seeing throughout the region is, as the IMF says, a real revolution!  

Now, without being too smug, aren’t you all impressed that I talked about this in my book Cashless 4 years ago?! 

♻️Repost to share SE Asias QR revolution! 
#Fintech, #AI, and Tech at the speed of Asia and China.

SE Asia's QR Digital Payment Revolution

Banks are stuck in AI “pilot purgatory,” and to escape, the author Oscilar proposes an “imitation is flattery” strategy that copies AI use cases that work elsewhere and runs them in parallel with legacy systems.

Copying the success of others isn’t a novel strategy, but in this case, it is very practical because banks don’t have time to waste.

It also saves banks from ripping and replacing legacy systems. 

Some see this as the best solution to AI pilot purgatory, but it will only make banks fall even further behind. 

Systems replacement also doesn’t solve other problems that cause the gap between AI enthusiasm and action:

↳ Long procurement procedures, 

↳ Lack of real-time continuous AI governance, 

↳ The inability to surrender control of their business to AI vendors/providers.   

The strategy is for banks to simply “wedge” AI infrastructure alongside legacy systems without requiring wholesale replacement.

The authors, of course, give this a fancy name.

They call it a “tractable wedge,” which is an “execution pattern observed in live deployments that allows banks to progress toward the AI Bank of the Future incrementally.”

Sounds like copying to me.  

Proposed Wedges:

↳ Shadow mode:  Run a new AI decisioning engine in parallel with legacy

↳ Analyst-assist agents: agents to perform the data gathering and preliminary analysis.

↳ Greenfield products: Build an AI-native risk stack specifically for new product lines. 

↳ Natural language to rule generation: Allow non-technical users to create and test rules. 

↳ AML False Positive Reduction: Deploy an AI “overlay” that performs secondary scoring

Some may critique this strategy as not using AI to its fullest as with a system rip and replace. 

They are correct, but Oscilar estimates that these installations can capture 70-80% of AI’s potential value.

That is more than good enough if copying can actually deliver AI into actual production!  

The problem isn't optimizing AI, but not falling behind.

Banks should jump on this paper.

This is a down-and-dirty AI strategy for laggard banks that makes perfect sense.

(Even if it's just copying with a new name.)  


♻️Repost to share this practical AI strategy: copy others!
#Fintech, #AI, and Tech at the speed of Asia and China.

Banks Need To Get Out of "Pilot Pugatory!"

The AI commoditization crisis is here — this powerful new read explains why, with almost every company using the same AI tools, true differentiation is slipping away unless they build in "intelligence capital."

The author Simon Torrance is on the front lines implementing AI Agents with his company AI Risk, and I received his permission to reprint this article.

How many customer chatbots have you used lately? 

They felt novel a few years ago. Now they’re table stakes — no real edge, no differentiation.

Why? 

Banks and finserv firms rely on the same three or four LLMs, the same consultants with McKinsey and Accenture, and have the same use cases. 

The outcome: AI that automates, but is dull and can’t create a competitive edge.

Enterprise leaders should be asking: 

“Is my AI investment building something a competitor cannot replicate by simply buying the same tech six months later?”

Simon answers that the answer is: No, unless they shift from AI that doesn’t just automate but accumulates. 

The goal of AI isn’t just faster processes; it’s "Intelligence Capital." 

↳ Intelligence Capital: institutional knowledge that compounds with every decision, persists beyond individuals, and can’t be copied by purchasing the next vendor release.

↳ AI agents that provide intelligence capital provide true differentiation as “your competitors can’t build them.”

↳ To generate intelligence capital with AI, we need to use agentic teams that deliberate over data to turn it into a decision.  

Make no mistake, this is advanced agentic use at a time when many think chatbots are cool.  

It is intelligence capital that will differentiate companies' AI usage and provide real value. 

The interesting part is that this use of agents mirrors a human committee rather than an assembly line.  

It also opens the door to using AI digital twins of employees to access their decision-making abilities.

In a world where AI assistants are already a commodity, the winners won’t be the ones who automate fastest.

They’ll be the ones who accumulate Intelligence Capital that no one else can replicate.

