Last updated: September 17, 2026
Financial institutions are handling record transaction volumes, but online threats are rising just as fast. Fraudsters who used to send phishing emails now lean on automation, synthetic identities, and deepfakes. These newer tricks break the old detect-and-react approach and push banks toward systems that learn and act in real time.
Commenting for TechBullion, Vittesh Sahni makes the case that results come from choosing the right model, not the newest one. He describes in detail real fraud scenarios institutions face today and maps each to the AI methods that address them in the research report Future of Finance: How AI Is Advancing Fraud Detection in Banking and Financial Services.
Vittesh discusses with TechBullion the following:
- How to weigh latency, explainability, complex data, compliance, and customer experience before deploying
How to weigh latency, explainability, complex data, compliance, and customer experience before deploying
- Which model maps to which fraud
Which model maps to which fraud
- When unsupervised models and autoencoders catch fraud with no history and insider activity
When unsupervised models and autoencoders catch fraud with no history and insider activity
- Why the five-phase detection lifecycle keeps humans in the loop by design
Why the five-phase detection lifecycle keeps humans in the loop by design
No single model wins
The institutions that come out ahead treat model choice as a business decision tied to fraud type and regulation, then keep human insight on the high-stakes calls.
Read the full story by Angela Scott-Briggs at TechBullion.
