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Solving the problems of reconciliation

Woman looking at a computer
ActiveViam |
November 18, 2021

The concept of front to back office (FOBO) reconciliation has always been fundamental to financial services and the management of operational risk. Managers need to be sure that the information in the Front and Back office is consistent, and that trades are not being “lost” or wrongly assigned between two parts of the trading system. They also need to be sure that discrepancies between the Front and Back office do not represent fraudulent behaviour being concealed by traders.

To achieve this requires an ability to extract the data at scale, map it correctly between the Front and Back office, then analyse any anomalies. Historically, the reconciliation process has often been frustrated by “old” data, poor analytics and a disappointing UI. Cedric Cavallier, with Senior Business Development at ActiveViam, explains the transformation occurring in the world of reconciliation.

Reconciliation between the Front and Back office is a vital, daily control for banks and asset managers. It is an essential management tool as well as a means to control fraud and meet the growing demands of regulators. And with millions of trades to reconcile daily, those problems are multiplying daily.

However, the reconciliation process has traditionally posed multiple problems. First, there is the core problem of mapping the different data between Front and Back office. This has generally been a very technical task that required expertise in specific software systems. A second issue is the sheer time taken to do the reconciliation. Typically the batches are run overnight and certainly not intra-day or in real-time, even when it has been necessary to check a trade or number.

Thirdly, the output following reconciliation is static and immediately out of date. So it gives a picture of the situation at the previous day’s close, not a much more useful view of a dynamic, evolving situation. Finally, in order to analyze the output of the reconciliation process, many banks need to bolt on another system that is not designed for the sole purpose of reconciliation, which is often a frustrating experience for users.

A system designed for reconciliation 

Atoti solves each of these problems with single, seamless software designed to handle huge volumes of data, flexibly and at speed. As well as its well-proven ability to tackle problems like market risk and PnL explained, it provides the ideal solution to the well-known challenges associated with reconciliation.

With Atoti it is easy to create a connection between Front and Back office data for effective mapping. Non-technical professionals can map the file to be reconciled, and start building the dashboards and reconciliation reports. Of course this reconciliation could take place between any systems that require data exchange, for example, Accounting with Front office, or Risk and Accounting. It also works in real-time, so if required the picture that the user sees can be current and evolving, not static and dated.

Unlike the systems typically used by many banks and asset managers, Atoti also comes with the necessary analytics built in, so users don’t just receive the data, they can also analyze and interpret it, drilling down to explore any anomalies thrown up by the reconciliation process. It is also easy to create dynamic dashboards to highlight trends between different dates. We find that Atoti gives users a great UI experience compared with most legacy systems.

Users can easily perform trend analysis and look at day-to-day variations. The software is designed with a full limits framework with approval workflow and audit and a sign-off functionality to follow up the reconciliation activity and validate it (e.g. the “four-eyes” principle). Atoti lets users carry out deep root-cause analysis, so once a mismatch is identified users can quickly interact with the data and identify, visualize and present the problem to any department at any management level.

A tool that makes the reconciliation process easier and more efficient will automatically improve upon the management of operational risk within banks.

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