Thanks again to Simon Torrance for sharing this article!

♻️Repost to share how Agents can provide a real edge!
#Fintech, #AI, and Tech at the speed of Asia and China.

Expert on Strategy & Innovation | Agentic AI | CEO, AI Risk | Founder, Embedded Finance & Insurance Strategies | Guest lecturer, Singularity University | Keynote speaker

AI Risk

AI is Now a Commodity:  Build a Real Edge

Weekend Read: AI’s Impact on Jobs: What the Data Actually Shows (Spoiler Alert: It’s Not What You Think)

A fresh study cuts through the AI job-loss panic fueled by headlines like Block’s 40% staff cuts.

The real numbers paint a more nuanced picture.

AI disruption is coming, but it’s not hitting hard yet, at least not as fast as the fear-mongering suggests.

Key takeaways:

↳ Limited evidence so far that AI is driving broader unemployment — top 25% most exposed workers show similar unemployment trends to those with zero exposure

↳ Limited evidence that AI is playing a role in the broader labor market today.

↳ Limited evidence that AI has increased unemployment to date.

↳ Hiring of younger workers (22–25) in high-exposure roles is down 14% — they may be staying in existing jobs, shifting roles, or returning to school.  25+ shows no impact.

↳ Actual AI usage is far below theoretical capability. The “observed exposure” (what people really do with tools like Claude) is much lower than models can handle

↳ Top-exposed workers earn 47% more on average, have higher education, and skew white-collar

↳ Demographics of the most exposed: Female (+16%), White (+11%), Married (+10%), Asian (+4%)

↳ Highest exposure roles: Programmers (75%, observed exposure), Customer Service Reps (70%), Data Entry Keyers (67%)

↳ Lowest exposure: Physical/manual jobs like Cooks, Mechanics, Lifeguards, Bartenders, Dishwashers (~30% of workers have zero observed exposure)

There is no doubt that AI is reshaping work, but it’s not the mass job-killer just yet.

With low "observed exposures," however, we're just at the start of the disruption. 

It’s hitting higher-paid, white-collar roles first, while physical jobs remain largely untouched.

-Are you seeing AI change roles around you? 

-Are the claims of AI job loss, like Block's, a way of claiming dubious AI advances rather than admitting poor performance?

♻️Repost to share the latest on AI's impact on employment
#Fintech, #AI, and Tech at the speed of Asia and China.

AI job losses aren't hitting just yet....but they will

Money20/20 Asia’s annual survey confirms what I’ve been saying for years: Asia is the world’s leading fintech capital, driving global innovation with unmatched momentum.

Key highlights from the report:

↳ 90.6% of executives say financial inclusion and social good are now embedded in core business strategy — not vague CSR, but a commercial imperative for long-term success

↳ 22.9% identify SEA as their primary growth target, solidifying its role as the global growth engine, beating all other global regions.

↳ 61.2% of organizations have already adopted AI or machine learning at scale

↳ 63.5% rank fraud prevention as their top operational priority

↳ 72.9% see SME-tailored fintech solutions as critical to regional economic growth

↳ 66% see hyperlocalisation as key for launching across new Asian markets.   APAC is definitely not homogeneous! 

The region is shifting from experimentation to full-scale deployment across AI, digital assets, QR payments, superapps, cross-border linkages, tokenization, and stablecoins.

And unlike the West, regulators are actively backing disruption, not protecting incumbents.

No technology is off-limits in the pursuit of inclusion and real GDP growth.

Asia isn’t copying the West, it’s redefining the future of finance.

♻️Repost this important read on fintech in APAC! 
#Fintech, #AI, and Tech at the speed of Asia and China.

SEA Fintech: "A Primary Driver of Global Innovation"

Identity fraud is a global $58B problem, and this year’s top trend is that it is changing from “low-effort” high-volume schemes to “sophisticated fraud” using deepfakes and social engineering, up 180% YoY.  

The days of an email asking you to unlock your account sent from a hopelessly wrong email address are gone, and for that, we owe thanks to AI.  

But the AI knife cuts both ways. 

