Why AI stalls in banks and financial services
A fraud, risk or fund operations agent only goes live when you can show your auditor three things. Where the data was processed, which policy applied and how each decision was made. Most projects stall because they copy customer and transaction data into a new platform and build pipelines nobody can explain at review.
Decisions nobody can prove
Risk and compliance teams only sign off on AI they can defend. That means traceable inputs, controlled execution and evidence ready for the supervisor.
No trace from input to decision
No evidence trail for DORA and the AI Act
Changes that skip change control
Data that cannot move
Core banking, payments and the fund register stay inside your perimeter. They will not be moved to a new platform on a vendor timeline, so the agent has to run where they already live.
New copies of customer data
Models that call out to a public cloud
Migration projects before any value
Pilots that die at review
The model is the easy part. Getting governed agents past security, risk and compliance and into production is the hard part, and that is what Lascaris solves.
One-off prompts instead of governed agents
Policy checked on paper, not at run time
Proofs of concept that never reach production
Built on an open, proven foundation
Our founding team created Apache Wayang, now an Apache Top-Level Project, and has held senior roles at Allianz, Cloudera, Confluent and McKinsey. Lascaris runs on the Kafka you already have, Apache Kafka or Confluent, so your agents work where your data lives and nothing sensitive leaves your perimeter.
