Guest Name: Frank McKenna, Brad Joffe
Today I want to talk about scam-baiting AI and what it looks like when fraud fighters start using automation against the people who have been using it against everyone else for years.
Because that is really the shift here.
Not just detecting scams after the fact.
Actively engaging them, slowing them down, and extracting intelligence while scammers think they are talking to a real person.
In this episode of Fraudology, I’m joined by Frank McKenna and Brad Joffe, CCO of Apate AI, to talk about a very different kind of fraud prevention.
We dig into AI bots against scammers, voice bots for scam detection, text bots for fraud intelligence, and how thousands of AI-powered agents can waste scammers’ time while gathering useful intelligence like phone numbers, crypto wallets, and bank account details.
And this matters.
Because scam-baiting AI is not just a clever idea. It is part of a much bigger move toward proactive scam prevention, conversational AI for fraud fighting, and AI-assisted anti-scam strategy that puts pressure back on scam operations instead of leaving all the burden on victims and frontline teams.
Here is what that fraud lens means in practice:
- I explain how scam-baiting AI shifts fraud defense from passive detection to active disruption
- AI bots against scammers can gather valuable intelligence while reducing scammer productivity
- Voice bots for scam detection and text bots for fraud intelligence help expose scam infrastructure
- Proactive scam prevention becomes stronger when teams can waste scammers’ time and learn from the interaction
What you’ll hear in this episode:
- Why scam-baiting AI is becoming part of the new war on fraud
- How AI bots against scammers use voice and text conversations to frustrate scam operations
- What fraud intelligence gathering tools can uncover, including scammer phone number intelligence, crypto wallet scam tracking, and bank account scam intelligence
- How AI scam defense for banks is being used to protect customers before losses happen
- Why conversational AI for fraud fighting could become a key part of real-time scam engagement and scam call interception technology
You should listen to this episode if you:
- Work in fraud, trust and safety, banking, or investigations and want to understand scam-baiting AI
- Are exploring scam prevention with AI and want practical examples of AI-powered scam disruption
- Need insight into voice AI for scam response, text bots for fraud intelligence, and proactive scam prevention
- Want to understand how to stop social engineering scams by mapping scammer infrastructure
- Care about AI-assisted anti-scam strategy and the future of fraud intelligence gathering tools
If you liked this episode, be sure to subscribe and review the podcast on iTunes, Spotify, YouTube, or wherever you listen to podcasts. It really helps with getting the word out.
Episode notes & key takeaways
Scam-baiting AI is turning fraud prevention into active disruption
Let me break this down.
For a long time, scam prevention has mostly been reactive.
Warn the customer.
Flag the transaction.
Investigate the complaint.
Maybe recover funds if the timing works out.
Scam-baiting AI introduces a different model. Instead of waiting for the scam to mature, fraud teams can engage scammers directly, keep them occupied, and gather intelligence while the scammer wastes time on a fake target instead of a real victim.
That is the core idea.
Brad explains how Apate AI uses what he calls an anti-scam bot army made up of voice and text bots that can hold realistic conversations with scammers at scale.
The goal is not just to annoy them, although that part is admittedly satisfying.
The real goal is to extract usable intelligence while reducing the time and attention scammers can spend targeting real people.
- Scam-baiting AI helps waste scammers’ time and reduce their operational efficiency
- AI-powered scam disruption gives fraud teams a more proactive role in scam defense
- Real-time scam engagement can generate intelligence before victims lose money
- Conversational AI for fraud fighting changes how teams think about intervention
Voice bots and text bots can gather intelligence during scam conversations
One of the most interesting parts of this conversation is how these bots actually operate.
Frank, Brad, and I talk about voice bots for scam detection and text bots for fraud intelligence, including examples of bots that sound realistic enough to keep scammers engaged over extended conversations.
That matters because scammers reveal a lot when they believe the interaction is working.
Phone numbers.
Crypto wallets.
Bank account details.
Scripts.
Escalation patterns.
All of that information contributes to scammer infrastructure mapping, and it becomes significantly more valuable when it is gathered systematically rather than through isolated reports.
At first glance, this might sound like a novelty. It is not.
Fraud intelligence gathering tools become far more powerful when they can collect and structure interaction data at scale.
- Voice bots for scam detection can maintain realistic conversations with scammers
- Text bots for fraud intelligence reveal repeatable scam signals and infrastructure details
- Scammer phone number intelligence, crypto wallet scam tracking, and bank account scam intelligence support investigations
- Scammer infrastructure mapping improves when engagement data is consistently captured
AI scam defense for banks is shifting prevention upstream
The episode also explores how financial institutions like Commonwealth Bank are experimenting with this type of technology.
That signals something larger than a single vendor use case.
AI scam defense for banks is becoming part of a broader shift toward proactive scam prevention.
Banks often encounter scams at the final stage, when the victim is about to move money.
By that point the social engineering has already happened, and the fraud team is trying to stop a transaction that the customer may strongly believe is legitimate.
Scam-baiting AI offers another approach.
Slow the scammer down.
Gather intelligence.
Learn from the interaction.
That intelligence can help institutions intervene earlier in the scam lifecycle.
- AI scam defense for banks helps shift detection earlier in the scam journey
- Proactive scam prevention improves when institutions gather intelligence upstream
- Stopping social engineering scams requires earlier signals than payment monitoring alone
- AI-assisted anti-scam strategy strengthens investigator context and customer protection
The real value is asymmetry against scam operations
Here is what is really happening.
Scammers have been operating with scale, automation, and efficiency for years.
They automate outreach, reuse scripts, and optimize for volume.
Scam-baiting AI introduces asymmetry.
Instead of only defending against scams, fraud teams can now force scammers to spend time on interactions that produce no payout while defenders collect intelligence.
That changes the economics.
Voice AI for scam response and scam call interception technology become tools that apply pressure to scam operations rather than simply reacting to them.
This does not eliminate scams. But it makes them less efficient.
And in fraud prevention, making abuse less profitable is usually a step in the right direction.
- Scam-baiting AI reduces the efficiency of scam operations
- Voice AI for scam response can intercept and study scam behavior
- Scam call interception technology adds friction for attackers instead of customers
- AI bots against scammers create a more strategic anti-fraud posture
The bigger theme in this episode is that scam-baiting AI gives fraud teams a more active role in fighting social engineering at scale.
Between voice bots, text bots, intelligence gathering, and proactive engagement, this approach introduces a new defensive layer that sits upstream of the payment itself.
That is the real shift.
Instead of only cleaning up scam damage, fraud teams can begin shaping the battlefield before the loss occurs.
And given where scam volumes are heading, that is something worth paying attention to.