“Sophisticated fraud” is an attack that combines multiple coordinated techniques such as AI-generated identities, social engineering, device or telemetry tampering, and cross-channel manipulation. 

Unlike phishing or other more simple attempts, these multi-step schemes require AI, planning, automation, and adaptability, making them far harder to detect and contain.

They are also larger, with a longer-term impact for victims and institutions, as they require greater preparation and higher costs.

The top trends for this year:

↳ The Sophistication Shift: Less sloppy and low effort fraud, but more advanced attempts.

↳ AI industrializes Fraud: AI is evolving into a sophisticated fraud production ecosystem.

↳ AI Fraud Agents: Autonomous systems that combine generative content, scripting, and behavioral mimicry to execute full verification attempts end-to-end.

↳ Telemetry tampering becomes the new evasion: fraudsters are increasingly targeting the data pipelines themselves rather than the identity artifacts alone.  API attacks!

In banking and finserv synthetic identities are hot, making static online onboarding problematic and increasing the need for behavioral biometrics. 

Synthetic identities can slowly build credibility before executing large cash-outs and chargeback abuse. 

And since regular readers might ask: What about APAC? 

APAC has the dubious distinction of leading, as it is one of the most active fraud environments in the world. 

AI is at the core of both committing and defending against fraud; which do you think will prevail in the end?

👉How to read:  Don't be afraid of the long length.  Read the first 30 pages 

♻️Repost to share this great read on Identity Fraud
#Fintech, #AI, and Tech at the speed of Asia and China.

Identity Fraud's $58B Nightmare Continues as AI fights AI!

Plaid's fintech trends for 2026 are a great read, and wake up call as four out of ten are fraud-related.

This shouldn't come as a surprise when the US alone lost $12.3 billion to fraud in 2023, with GenAI potentially pushing losses up to $40 billion by 2027. 

Unless the industry gets better control of fraud, which is exploding due to GenAI, the losses will be simply unsustainable. 

The irony, of course, is that GenAI is used by both fraudsters and financial services, with fraudsters having the edge on innovation.  

Of course, AI ranks the No 1 in the list due to its use in fintech apps, where chat will become the "default front door for consumer finance."

Still, the sad reality is that while everyone is excited about AI's impact on the front door, the back door is wide open. 

Meanwhile, concrete anti-fraud measures like digital ID don't get a reference in the paper.   

We are left hoping that finserv can use GenAI to control fraud at least as well as fraudsters.     

Now that should be a wake-up call. 

👉Plaid's Top 10 for 2026

Trend #1: Fintech apps are becoming a financial co-pilot 
Trend #2: Fraudsters are getting smarter 
Trend #3: Fraud prevention has become a team sport 
Trend #4: Alternative payment types reaching mainstream acceptance 
Trend #5: Lenders are moving beyond all-purpose credit scores 
Trend #6: Open banking is a baseline expectation 
Trend #7: Al shifts from novelty to consumer necessity 
Trend #8: Neobanks are launching on stablecoin rails 
Trend #9: Al is the front line in the fight against fraud 
Trend #10: Fraud moves upstream in the transaction process

⛷️Short holiday post!  
♻️Repost to share this honest read with others
#Fintech, #AI, and Tech at the speed of Asia and China.

Fintech Predications 2026:  Fraud Dominates the List

With the NYSE and the NASDAQ creating new markets for tokenized stocks, we'll all soon be deciding whether we want to buy our shares "on-chain" or through traditional methods. 

But buyer beware, not all tokenized stocks are created equal.

Tokenization promises direct, verifiable ownership, faster settlement, lower reconciliation risk, and 24/7 asset movement.  

These are compelling and very real efficiency gains that we should all look forward to!

Still, to profit from tokenization, we all need to understand the three types of tokenized stocks in the market:

• Direct Tokenization: A method that directly tokenizes stock ownership (e.g., Securitize, Superstate). This corresponds to Issuer - Sponsored Tokenized Securities in the SEC’s classification framework.

• Entitlement Tokenization: A method that tokenizes stock-related rights within existing infrastructure (e.g., DTCC). This corresponds to Tokenized Security Entitlement in the
SEC’s classification framework.

