The Tech Trek

Victor Fang, CEO and Founder of Anchain AI, joins The Tech Trek for a timely conversation on crypto crime, AI driven fraud, and what financial institutions need to understand as digital assets move closer to the mainstream. This episode is worth your time if you care about cybersecurity, compliance, crypto risk, anti money laundering, or where agentic AI is starting to reshape investigation work.
This conversation goes beyond headlines. Victor breaks down how bad actors are using generative AI for phishing, identity fraud, exploit development, and ransomware, then explains how defenders are using AI, graph intelligence, and agent workflows to fight back. It is a sharp look at the collision of crypto, cybersecurity, regulation, and AI infrastructure.
In this episode
What crypto crime actually looks like today, from exchange hacks to romance scams and ransomware

Why crypto risk now extends well beyond crypto native users

How financial institutions, regulators, and compliance teams are adapting

Where AI is helping attackers move faster, and where it is giving defenders an edge

Why agentic workflows and MCP powered investigation tools could change this category fast

Timestamped highlights
00:00 Victor Fang on crypto crime, AI versus AI, and agentic AML

00:53 What Anchain AI does and why blockchain investigation is becoming more important

01:56 How generative AI is already being used in crypto crime and phishing

06:30 What banks, regulators, and AML teams need to understand about crypto adoption

10:44 Why Victor believes AI can give defenders the advantage

16:17 How Anchain uses blockchain data, graph intelligence, and agent workflows to investigate faster

22:04 Why the company’s MCP server could extend beyond crypto into KYC and financial applications

25:21 What the next wave of agent driven security and investigation might look like

One standout idea from the conversation, crypto is much closer to you than you think.
Practical takeaways
Crypto risk is no longer a niche issue, it is increasingly tied to broader fraud, ransomware, and financial crime

AI is accelerating both offense and defense, which raises the bar for security and compliance teams

Agentic investigation workflows could dramatically reduce manual work in AML, fraud, and cyber operations

Companies building in regulated spaces need infrastructure that can handle both speed and scrutiny

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Cam Crow, Director of Data and Analytics at Vacatia, joins The Tech Trek to unpack what happens when a startup outgrows informal ways of working. This episode looks at how data teams can introduce project management frameworks without killing speed, how to manage stakeholder demand as complexity rises, and why the right operating model matters even more as AI begins to reshape analytics work.

Cam shares a practical view from the middle of real growth, from startup scrappiness to acquisitions, migrations, and a much wider stakeholder base. He explains when process becomes necessary, how to build trust during that shift, and where AI is starting to change both delivery workflows and the future of business insights.

In this episode
• Why early stage teams should add process cautiously, not by default
• The moment speed and quality start breaking under too many competing requests
• How public communication and domain based stakeholder channels reduce friction
• Why planning routines matter as much for stakeholders as they do for the data team
• Where AI fits today, from faster delivery to semantic layers that support better answers

Highlights
00:00 Cam Crowe joins the show to discuss project management frameworks through the lens of data, startup growth, and stakeholder alignment
01:58 Why Cam resisted formal sprint planning in the startup phase and why that made sense at the time
05:58 The tipping point where too many priorities start hurting both velocity and quality
11:49 How moving conversations out of direct messages and into domain channels changed team operations
15:03 Inside the two week development cycle and the planning week that keeps stakeholders engaged
21:08 How Cam is thinking about AI, semantic layers, and the future of on demand analytics

A standout idea from this conversation, process should be added conservatively, only when the business truly needs it.

Practical takeaways
• Do not formalize too early, but do not wait until the system is already breaking
• Make prioritization visible once demand exceeds capacity
• Use shared channels instead of one to one communication to reduce bottlenecks
• Build stakeholder rituals into the operating model, not just team rituals
• Treat AI readiness as an infrastructure challenge, not just a tooling decision

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Deep Sogani, SVP and Group Data Management Officer at Datasite, joins The Tech Trek to unpack why data governance, lineage, and business process design have become mission critical in the age of AI. This conversation gets past the surface level AI hype and into the operational reality, how companies actually build trustworthy systems, where AI initiatives break down, and why strong data foundations now shape business outcomes in real time.
This episode explores the shift from downstream analytics to data that actively drives live decisions, workflows, and automation. Deep explains why many AI projects fail before the model even matters, how business architecture should lead technical design, and why human oversight still matters in high stakes environments.
In this episode
Why AI has made data governance and data lineage far more operational

