Regulatory Reporting Builder: Requirements and Software Selection

Why a separate builder for regulatory reporting?

Regulatory reporting breaks not where the "report looks nice", but where you need to meet deadlines and prove the origin of every figure. While there are few forms, this is often handled with Excel and manual exports. But when data sources multiply and forms change several times a year, the manual approach starts to fail.

Typically this shows up like this: deadlines are missed because data collection depends on several teams and manual reconciliations; sources "drift" (today a field comes from one system, tomorrow from another, and it’s hard to explain); form versions and calculation rules get confused, especially when edits happen on the last night; corrections aren’t tracked, and a month later no one remembers who changed a formula and why.

Excel templates are OK as a temporary fix, but they don’t scale with growing requirements. Any new regulator check becomes extra sheets, macros and comments. The risk of errors grows, and the cost of control increases: more time is spent explaining and finding discrepancies than preparing the report.

Compliance and audit teams are usually worried not about a "cell error" but systemic risks: lack of traceability, unrecorded changes, inability to repeat a calculation for the same date, and gaps between the approved methodology and how the report was actually assembled.

RegTech can help automate collection, checks and publication of forms, but without process discipline it won’t save you. Clear rules are needed: who owns each metric, where the methodology is stored, how changes are approved, and how re-runs are done.

A regulatory reporting builder differs from BI because it’s about "submit and prove", not "view and analyze." So the emphasis is usually on version control for forms and calculations, change control and action logging, reproducibility of calculations and exports to required formats.

Typical requirements from financial and public organizations

Requirements almost always come from several directions at once. The regulator defines forms and rules. Internal control adds checks and approvals. External auditors demand evidence: where numbers came from and who changed what.

Even a single small report usually has strict deadlines. Some reports are due daily, others monthly or quarterly. Deadlines are often tied to the regulator’s calendar and don’t account for your internal delays, so readiness statuses, fast rebuilds and a predictable approval process matter.

A common pain point is reference data. Counterparties, products, accounts, divisions, budget classification codes, industry classifiers: if they differ between systems the report starts to "float." Therefore a reporting builder should almost always be able to centrally store and version reference data, or at least strictly validate it before submission.

What is usually checked before sending

Before submission there is almost always basic data quality control: completeness (required fields filled), consistency (totals reconcile, related forms don’t conflict), no duplicates, correct periods and slices (date, currency, branch), and reproducibility of calculations (the same inputs yield the same numbers).

And finally, changes are inevitable: forms are updated, new fields appear, rules are clarified, new slices are added. The common requirement from all sides is one: changes must be managed and leave a clear history so the team can quickly explain why a number this month differs from the previous month.

Data and transformations: what the process must handle

Regulatory reporting is almost never assembled from a single source. Even within one organization required fields may live in the core banking system, ERP, HR system, procurement, data warehouse and in manually maintained spreadsheets. The process must tolerate heterogeneous extracts, different reference data and varying data quality.

The first bottleneck is normalization. If dates are DD.MM.YYYY in one place and ISO in another, currencies are in local units somewhere and in foreign units elsewhere, and counterparty identifiers change between systems, the final report becomes random. A good builder should enforce unified conversion rules and store them as part of the process, not as one-off fixes.

Next come transformations: calculations, aggregation, grouping, exclusions, allocations by codes and items. It’s important that rules are readable and repeatable: not just "calculate the total" but show what it’s composed of.

Before export you need validations. In practice the minimum set includes logical relationships between fields (if a flag exists, details must be present), balance checks (totals and subtotals reconcile), threshold and anomaly controls (sudden spikes, negative values), reference data and code verification, and completeness checks (all divisions and periods included).

Finally, formats. In one case the regulator accepts a tabular file, in another XML or XBRL is required, and for signing and internal approval a printable form is requested. The process should support multiple outputs from the same set of calculations.

Example: a public organization prepares a report on procurement and headcount. Amounts come from ERP, contract status from procurement, headcount from HR. If normalization and transformation rules aren’t fixed, any change in a directory or a new expense item can silently change numbers and break reconciliations.

Change control and audit: critical functions

Regulatory reporting rarely fails because of a single formula. More often the problem is that no one remembers who changed a rule, on which data the file was assembled, and why numbers "yesterday and today" don’t match. So forms are important, but so is change discipline.

The first thing auditors and infosec teams usually look at is roles and permissions. Methodology setup, technical assembly, approval and submission should be separated among different people or at least different roles. If one user can edit rules, trigger recalculations and submit the report, any mistake looks like a single-point risk.

The second block is environment separation. A normal scheme: development for changes, test for validation on a copy of data, and production for submissions. Production should not allow "hot fixes." Even if a form must change urgently, the edit should go through versioning and approval.