• Indirect Tokenization: A method where a company buys and holds stocks on behalf of users and tokenizes the beneficial interest in them (e.g., Robinhood, Backed, Ondo, Dinari). This corresponds to Security - Based Swap in the SEC’s classification framework.

Synthetic/indirect models offer exposure but often strip shareholder rights, introduce counterparty risk, and create fragmented liquidity (platform-dependent, not interoperable).

Are synthetic or "indirect tokenizations" bad?  

NO!  

They can be a great way for investors to gain access to stocks, but know before you buy what form you're getting. 

Remember, liquidity is everything when you want to sell, and it is not interoperable across different indirect tokenization platforms!

How to read: Go to page 18 and read the three mechanisms currently available, and then page 30 for an analysis of the major tokenization platforms!  

Are you excited about tokenized stocks and looking forward to your first purchase or sticking with traditional shares for now?

♻️Repost this as a public service to help others understand the "buyer beware" nature of tokenized stocks.
#Fintech, #AI, and Tech at the speed of Asia and China.

Tokenized Stocks Are Here – But Not All Are Created Equal

Citi's "Ten Global Insights for Leaders" Is Required Reading for Everyone — Not Just Leaders

Citi’s latest report is a must-read for anyone trying to navigate our chaotic world, not just the C-suite. 

Calling it “for leaders” feels a bit tone-deaf — these insights matter to all of us.

While AI and stablecoins unsurprisingly top the list, Citi also spotlights a rising Asia as a defining force:

Asia Pacific Economic Cooperation (APEC) countries now account for 61% of global GDP and 60%+ of FDI (up from 55% and 40% two decades ago)

Chapter 5 on geopolitics is essential — and not just because bombs and missiles are now sadly flying. 

Every major tech and fintech trend, stablecoins, payments, and AI now carries a geopolitical layer we can’t ignore.  

AI above all is full of geopolitical landmines: chips, data sovereignty, open-source LLMs, and more.

This is a great macro overview that isn't just for "leaders" but for everyone trying to make sense of a chaotic world.  

The four interconnected themes:

1️⃣  AI transformation:  
While Al is starting to drive productivity gains, it is also creating new fraud risks, as deepfake incidents surged 47x since 2022.

2️⃣  Geopolitical realignment: 
Rising geopolitical tensions are redirecting capital flows, with family offices citing trade disputes as their top concern, and defense spending now exceeding development aid by 2:1.

3️⃣  The evolution of financial markets: 
Financial infrastructure is undergoing radical change through private market democratization, 24/7 settlement cycles and European market harmonization.

4️⃣  The impact of digital assets: 
Stablecoins and bank tokens are transitioning from
crypto novelty to core financial infrastructure, with the
market projected to reach $1.9 trillion by 2030.


👉The Ten Themes/Chapters:
1.  Al: The Information Era's Apex Technology
2. Al in Investment Management: Beyond Efficiency Gains
3. Al Deepfakes: When Seeing and Hearing Can't Be Trusted
4. Private Markets: Access, Innovation and New Pathways to Progress
5. Global Family Offices Navigate a Changing World Order
6. Public Sector Perspectives: Financing National Security Priorities
7. APEC: The New Connections Driving Trade
8. The Future of Post-Trade: Custody and Settlement in an Always-On World
9. Reimagining European Capital Markets: From Fragmentation to Harmonization
10. Stablecoins 2030: Web3 to Wall Street


⛷️ This is a short holiday post!
♻️Repost to these Ten Insights everyone needs now! 
#Fintech, #AI, and Tech at the speed of Asia and China.

10 Global insights NOT just for "leaders" but EVERYONE

The most comprehensive survey yet of AI use reveals the disparity between expectations and reality, with 70% using AI and 80% report no impact on company productivity or employment.

AI is everywhere, with billions invested in implementing it with the goal of increasing worker productivity and cutting employment costs. 

The National Bureau of Economic Research (NBER) surveyed 6000 executives in the US, UK, Germany, and Australia to show that while AI benefits may be coming soon, for now, they are still nowhere to be seen.