Why business process clarity matters before data architecture or tooling decisions

How real time AI changes the demands on data quality and system design

Where agentic AI fits, from workflow automation to more advanced decision support

Why human judgment still matters in AI systems shaped by risk, ethics, and security

Timestamped highlights
01:47 Why AI raises the stakes for governance, lineage, and trust in data

04:57 Why business architecture has to lead before technical design

09:11 The progression from predictive models to agentic AI workflows

17:55 Why the human in the loop is still essential

21:16 What makes an AI project worth prioritizing

26:06 What has changed, and what has not, in AI related change management

Standout line
“Business architecture and business thinking should dictate the what and the why, and the data architecture is the how part which needs to follow.”
Practical takeaway
If you are evaluating AI inside the enterprise, do not start with the tool. Start with the business problem, the workflow, the decision risk, and the quality of the data behind it. Strong models on the wrong problem still fail.
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Suresh Martha, Head of Data Driven Innovation and Analytics at EMD Serono, joins The Tech Trek for a practical conversation on what leadership looks like when your team is asked to take on new technical capabilities. This episode is about extending team impact, evaluating new tools, building credibility with stakeholders, and leading through change without pretending to be the deepest expert in every domain.

For data leaders, analytics managers, technology executives, and operators, this conversation gets into the real work behind capability building. Suresh breaks down how to assess whether a new technology is worth pursuing, when to start with a pilot, how to upskill internal talent, and how to hire for skills your team does not yet have.

In this episode

• How to evaluate whether a new tool or technology actually adds business value
• Why small pilots help leaders build trust before asking for larger investment
• What it takes to lead technical work you have not personally done yourself
• How to hire for capabilities your team does not yet have
• Why business context and data knowledge still matter as much as technical depth

Timestamped highlights

00:04 Extending technical impact as a leader when new capabilities land on your team
03:37 A simple framework for evaluating new tools, investment, and fit
05:28 Hiring for skills your team does not yet have
07:44 Upskilling as a leader so you can guide the work with confidence
12:06 Managing experts whose technical depth goes beyond your own
15:21 Making room for learning and experimentation while still delivering

Standout line

As long as I understand the intricacies and can explain that, that is what matters, especially for a leader.

A practical takeaway

Start small. Pick a real business problem. Run a focused pilot. Measure the outcome. Earn the right to scale.

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Sourish Samanta, Director AI and ML at Advance Auto Parts, joins The Tech Trek for a grounded conversation on where machine learning still creates the most business value, where generative AI fits, and why many teams are chasing the wrong solution. This episode is worth your time if you want a clearer view of how serious operators think about AI strategy, product delivery, and practical use cases that can ship now.

This conversation cuts through the noise around AI and gets back to first principles. Sourish explains why machine learning remains the foundation behind today’s AI wave, how to choose between deterministic and creative systems, and what it actually takes to build production ready products that solve real business problems.

In this episode:

Why machine learning is still the core layer behind modern AI
When to use machine learning, when to use generative AI, and when simple analytics is enough
What a real product mindset looks like for AI and ML teams
How pod based teams can ship faster with better cross functional alignment
Why AI and ML talent need to spend time continuously reskilling

Timestamped highlights:

00:00 Why machine learning remains the foundation of today’s AI stack
01:57 The difference between ML teams, AI teams, and agent focused workflows
05:56 Choosing the right solve, from forecasting and inventory to creative content generation
10:09 The product mindset required to turn AI ideas into working systems
13:51 Why some business problems need analytics, not AI
15:52 Why AI teams need to spend part of their time learning, testing, and staying current

Standout line:
AI is not the strategy. Solving the right problem is.

Practical takeaway:
If you are leading an AI initiative, start by classifying the problem. If the outcome needs consistency, prediction, or forecasting, machine learning may be the better path. If the outcome needs creativity or flexible generation, generative AI may be a better fit. And in some cases, the best answer is still a clean dashboard and strong analytics.

Follow The Tech Trek for more conversations on AI, data, engineering, and how technology actually gets applied inside real businesses.