How to Streamline Reconciliation and Drive Productivity with AI/ML Technology

Reconciliation is a complex, time-consuming process that involves a variety of stakeholders. With the rise of artificial intelligence (AI) and machine learning (ML) technologies, asset managers can streamline reconciliation and unlock new productivity gains. In this blog, we will discuss the importance of reconciliation and how AI and ML technologies can improve the process.

Reconciliation is the process of comparing and reconciling two sets of records or documents to ensure transactions are accurately recorded in both. It is an important part of financial management and can help protect organizations from fraud and other financial risks. As such, reconciliation is an integral part of the accounting process and can help asset managers improve accuracy, efficiency, and cost savings.

What are the Operational Challenges of Reconciliation?
The operations team is responsible for ensuring every transaction in the bank statement is accurate, comparable, and consistent with the information in the internal book of records (IBOR) or accounting book of records (ABOR). This process can quickly become very complicated because each transaction is recorded by the bank and the company at different times. This is especially true for hedge fund reconciliation. Other complications arise from differences in currencies and data formats as well as from huge transaction volumes.
The critical challenge for the operations team is reconciling varied transactions, such as interest bookings, cash deposits, cash withdrawals, pay downs, and expenses, separately from standard buy and sell transactions. These other transactions are often booked without clean identifiers, so the transaction description is the only meaningful way to identify them. Unfortunately, these descriptions are often not structured and consistent because they contain non-standard, running hand trade information. Reconciling these transactions is risky because matching rules can’t easily “match” or “pair” two related transactions. This problem worsens in “one-to-many” and “many-to-many” scenarios.

How AI/ML Technology Streamlines Reconciliation
Together, AI and ML technology can increase the speed and accuracy of reconciliation. In general, AI can be used to review data, detect anomalies, and make recommendations for further review, while ML algorithms can automate the process, identify trends and patterns in data, and apply those trends to future reconciliations.
The primary advantage of using AI and ML technology for reconciliation is dramatically reducing or even eliminating the need for manual workflow, which can save a great deal of time and money. Additionally, it can reduce the risk of errors that occur in manual reconciliations, resulting in fewer financial losses.

Break Resolution with an AI/ML-Powered Suggestion Engine
One of the most powerful applications of this technology is an AI/ML-powered suggestion engine, which has the capacity to resolve all reconciliation breaks more efficiently. Here are five reasons why:
1. Using an AI/ML-powered suggestion engine enhances the effectiveness of break investigation and classification, keeping the focus on the fund’s specific strategy and business processes.
2. A suggestion engine can glean additional information from unstructured data (such as similar names, abbreviations, or synonyms) to make matches for a wider range of transaction types.
3. A suggestion engine can help match two data sets that are not grouped at the same level, including scenarios in which one party has one booking and another party has multiple bookings of the same transactions, or vice versa.
4. With the help of ML, a suggestion engine can successfully analyze patterns and recognize them “on the fly,” eliminating the need to perform the same actions manually, day after day.
5. A suggestion engine can leverage advanced data mining and analytics technology like neural networks, decision trees, and TensorFlow, among others, to analyze data sets, draw relationships within each data set, and predict the next appropriate action.

Overall, an AI/ML-powered suggestion engine improves operational control by efficiently handling position reconciliations. When it is combined with an outsourcing arrangement, creating a “reconciliation as a service” approach, it also frees up time and allows asset managers to focus on other critical tasks.
Learn more about the IVP Reconciliation Solution or contact sales@ivp.in to set up a demo.