The survey found:

↳ “First, around 70% of firms actively use AI, particularly younger, more productive firms.

↳ “Second, while over two-thirds of top executives regularly use AI, their average use is only 1.5 hours a week, with one-quarter reporting no AI use.

↳ 🔥“Third, firms report little impact of AI over the last three years, with over 80% of firms reporting no impact on either employment or productivity.

↳ “Fourth, firms predict sizable impacts over the next three years, forecasting [that] AI will boost productivity by 1.4%, increase output by 0.8% and cut employment by 0.7%.”

↳ This contrasts with individual employees, who predicted “a 0.5% increase in employment in the next three years as a result of AI.” ‼️

There remains a huge gap between executive use in AI, with 25% of executives not using AI at all!

A third of executives surveyed reported using AI in the workplace, but the usage amounted to only 1.5 hours per week on average. 

So we have a system where executives who have little direct experience using AI are being asked to manage AI rollouts. 

If that isn’t a recipe for failure, what is? 

Another factor is also at play, and that is Solow’s productivity paradox. 

Economist Robert Solow famously observed in 1987: “You can see the computer age everywhere but in the productivity statistics.” 

He was highlighting how massive investments in IT failed to deliver measurable economic gains despite widespread adoption due to "information overload." 

It is likely we are facing a similar delay in realizing AI productivity gains. 

What the survey shows is that AI is now the “new normal” in business and that while productivity gains may be delayed, they are likely coming. 

The big question is, will they result in the predicted 1.75 million fewer jobs by 2028 at these firms?


♻️Repost to share this important survey on AI productivity
#Fintech, #AI, and Tech at the speed of Asia and China.

80% of Companies Report No Productivity Gain from AI

Kudos to the DTCC, Clearstream, Euroclear, and BCG for working on bringing DLT interoperability to securities because, until they, or the NYSE, set the standards, market fragmentation is a real risk.

My first article on DLT securities interoperability dates back to April 2024, and I’m frustrated because we still haven’t tackled DLT interoperability. 

All the hype around tokenized/digital securities — 24/7 trading, lower costs, instant settlement — remains just that: hype.

This is so basic it’s shocking that no one has tackled interoperability at scale. 

Interoperability in capital markets can be defined as “the ability to exchange assets across ledgers – DLT and traditional – while preserving the asset’s integrity, ownership rights and lifecycle, with full legal and regulatory compliance.”

In other words: “same asset, same rights, same outcome” across both rails. 

One possibility, and I don’t think this is far-fetched, is that the NYSE and NASDAQ tokenization programs will end up defining/driving interoperability based on their standards. 

But here is the real problem: as ad-hoc DLT adoption surges in capital markets, NYSE and NASDAQ efforts included, there is a real risk of market fragmentation. 

Fragmentation traps assets in silos, inflating operational costs, fragmenting liquidity, and may lead to inefficiencies that are worse than traditional markets. 

So, how to move the market forward?  Brace for it, it's not rocket science:

1. Data standardization – creating common identifiers, taxonomies, and message standards, as financial markets did in the past with ISIN.

2. Process harmonization – ensuring minimum definition of core processes such as settlement, reconciliation, and corporate actions, replicating the efficiency gains achieved in traditional finance through SWIFT standardized protocols.

3. Consistent roles assignment – clearly defining responsibilities for critical functions (for example, custody, validation, oversight), as traditional finance achieved with custodians, CSDs, and clearing houses, to preserve accountability and trust.

Three years on from my first piece, we’re still at square one on scaling this.

Does this latest white paper give you confidence that DLT securities/tokenization are truly around the corner... or is it still mostly talk?

♻️Repost if you want interoperable securities tokens not hype.
#Fintech, #AI, and Tech at the speed of Asia and China.

Lack of DLT Interoperability Threatens Tokenization

FATF’s latest report lays it bare: P2P stablecoin transactions on unhosted wallets are now the go-to method for illicit finance.

↳ Criminal use of stablecoins now represent 84% of the USD $154B illicit virtual asset transaction volume in 2025.

↳ Stablecoins now beat Bitcoin as the dominant crypto asset for crooks.

