AI inside the credit workflow, not beside it.
Commercial lending runs on documents, thresholds and deadlines. Catalyze builds the systems that read the documents, test the thresholds continuously and escalate with the evidence attached.
The operating reality
The back office is where the margin went.
Commercial lending is a document business wearing a data business's clothing. Covenants live in credit agreements, borrower performance arrives as PDFs, and the controls that matter are tested on a quarterly calendar by people who are expensive and scarce. The work is not hard because it is complex; it is hard because it is manual, continuous and unforgiving of a missed date.
- Covenant terms buried in agreements that were negotiated, not templated
- Borrower financials arriving by email and re-keyed into spreadsheets
- Thresholds tested per loan on a quarterly cycle, against data already weeks old
- Breaches surfacing after the options for remediation have narrowed
- Portfolio-level exposure reassembled by hand for every reporting cycle
- Regulatory reporting that depends on the same manual chain
Where we work
The problems an operational AI system can actually take on in a commercial bank.
01
Covenant monitoring
Terms extracted from agreements, modelled with their thresholds and triggers, and tested continuously rather than quarterly.
02
Loan document intelligence
Statements, collateral reports and compliance certificates read on arrival, with the figures that matter pulled into structured form.
03
Credit operations
Intake, exception handling and borrower follow-up run as a workflow instead of an inbox.
04
Portfolio surveillance
Exposure and covenant health at portfolio level, produced from the system of record rather than reassembled per report.
05
Regulatory reporting
Reports generated from governed definitions, so the number in the filing and the number on the dashboard came from the same place.
06
Customer-facing service
Relationship teams given the position before the call rather than after it.
Architecture
What the system looks like in a bank
The general architecture, instantiated against lending systems and lending vocabulary.
- 01
Systems of record
Core banking · loan servicing · document stores
Where the obligations and the evidence already live. The system integrates with them rather than asking the bank to migrate.
- 02
Documents
Credit agreements · statements · collateral reports
The unstructured material that carries the actual terms. This is the layer conventional automation cannot read, and the reason the work stayed manual.
- 03
Semantic layer
Covenant taxonomy · thresholds · breach definitions
Affirmative, negative and financial covenants defined once, with what constitutes a breach agreed before anything tests for one.
- 04
Model layer
Extraction · term identification · anomaly detection
Models that turn an agreement into structured obligations and spot the movement that precedes a breach.
- 05
Rules and validation
Threshold tests · confidence gates
The threshold test itself is deterministic. A ratio against a limit does not need a language model and should never be given to one.
- 06
Workflow
Trigger rules · escalation routing · borrower notification
Where a detected breach becomes a routed case with the evidence attached and a named owner.
- 07
Human review
Credit authority · exceptions
The decisions that require credit judgement, reaching the right authority with the analysis already done.
- 08
Outcome
Detection lead time · cost per loan · capacity
Earlier detection widens the remediation options; time returned moves from monitoring to origination.
Evidence
What we have built here
Commercial lending is the sector with the most Catalyze work behind it.
Capabilities that apply
What we would bring to financial services.
Related insights
Our thinking on this.
Questions
What buyers ask us about financial services.
How can banks use AI in commercial lending?
The highest-value applications are document-heavy and continuous: extracting covenant terms from credit agreements, reading borrower financials on arrival, testing thresholds against current data rather than on a quarterly cycle, and routing breaches to credit authority with the supporting evidence already assembled. The pattern is the same — replace a periodic manual review with a monitored workflow.
Is AI reliable enough for covenant testing?
The test itself should not be done by a model. In a well-designed system a language model reads the agreement and extracts the terms; the comparison of a ratio against a threshold is deterministic code. Models handle the unstructured input, rules handle the arithmetic, and anything below a confidence threshold goes to a person. That division is what makes the result defensible to a control function.
What about data residency and model privacy in a bank?
A routing architecture lets most work happen inside your boundary — private open-weight models for classification and extraction, deterministic rules for validation, and a frontier model only where the task genuinely requires one. This reduces how much leaves the perimeter and makes that decision explicit rather than incidental. It is an architectural choice, not a security guarantee.
Do you replace the core banking system?
No. Operational AI sits behind the systems of record and writes back to them. Replacing a core is a different and far riskier programme, and it is almost never what the operational problem actually requires.
Tell us about the operation. We'll tell you whether we have built something like it.