Functional Analyst
A functional analyst translates business needs into precise system behavior, from fit-gap analysis to functional design. This pillar covers that craft in practice: turning a business requirement into a functional specification, running fit-gap analysis against what the system already does, mapping state machines and reason codes, and documenting behavior so nothing is left ambiguous for the build team. Written for analysts who own the spec, with examples from real banking and payments delivery.
What user acceptance testing is, how UAT differs from SIT and QA testing, who writes the scenarios, and the entry and exit criteria that make a UAT cycle real.
The ISO 20022 payment transaction statuses explained: RCVD, ACTC, ACCP, ACSP, ACSC, PDNG, RJCT, and more, with the lifecycle order and what each guarantees.
The ISO 20022 reason codes analysts meet daily: account codes (AC01, AC04, AC06), amount codes (AM04, AM05), agent and regulatory codes, with the action each implies.
CBPR+ ends fully unstructured addresses in November 2026. What structured and hybrid addresses are, the elements that matter, and how to migrate without rejections.
Which ISO 20022 message replaces each SWIFT MT: MT103 to pacs.008, MT202 to pacs.009, MT940 to camt.053, and the traps in the mapping. A reference table.
When money must come back, ISO 20022 gives three distinct mechanisms: returns, reversals, and recalls. Who initiates each, which message carries it, and how to model them.
A business analyst describes intended behavior; a technical BA verifies it against the running system. What separates the roles and how to cross the gap.
A functional analyst is accountable for exact system behavior; a product owner for what gets built and in what order. The split, and which seat fits you.
Medallion and Data Vault answer different questions: medallion says how many quality layers, Data Vault says how to model history inside them. With diagrams.
A QA analyst derives what must be tested from requirements and risk; a test engineer builds the automation that runs it at scale. The split and the overlap.
A self-assessment matrix of 25 skills across the five technical analyst hats, with three levels per skill and where to build each one. Original to this site.
How to write acceptance criteria for AI and LLM features when outputs are non-deterministic. Use bounds, properties, guardrails, and evaluation sets, not exact matches.
The difference between a Business Requirements Document and a Functional Requirements Document: what each covers, who reads it, and when you need both. With examples.
How to turn a vague business requirement into a precise functional specification: decompose intent, define inputs and outputs, and write testable behavior. With examples.
What a data dictionary is, how to build one, and why a single authoritative definition of every field prevents the ambiguity that breaks integrations and reports.
What a dead-letter queue is, why event-driven systems need one, and how an analyst specifies DLQ behavior: retries, routing, monitoring, and recovery. With examples.
How to use decision tables to specify complex business rules completely: conditions, actions, rule columns, and collapsing combinations. The tool that leaves no case undefined.
How to write requirements for event-driven systems: event schemas, ordering, idempotency, retries, and consistency. A practitioner guide with a Kafka example.
Field notes on going from intimidated to fluent with OpenAPI: what confused me, what clicked, and how reading the contract myself changed how I work as an analyst.
How to run a fit-gap analysis: compare requirements against system capability, classify each as fit, gap, or partial, and turn gaps into decisions. With a payments example.
A practical functional specification template: scope, actors, data, business rules, behavior, error handling, and acceptance criteria. The structure that makes a spec buildable.
Write API requirements the right way: endpoint, method, request and response schema, status codes, error contracts, and testable acceptance criteria. With examples.
An analyst's guide to HTTP status codes: the 2xx, 4xx, and 5xx families, what each common code means, and why 202 vs 200 matters in payments. Practical, not exhaustive.
What ISO 20022 actually is as an architecture: the business model, message definitions, structured data, and usage guidelines that shape modern payment systems.
How an analyst reads JSON: objects, arrays, nesting, and types. Understand API payloads, event messages, and config without asking a developer. Practical, not theory.
How to use MoSCoW prioritization to rank requirements: what each category means, how to apply it without everything becoming a Must, and how it drives scope decisions.
How to design negative tests systematically: boundary values, invalid inputs, state violations, and failure injection. The unhappy path is where the real defects live.
A field-by-field guide to writing pacs.008 test cases: mandatory fields, structured data, validation, reason codes, and the pacs.002 responses that prove each case.
How payment messages flow end to end in ISO 20022: pain.001 initiation, pacs.008 interbank, pacs.002 and pain.002 status, and camt reporting. A systems analyst map.
How an analyst reads an API contract: endpoints, methods, request and response schemas, status codes, and OpenAPI structure. Understand any API without asking a developer.
How to map payment reason codes to causes and customer messages: ISO 20022 codes like AC04, internal errors, and the mapping that prevents support incidents.
How to design reconciliation between systems: matching keys, break detection, tolerance, timing, and exception handling. The control that proves the money is right.
The regular expressions an analyst actually needs: matching patterns in logs, validating formats like IBAN and BIC, and searching data. Practical regex, not theory.
What a requirements traceability matrix is, how to build one, and why it proves every requirement is designed, built, and tested. With a payments example.
How business analysts use sequence diagrams to map a flow across services, expose integration gaps, and write better requirements. With a payments example.
How to model a payment as a state machine: define the states, the allowed transitions, the triggers, and the illegal moves. The tool that makes status behavior precise.
Synchronous and asynchronous communication differ in whether the caller waits, and that choice shapes coupling, latency, resilience, and the customer experience.
What a system context diagram is, how to draw one, and why starting at the boundary stops you scoping the wrong thing. With a payments example and the C4 model.
User stories capture intent; specifications capture exact behavior. Here is the real difference, when each fits, and why complex systems need both. With examples.
A functional analyst turns business intent into precise, testable system behavior. Here is what the role does in banking and payments, and how it differs from a BA.
A QA analyst proves a system behaves correctly by testing it end to end, not by reading the spec. Here is what the role does in banking and payments.
A systems analyst maps how services, messages, and data flow across a system so the pieces work as a whole. Here is what the role does in banking and payments.
A technical business analyst analyzes, codes, tests, and supports. Here is what the role actually does day to day in banking and payments, and how to become one.
Field notes on an AI project where the acceptance criteria did not work: why exact-match criteria break on non-deterministic output, and what we replaced them with.
PAIN vs pacs explained: why pain.001 is not pacs.008, how pain.002 and pacs.002 mirror it, where camt fits, and how ISO 20022 splits customer and bank.
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