Critical control functions typically include:

• a role model (who configures, who approves, who signs, who submits);
• versioning of forms and rules (what changed, under which request and with what justification);
• an action log (who changed reference data, calculation parameters, field mappings, validations);
• version comparison to see differences without manual searching;
• prohibition of editing final numbers without trace or explicit marking of manual adjustments.

A separate topic is the "submission package." This is an archive you can restore months later to reproduce the result. It typically contains the form and rule versions, a data snapshot (or links to the exact extracts), validation logs, approval statuses and electronic signatures.

Example: the regulator issues a clarification three days before the deadline. The team makes a change in development, runs test checks, gets approval, and only then deploys the new version to production. If a later inquiry asks "why values changed", the log will show the specific change, author and date, and the submission package will let you repeat the calculation exactly.

What to look for in a reporting builder: basic feature set

A good regulatory reporting builder is valuable not for "pretty forms" but because it reduces error risk and makes the process repeatable. Before choosing, check where forms and rules live: in a built-in designer or a separate module. Built-in designers are usually easier for users and faster for changes. A modular approach is useful if your organization already has a strong data platform and you need a report layer only.

The system should support templates and reuse. In practice dozens of reports share common blocks: reference data, calculations, groupings, signatures, control ratios. If those are copied manually, a requirement change forces edits everywhere and something will be forgotten.

Another topic is validations. Validators should provide not just yes/no but a clear error log: what is wrong, where exactly, where the value came from and how to fix it. This is especially important when submitting in XBRL or similar structures: a schema error message doesn’t help meet the deadline.

Below is the basic set without which a builder quickly becomes a manual conveyor:

• a form and rule designer where structure and control relationships can be changed without development;
• a library of templates and common calculations so one fix updates all related reports;
• a validator with an error log and user-friendly explanations;
• a scheduler: timelines, deadlines, and automatic re-runs after fixes;
• export and archiving: a single package for review, approval and long-term storage of the submitted version.

Also ensure that export and archiving cover the full lifecycle: what was sent, who approved it and which data versions were used. For financial and public organizations this often matters more than speed of assembly.

How to choose software for your reports: non-marketing criteria

Choose a builder not by a feature showcase but by the forms you actually submit and how often they change. Start with an inventory: 10–15 key reports, their formats, deadlines, owners and data sources. This quickly shows what is worth paying for and what is unnecessary.

Check formats and integrations you actually need

If you don’t use XBRL, don’t buy an expensive module just because it’s "included." The same for rare formats: sometimes it’s easier to export CSV/Excel and then build the package than to bring in a heavy platform.

Next you hit integrations. Understand what suits you: ready connectors or custom integrations to your databases and accounting systems. What matters is not the promise "we’ll connect everything" but timelines and responsibility for maintaining integrations after source updates.

Here are criteria to include in requirements and test in a pilot:

• support for the regulatory formats you need (only those used) and schema validation;
• traceability: from the final figure back to the source record and calculation rule;
• versioning of rules and forms, version comparison and easy change review;
• change testing: runs on a copy of data, discrepancy checks and regression reports;
• operability: who and how will change rules without developers and without risking other reports.

Year-ahead support matters more than a one-hour demo

Decide in advance who will own the rules: business, IT or a dedicated reporting team. If after a year all changes still go through one developer, you’ll be back to manual mode.

Ask the vendor three direct questions before deciding:

• how long does it take to release a form change and who does it on the client side;
• what the audit trail looks like: who changed a rule, when and what changed;
• how integrations are updated and what happens when a source system version changes.

If you work with an integrator, contract not only implementation but regular support for regulatory changes so the process does not depend on specific people.

Step-by-step plan to implement a reporting builder

Implement the builder as a change-management project: first tidy up reports and data, then lock down rules, and only after that scale.

Steps 1–3: understand what you submit and how it is calculated

Start with an inventory. Often dozens of forms live in email and on personal drives.

Create a single report registry: forms, deadlines, regulator, owner, signatory, where history is stored. For each report describe data sources (systems and views) and assign field-level owners for data quality, not just system owners. Record calculation and validation rules: formulas, rounding, exclusions, control relationships, what counts as an error and who fixes it.

Steps 4–6: set up the process, pilot and formalize regulations

Next make the process repeatable so it doesn’t depend on specific people.

Set up roles and approvals: preparation, review, approval, submission. Enable an action log so changes are visible. Pilot on 1–2 reports that are painful: frequent form edits, many manual reconciliations. Measure period close time and number of reworks.

After the pilot move to production: approve regulations, schedule form updates and tests, and define response procedures for requirement changes. If reporting and data are hosted in your environment, check compute capacity and support in advance.

Common mistakes and traps when automating reporting

Automation fails more because of team habits than formats. A tool can be good, but the surrounding process may not.

