Payment reconciliation at scale.

Millions of transactions per period across multiple payment rails, matched by hand.

Client: A betting and gaming operator
Sector: Gaming, finance operations

99.25%match rate, 4.6M transactions

Growth goal

Reconcile roughly 4.6 million transactions per period across several payment providers without the finance team touching routine matches.

Operating constraint

Each provider delivered a different format. Manual reconciliation was both a bottleneck and a control risk.

Measured result

99.25% matched automatically. The finance team stopped touching routine reconciliation.

The full story

Baseline: Millions of transactions per period, matched by hand across formats, with reconciliation lagging the business.
System: A five-pass cascade: AI reads and normalises each source, deterministic logic matches and flags, and people see only genuine exceptions.
Controls: Every match links to its source lines and the rule that made it. Unmatched items go to a named person, never to a default.
Result: 99.25% match rate on 4.6 million transactions. Routine reconciliation runs without the team; exceptions arrive with context.

In depth

Growth needed reconciliation that could keep up

The operator was growing, with more players, more payment options and more volume in every reporting period. Finance had to reconcile roughly 4.6 million transactions per period across several payment providers, and it had to do it without slowing down payouts, reporting or compliance. The goal was clear. Routine matches should never need a person’s time. Finance wanted to focus on controls, exceptions and insight, not on typing amounts into spreadsheets and lining up statements by hand across different feeds. Growth depended on reconciliation that could keep up every period.

The opportunity was there in plain sight. Every new payment rail made it easier for customers to deposit and withdraw. Each new provider helped the business reach more players in more places. The volume justified that choice. But every provider added another feed, another set of files and another way amounts and reference fields could line up. The more payment rails the business added, the more reconciliation time and attention each period consumed from finance. Growth was pulling the operation to a limit.

Manual matching hit a hard operational limit

The existing process did the job, but only just. Millions of transactions per period were matched by hand across several formats, with reconciliation lagging the business. Each provider delivered data in its own structure. Columns, reference fields and file layouts varied. The finance team had built up knowledge of how to read each file and how to interpret quirks, but that knowledge lived in people’s heads and scattered spreadsheets, not in a shared, reliable system that scaled with volume.

The bottleneck and the control risk came from the same place. Manual reconciliation took time and attention. As volume grew, it became harder to keep up without extending cut-offs or adding more people. Matching by hand also made it harder to prove that every line had been treated consistently. When a person is copying, filtering and joining, they can make adjustments without leaving a clear audit trail. The operation had reached a point where more growth meant more strain on both capacity and control.

SIEL built a cascade reconciliation engine that fit the operation

SIEL worked with the finance team to build a five-pass cascade reconciliation engine that sat alongside the systems they already used. The new system started by reading each source as it was, then normalised formats so that provider feeds and internal records could be compared on consistent terms. Deterministic matching logic then applied a clear set of rules across those shared structures. Each pass handled a defined case, and together they formed a cascade that could deal with the real mess of production data without losing rigour.

AI supported the parts that had previously relied on local knowledge. Where provider formats were irregular or reference fields were noisy, AI models read and interpreted entries into standard fields the rules engine understood. The core decision about whether two lines matched or not was handled by deterministic logic that could be explained and inspected. People did not have to re-key or clean data. The system turned diverse sources into a common shape, applied consistent logic and then surfaced only the items that still needed a person to look at them.

Controls were built into every match, rule and exception

Control was designed into the workflow from the start. Every matched transaction in the new system linked back to its source lines and to the rule that made that match. That meant the finance team could see, for any item, exactly why the system had treated it as a match. If they needed to review a period, they could trace a straight line from outcome to underlying data and logic. Rules could be adjusted through change control, and the impact of changes could be understood with confidence.

Unmatched items did not fall into a general holding area. Each exception went to a named person, never to a default. That created clear ownership and prevented items from sitting unreviewed. The exception queue carried context from the earlier passes: which rules had been tried, what fields did or did not line up, and any relevant history. Finance could focus on decisions, not on digging for information. Routine reconciliation ran on defined logic. People concentrated on the small residual set of genuine exceptions and on improving the rules over time.

Reconciliation capacity increased and the team moved up the curve

Once the cascade reconciliation engine was in production, it achieved a 99.25% match rate on 4.6 million transactions. Routine reconciliation ran without the finance team touching it. The team stopped handling everyday matches and instead worked where their judgement mattered. Exceptions arrived with context, and every resolved item flowed back into the system as learning to refine rules or adjust how sources were interpreted. Growth in transaction volume no longer translated directly into extra manual work each period.

For a company in a similar position, the meaning is straightforward. You can add more payment providers and handle higher volumes without accepting that reconciliation will always trail behind. By encoding the way your team thinks about matching into a connected, intelligent workflow, you create operating capacity that scales with demand. SIEL’s role is to work with your finance and operations teams, starting from one bottleneck like reconciliation, and to build the system that lets people keep control while the volume grows. Six months later, you own a live workflow that matches your growth, not your old limits.

Bring us a workflow like this

Fixed fee, agreed before we start. A senior engineer replies within two business days.