Financial Services

AI-assisted fraud narrative generation

A retail bank's fraud team reviews hundreds of flagged transactions daily, each requiring manual cross-referencing before writing a case narrative. Raw VSAM transaction records contain packed decimal amounts, internal offsets, and system codes irrelevant to the analyst. A REXX script flattens the VSAM structure and converts packed decimal values to readable form; DFSORT filters the output to the 12 fraud-relevant fields and caps record volume. Connect/AI passes each prepared record with a structured prompt to a cloud model, writing a plain-language case summary back to dataset for analyst review each morning.

Outcomes

• ● Case prep time reduced from 25 mins to under 5
• ● VSAM flattening handled by existing REXX skills
• ● DFSORT controls field selection and payload size
• ● Audit trail maintained entirely on z/OS

Insurance

Automated policy exception commentary

A life insurer runs nightly batch processing across millions of policies. When a policy hits an exception — lapse risk, beneficiary conflict, underwriting anomaly — it is written to a VSAM KSDS with a deeply nested, repeating-group structure. Compliance rules prohibit policyholder data leaving the on-premises environment. IDCAMS REPRO extracts the relevant exception records; a COBOL program restructures the variable-length records into a consistent flat layout, normalises date and currency fields, and strips fields not relevant to the exception type. Connect/AI targets a locally-hosted inference model, generating a plain-English explanation and suggested resolution path for each exception.

Outcomes

• ● Policyholder data never leaves on-premises
• ● COBOL normalisation reuses existing program skills
• ● Exception queue resolution time cut by 40%
• ● Compliance posture unchanged

Government

SMF-based capacity insight reporting

A government agency’s infrastructure team produces weekly capacity reports from SMF record types 30, 70, and 72 — covering CPU utilisation, DASD activity, and WLM metrics. Raw SMF data arrives as undecoded binary records, previously handled by a mix of SAS macros and manual effort. Standard SMF extraction tools decode the binary records to a workable sequential format; a REXX script then normalises field representations and aggregates readings to hourly intervals to reduce inference payload size, flagging records that breach capacity thresholds. Connect/AI passes the prepared dataset to a z/OS-hosted model requesting a trend summary and risk narrative, delivered by email to infrastructure management before the weekly review.

Outcomes

• ● SMF decode automated using standard extraction tools
• ● REXX aggregation replaces manual SAS macro work
• ● Capacity risks surfaced before the review meeting
• ● All processing sovereign — no data leaves z/OS

Mainframe Operations

Direct JES report inference for job failure triage

A mainframe operations team runs hundreds of batch jobs daily via JES2. When jobs fail or produce anomalies, operations staff manually scan spool output — JESMSGLG, JESJCL, SYSOUT — to identify root causes. With Connect/AI, the raw JES report is passed directly to the inference model, which understands fixed-width columnar formats, JCL syntax, and standard IBM message codes natively. No ETL, field extraction, or data preparation pipeline is required. The model reads the spool output as-is, identifies the failing step, interprets the relevant system messages, and drafts a triage summary routed to the operations team via Connect/Hybrid.

Outcomes

• ● No ETL or field-mapping pipeline required
• ● JES report format understood without pre-processing
• ● IBM system message codes interpreted in-context
• ● Faster mean time to triage on batch job failures
• ● No changes to existing JES2 job definitions

Logistics

Intelligent exception routing for supply chain events

A global logistics operator processes millions of shipment events daily through a z/OS core system, with exception records stored in DB2. Each record contains the full shipment history — hundreds of columns including rate tables, routing history, and carrier codes — far exceeding what an inference model needs. A DB2 extraction tool selects only current-day exceptions; DFSORT filters to the 15 impact-relevant fields and translates internal carrier and status codes to human-readable equivalents via a lookup. A two-step inference job follows: a lightweight local model classifies severity; for high-severity events only, a cloud model drafts a customer communication, routed via Connect/Hybrid into the CRM for agent review.

Outcomes

• ● DB2 extraction uses existing toolchain — no new skills
• ● DFSORT lookup handles code translation in-pipeline
• ● Cloud API costs limited to high-severity events only
• ● No mainframe application changes required

Performance Engineering

AI-driven Omegamon performance analysis and reporting

A z/OS performance engineering team monitors system health using IBM Omegamon — navigating multiple 3270 monitor screens daily to assess CPU, memory, I/O, and address space activity. Producing the weekly capacity and incident trend report requires manually visiting Omegamon screens, transcribing key metrics, and composing a narrative summary — typically a half-day task. A Python script automates the entire collection process via the Connect/3270 REST API; DFSORT or REXX step merges and sizes the multi-screen payload; Connect/AI then passes the prepared dataset to a cloud model requesting anomaly detection, trend commentary, and a recommended actions summary. The finished report is emailed to the performance team and uploaded via FTP to the infrastructure document store.

{
  "screen":   "OMEGAMON_CPU_ACTIVITY",
  "captured": "2026-05-01T06:00:12",
  "lpar":     "SYSPLEX1",
  "cpu": {
    "dispatch_pct": 87.4,
    "zlip_pct":     61.2,
    "wait_pct":     12.6
  },
  "top_consumers": [
    { "jobname": "BATCHPRC1", "cpu_pct": 23.1, "type": "BATCH" },
    { "jobname": "CICSREG2",  "cpu_pct": 18.7, "type": "CICS"  },
    { "jobname": "DBMAINT",   "cpu_pct": 11.3, "type": "BATCH" }
  ]
}

Outcomes

• ● Weekly report prep reduced from half a day to an overnight scheduled job
• ● Screen navigation fully automated — no engineer involvement in data collection
• ● AI identifies anomalies across all screens simultaneously
• ● Omegamon untouched — no configuration changes required
• ● Output delivered by email and FTP — no new tooling for consumers