The most common problem is calculation rules living in emails and heads of key people. While one specialist is on leave another does things "as they remember" and numbers start to drift. The same goes for exceptions: one-off corrections become permanent but never enter the unified description.

Second trap is mixing development and submission. When someone "tweaks a formula a bit" in the live report before a deadline without versioning or comments, you lose the ability to explain what changed and why results differ.

Third: no owner for reference data. In financial and public organizations codes, names and classifiers (ОКЭД/КБК/КФС and internal classifications) easily diverge between systems. A report can reconcile by sum but be compiled with the wrong set of codes.

Fourth: no reconciliation checks with sources and a control base. The system may calculate honestly from the data it was given, and you might not notice that a branch load was missing or a portion of transactions was filtered out.

Fifth: poor traceability. If you can’t quickly show the path of a number from a report row to specific records and transformation rules, a disagreement with an inspector becomes guesswork.

Small example: the regulator changes a form a week before submission and adds a new breakdown. Without versions, a reference-data owner and a change log, the team may "add a column" but won’t be able to prove why totals changed and which data fell into the new category.

Quick readiness check: a 30-minute checklist for the team

Before choosing or expanding a builder, spend 30 minutes to gauge how ready your team is for a controlled process. If at least half the items rely on people’s memory and emails, automation will cause errors and conflict with audits.

• There is a single registry of all reports and a submission calendar including owners and backups for vacations.
• Roles are described by steps: who prepares numbers, who reviews methodology, who approves the final form, who administers reference data and access.
• Every change is recorded: what was changed, who, when and why, and you can open a past form version without manual searches.
• For any number in the report there is a breakdown to the source: which system provided the value, which rules were applied, which filters and dates were used.
• There is a test environment (or at least a draft mode) and a change-testing procedure: what is tested before submission, who signs off and where the protocol is stored.

Also agree on archive retention: how many years you store not only the report but the submission package (files in required format, attachments, versions, logs, confirmations and calculation descriptions). If retention isn’t set early, it’s often decided too late—after the first disputed case.

Example scenario: a form change before submission and the team’s response

Imagine: 5 business days before the deadline the regulator releases a form update. Two new fields appear, a reference list changes and new control ratios are added (for example, line sums must reconcile with a total elsewhere). The report is almost ready and data are collected from several systems.

Without a dedicated tool a manual race begins. An analyst edits the Excel template, a developer changes an extract, and the methodologist keeps a parallel version of requirements. Different files circulate by email with similar names, some edits are lost and checks are done by eye. An error is found late, the package must be rebuilt, deadlines are at risk and after submission it’s hard to explain who and why changed numbers.

With a reporting builder the process looks different. The form gets a new version where changes and validation rules are recorded. The team runs a test assembly on a control dataset, sees which sources don’t populate new fields and fixes mappings selectively. Changes go through approval (methodologist and data owner confirm logic) and only then the final submission package is created.

For audit there are clear traces: change logs (who, when and what changed in forms, rules and calculations), validation logs and lists of pre-fix errors, an archive of form and submission versions, and links from metrics to source data and applied transformations.

In metrics you usually see quick wins: preparation time for urgent updates drops, the number of reworks decreases, and transparency increases because disputed numbers can be broken down step by step. If an integrator implements the solution, agree in advance on rule ownership and who signs off on changes so speed doesn’t come at the cost of chaos.

Next steps: how to start a pilot and lock in the process

Start a pilot not with the hardest form but with 1–2 regularly submitted reports that generate the most questions from internal control or auditors. This helps you quickly see whether the solution withstands real deadlines, edits and checks.

Collect requirements in one place: which reports you produce, in which formats (tables, XML, XBRL), which validation rules you apply, retention periods for versions, who signs and who can edit. Also fix roles and access rights so accidental edits don’t become the norm.

How to run the pilot

Define a simple measurable set of steps:

• choose 1–2 reports and one type of change (for example, a new line and a new control formula);
• document the data path: source -> transformation -> form -> export;
• set roles: author, reviewer, approver, administrator;
• enable the change log and a rule: edits must include a reason;
• agree the pilot timeframe (usually 2–6 weeks) and a demo date for the business.

Success criteria are best measured in numbers: preparation time, number of manual edits, how many errors are caught before submission, how transparent it is to see "who and why changed", and how quickly a past period’s version can be restored.

Infrastructure and support

Plan the environment in advance: storage and compute for processing, workstations for preparation, access (preferably via SSO), backups and recovery. If the pilot proves value, scaling will be faster.

If you need a partner to cover infrastructure and integration, consider local manufacturers and integrators who provide servers and full support. For example, GSE.kz (gse.kz) supplies servers and workstations and offers system integration with 24/7 support, which is convenient for processes where deadlines and verifiability are critical.