↳ P2P stablecoins transactions on unhosted wallets are a feature, not a bug 

When the US decided to use stablecoins to digitize the dollar, it left a major loophole that criminals just love: P2P transactions. 

P2P stablecoin transactions through "unhosted wallets" are no different than passing a suitcase full of cash, as they occur directly between individuals or entities.

They are untraceable as they don't pass through a regulated intermediary Virtual Asset Service Provider (VASP) or financial institution. 

P2P transfers put AML responsibility on those ill-equipped to do it:
 
-Stablecoin issuers, who can't be bothered by small amounts.
-Intermediary VASPs, often in foreign domiciles, who don't care.
-Financial institutions, which are challenged to find the origin of the funds or don't want to know.

That AML is hard on stablecoins is a given, but it becomes nearly impossible with P2P where:  

-Stablecoins are used cross-chain to hide the transaction trail.
-Money mule networks are pressed into service. 

Now, if you think the FATF has a solution to this problem, you're dead wrong. 

↳ The FATF recommends only that nations put AML/CFT obligations on issuers, VASPS and banks.   

As we see above, that is simply not practical, but a scant few countries are making limited progress:

-Denying licenses to VASPs that allow self-hosted wallet transfers. (FATF doesn't say which country)
- Limiting customer transfer to first parties (Singapore)
- Enhanced due diligence. (Germany, Switzerland)
- Blockchain forensics (Germany, France, and Singapore)
 

↳ Next, the FATF recommends that nations require stablecoin issuers to be able to burn, freeze or withdraw stablecoins from secondary markets.

Stablecoin Tether is issued in El Salvador. How often will El Salvador request that it burn or freeze a coin, or how would they know how it is being used?  (Note: Tether complies when the US Treasury asks.) 

Stablecoins are incredibly convenient… and criminals know it.

Are we building the next-gen payment rail — or the next-gen laundering tool?

How to read:   Go to pages 11-18 for a good overview. 

♻️Repost to share the downside of stablecoins 
#Fintech, #AI, and Tech at the speed of Asia and China.

Unhosted wallets are like passing suitcases of cash.

HBR: AI Promised Less Work – Instead It Intensifies Workloads and Fuels Burnout...  Happy Year of the Horse!

As we gallop into the Year of the Horse, I want to take a moment to thank each of you for opening your inbox to me and your incredible support over the past year!

Today’s newsletter starts with a stunning study from Harvard Business Review showing that AI creates more work and potentially increases employee burnout! 

Well, isn’t that a surprise?  

Remember when the PC and spreadsheet promised to end the drudgery of ledgers and numbers?  

Excel has just become a new form of drudgery, and AI will likely have the same end result. 

We then dive into two pieces showing Asia leading the world in AI and stablecoin adoption.

In a brilliant strategy, most of Asia is profiting from the US-China AI rivalry by playing both sides off each other and, in doing so, gets the best of both ecosystems. 

Then, in the first-ever global survey of stablecoin users, Asia and Africa turn up on top as both can cut fees charged by banks for transfers by 40+%.   

While this sounds great, what of the societal costs of lost taxes and inequality? 

GenAI is powering today’s most dangerous fraud attacks, with fraudsters currently out-innovating banks.

But the report we cover shows how GenAI and agentic AI can give banks a real fighting chance.   

Now for the “chart of the day”, where, in the spirit of the New Year, we look at how, in a global survey on national optimism, Asian nations take the top five spots!

Finally, today’s “Bonus Read” is a masterpiece covering tokenized cash without the hype.  

I can’t recommend it highly enough!

♻️Repost to share this great read on AI, Asia and Stablecoins!
#Fintech, #AI, and Tech at the speed of Asia and China.

Thanks for reading the “Cashless” newsletter, an insider’s view on Asia’s fintech, CBDC, and AI for anyone striving to understand “the Asia Century.” 

HBR: AI Promised Less Work – Instead It Intensifies Workloads and Fuels Burnout

Fintech | Embedded Finance | E-commerce

I am a fintech enthusiast and product leader passionate about crafting simple solutions for complex problems. I have successfully launched several products in the payments and fintech space, consistently driving growth for early-stage startups and enhancing customer satisfaction for established ventures. Additionally, I run the Fintech Wrap Up newsletter, sharing the latest industry news and insights, and serve as a pro bono peer mentor in product management, demonstrating my commitment to giving back to the community.

