Bill payment volume is high, margins are thin, and every failed transaction costs you twice — once in the retry and again in the complaint. We put machine learning on top of your existing BBPS flow to predict failures, match unreconciled credits automatically, and give your support team the answer before the customer finishes explaining.
Each one is trained on your own transaction history. None of them requires replacing the BBPS platform you already run.
Scores each transaction before it is submitted, using biller health, time of day, channel, amount and recent failure patterns. High-risk attempts get held for a few seconds, rerouted, or flagged to the agent — instead of failing and generating a complaint an hour later.
Matches settlement files, bank credits and transaction records automatically, including the awkward cases where amounts are aggregated or references are truncated. Only genuine exceptions reach a human, and each comes with the model's best guess attached.
Learns which biller endpoints are slow or unreliable at which hours and adjusts fetch timing and retries accordingly. Caches predictable bills so a customer at a counter sees their amount in a second rather than waiting on a biller that is having a bad afternoon.
When a complaint arrives, the transaction, its full status trail and the likely cause are already on the agent's screen. Suggested resolution based on how similar cases actually closed, with the agent deciding — never an auto-response sent to a customer.
Flags unusual patterns at agent level — sudden volume spikes, repeated payments to the same consumer number, or reversals clustering on one terminal. Alerts your risk team with the evidence rather than a score nobody can interpret.
Predicts transaction volume by biller category, region and day so you can size float, staffing and biller capacity ahead of due-date peaks instead of reacting to them on the morning.
Payments is the wrong place for an impressive demo. These are the four rules we hold to when a model touches money.
The model recommends; a person authorises. Refunds, reversals, agent suspensions and customer-facing responses all pass through a human queue. What the model saves is the search and the reading, not the judgement.
No autonomous money movementWhen a transaction is flagged, the reasons are shown in plain language with the contributing factors ranked. Your risk team and your auditor can both follow the logic. A score with no explanation cannot be defended in a regulatory conversation.
Reasons, not just scoresEvery model spends four to six weeks predicting alongside your existing process without acting, so you can measure it against reality on your own data. If it does not beat the current baseline by a margin worth having, we do not deploy it.
Proven on your data firstModels train on your transactions inside your infrastructure, in the region your policy requires. Nothing is pooled across clients and nothing is sent to a third-party service for training. The model weights belong to you along with the code.
No cross-client poolingYou see the numbers before anything goes live, and you decide whether it continues.
Two weeks reviewing your transaction history, failure codes and settlement files. We tell you honestly which capabilities your data can actually support — sometimes it is two of the six, not all.
Measure current failure rate, reconciliation effort and complaint handling time. Without a baseline, any improvement claim later is unprovable.
The model predicts alongside your live process without acting. Four to six weeks of side-by-side results, reported weekly against the baseline.
Enabled for a slice of traffic first, widened as the numbers hold. A kill switch your team controls, not ours, is in place from day one.
Six months minimum for failure prediction, twelve is comfortable. Reconciliation matching needs less because the patterns are structural rather than behavioural. If you have under six months of clean data, we will say so and suggest starting with the reconciliation work while history accumulates.
Then we do not deploy it, and that is a real outcome rather than a formality. It has happened — on one engagement the client's manual reconciliation rules were already catching 96% of cases, and the honest advice was to fix the remaining exception workflow instead. The shadow run exists precisely so that finding costs you weeks, not a year.
No. It reads from and writes to what you already run, whether that is our BBPS Portal or another vendor's. If your current platform has no usable API, integration takes longer and we will flag that during the data assessment rather than after you sign.
Models drift as biller behaviour and volumes change, so they need periodic retraining. Most clients keep us on a light monthly retainer for monitoring and retraining. If your team has data science capability, we hand over the pipelines and training scripts along with the code and they run it themselves.
Forty-five minutes and a data sample under NDA. We will tell you which capabilities your data supports and what the realistic improvement looks like — including when the answer is that it is not worth it yet.