Before the coronavirus epidemic accelerated the processes of banking digital transformation and changed the familiar world, financial experts had thought about the 2020s as the decade of data. Day after day, the equipment of financial institutions and specialized clusters for processing Big Data are working with trillions of code lines coming from all over the world.
The amount and complexity of data are growing at such an incredible speed that processing it manually is no longer possible. We can say that, in the near future, only those companies that have the necessary tools to cope with this information flow will be able to withstand and continue developing.
The best way to overcome the challenges of the decade’s first year is to change your attitude to data once again. The coronacrisis has made the issue of informational process reliability more acute for the financial sector and aggravated the difficulty of creating stable and interconnected software systems. In this context, the continuity of control over business processes can be realized only with the help of new and advanced algorithms for managing giant data arrays coming from multiple sources every second.
The main task of optimizing work with data is to ensure the integrity of the transmitted information. This task becomes a key one where revolutionary approaches are needed, which require the automation of basic production and financial processes together with the use of Machine Learning technologies.
One of the critically important processes is reconciliation. This technique from the accountancy arsenal provides a means for controlling financial activities, as well as helps to avoid fines, loss of money, and even bankruptcy. Process automation is a demanding task for any organization or department. A host of Fintech development companies, including Andersen, help businesses from various fields with their data processing and reconciliation. There are special models for that — RMMs (Reconciliation Maturity Models).
The best RMMs are based on the experience of conducting reconciliations in the largest financial institutions, for example, in top-tier banks. As a statistical base, data from a large sample of different-size enterprises are also used. Such models provide good results and are easy-to-use. Moreover, the best RMMs have the ability to self-optimize using ML mechanisms.
Stages of RMM evolution
Based on the analysis of the most popular RMMs, one can compose a reconciliation evolution path consisting of five stages. In a nutshell, they are as follows:
- Manual reconciliation using spreadsheets. Characterized by a high risk of errors and low auditability.
- Hybrid reconciliation, which is a mixture of manual methods and spreadsheet calculations. Here, the coordination between employees involved in the process and the tools for accounting automation is poor. The checked data is divided into fragments, and there is a high probability of duplication of work stages.
- Automated reconciliation — a complex comprised of automated systems. Operators are divided into small groups: some conduct reconciliations, while others control the process. This greatly increases efficiency and reduces the risk of errors and data duplication.
- Improved reconciliations, which are controlled by advanced data integrity algorithms that operate at all stages of work, from start to finish. The main enemy of such a system is the simplification of displaying monitored processes, which leads to discrepancies and further functioning failure.
- Self-optimizing reconciliations, which are fully automated and do not require maintenance personnel. ML mechanisms adapt to avoid discrepancy and evolve to maintain objectivity. The occurrence of errors is eliminated, while the cost and complexity of operation are significantly reduced.
When sensibly applied, ML techniques give the possibility to correct data errors by identifying inconsistencies and compromised sources at an early stage, long before serious problems arise in systems of the next level. As the ultimate controlling tool for dealing with continuous data streams, reconciliations are the optimal point of application for ML. Its accuracy is directly proportional to the volume of the throughput digital stream.
The RMM allows an enterprise to assess its readiness for the digital future that is about to come. The above-described points may seem abstract and far from reality for those who are still surrounded by the methods of the beginning of the century: bulky electronic spreadsheets and pervasive personal control of responsible executives. However, by using the RMM, one can assess the real status of the enterprise and understand in which direction to continue moving.
Building on success
With the evolvement of reconciliation mechanisms ensuring data integrity that promote reliable operations and eliminate various risks, businesses are gaining a kick start for development. The RMM helps to work out a development strategy that solves the problem of reconciliation forever and opens the way for unimpeded growth.