Sam Boboev

Scaling AI in Banks Is a Governance Problem, Not a Data Problem

A successful AI transformation balances two things at once: near-term financial impact and long-term capability building. Miss either, and the effort stalls.
____

Here’s what actually works.

AI transformation does not start with a platform. It starts with a business strategy where AI is explicit, not implied. Domains and subdomains are selected based on value density, not technical curiosity. Execution then happens at scale, with reuse designed in from day one.

One large bank illustrates this well. Instead of shipping isolated models, it built reusable assets and an end-to-end analytics pipeline powering 50+ machine-learning models. Those components now support more than 150 use cases across the bank. Early impact: ~10% projected revenue uplift from hyper-personalization, lead optimization, and cross-sell. Same core assets. Different domains. Compounding returns.
____

This is the pattern most teams miss.

Transform one subdomain deeply. Move from MVP to industrialized use cases. Extract reusable components. Then plug them into adjacent subdomains. Build the AI stack in phases. Never all at once.
____

The real bottlenecks are not technical.

Teams fail when they optimize for models instead of value. High-performing banks run cross-functional squads where banking expertise, data, AI, and risk sit together from day one. Risk is embedded, not bolted on later. Adoption is designed, not assumed.

Sustained value requires central coordination. The strongest banks run an AI control tower with three clear mandates:

• Own the enterprise AI strategy and continuously reallocate capital based on realized value

• Standardize architecture, tools, and risk protocols while allowing selective federation

• Enforce reuse so assets built once are deployed everywhere

AI transformation is not a collection of experiments. It is a production system for decision-making, distribution, and growth.

That distinction is what separates pilots from platforms.

👉 Subscribe for more insights https://lnkd.in/d94JgWBU

Insights by McKinsey

Founder & CEO at Fintech Wrap Up | Payments | Wallets | AI

Payment Tokenization Explained

If you’re handling payments in 2026, understanding tokenization is a must.

Here's what caught my attention: 62% of merchants and 92% of financial institutions already use tokenization. But many teams still aren't clear on how it works or why it matters for their business.
____

What is Payment Tokenization?

Think of it as a security swap. Instead of storing actual credit card numbers (like 4532-1234-5678-3511), you store a random token (like 4532-8716-5413-2416). That token links to the real payment details stored in a secure, PCI-compliant vault.

When you process a transaction, you send the token. Your payment provider swaps it for the real card details behind the scenes. Simple concept, massive implications.
____

Why It Matters

-> Security: If you're breached, hackers get worthless tokens, not card numbers. The token can't be reverse-engineered.

-> PCI Scope Reduction: Tokens can reduce your PCI compliance scope by up to 90%. Less data = less liability.

-> Faster Checkouts: Returning customers do not need to re-enter their payment details. The friction disappears.

-> Multi-Processor Freedom: With the right tokenization strategy, you're not locked into a single payment provider.
____

The Three Types of Tokens

This is where it gets interesting:

1. PSP Tokens: Issued by your payment service provider. Great for getting started, but they lock you to that provider.

2. Network Tokens: Created by card networks (Visa, Mastercard, Amex). They boost authorization rates and reduce interchange fees, but require network-specific integrations.

3. Merchant Owned or Universal Tokens: Provider-agnostic tokens that work across all your processors and channels. Maximum flexibility, zero vendor lock-in.

Most sophisticated merchants combine universal and network tokens strategically based on their infrastructure and goals.
____

Whether you're scaling globally, managing subscriptions, or trying to reduce fraud, tokenization is foundational infrastructure. The question isn't whether to implement it, but how to do it right for your specific use case.

The payments landscape has shifted from single-processor setups to multi-processor strategies. Independent tokenization is what makes that transition possible without creating a data management nightmare.

👉 Subscribe for more insights https://lnkd.in/d94JgWBU

#paymenttechnology #fintech #tokenization

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