IVP’s AI/ML-powered suggestion engine can dramatically reduce the time spent handling break reconciliations. Providing insight on the most probable user actions so that operations teams can focus on true genuine breaks, IVP’s Suggestion Engine acts as an assistant in cleaning up breaks and addressing reconciliation processing inefficiencies. The result is enhanced operational efficiency, giving users a break from breaks.
What are the challenges faced by the operations team?
The operations team aims to verify that each transaction in the bank statement is consistent and comparable to the internal records as presented in the Internal Book of Records (IBOR) or Accounting Book of Records (ABOR). Especially with hedge fund reconciliation, this process can become very complicated because of the difference in time when a particular transaction is recorded in the bank and in the company. Oftentimes, adjustments are needed since other complications can arise from differences in currencies and data formats as well as potentially huge transaction volumes.
The critical challenge for any operations team is reconciling varied transaction types like interest bookings, cash deposits, cash withdrawals, pay downs, expenses, etc. apart from standard buy and sell transactions. These varied types of transactions are often booked with no clean identifiers, making the transaction description the only way to meaningfully identify them. However, these transaction descriptions are often not structured and consistent as they contain non-standard, running hand trade information. Reconciling them is risky as none of the matching rules can truly “match” or “pair” two related transactions that intend to be similar. The problem aggravates matching transactions in scenarios like “one-to-many” or “many-to-many”.
How can an AI/ML-powered suggestion engine resolve it?
An AI/ML-powered suggestion engine has the potential to resolve all of the breaks in an efficient approach:
- Using an AI/ML approach to break reconciliations enhances the effectiveness of the break investigation and classification process, keeping a focus on the fund’s specific strategy and business processes.
- By using natural language processing (NLP) and machine learning, the system can also understand and interpret information from free-flowing text like transactions, security descriptions, user remarks, etc. This is a level up from conventional matching algorithms that use strict string or number matching identifiers to determine matches.
- A suggestion engine can empower the system with additional information from unstructured data (e.g similar names, abbreviations, or synonyms) that can be used in order to make matches.
- A suggestion engine can also help match two data sets that are not grouped at the same level. It handles scenarios where one party has one booking and another party has multiple bookings of the same transactions, or vice versa.
- With the help of machine learning, a suggestion engine can be made intuitive enough to analyze patterns and learn “on the go” so that the user does not need to perform the same action day-over-day.
- A suggestion engine can leverage advanced data mining and analytics technology like neural networks, decision trees, TensorFlow, and more to analyze the data set, draw relationships within each data set and predict the next.
An AI/ML-powered suggestion engine not only improves the operations control function but by efficiently handling position reconciliations, this reconciliation as a service (RaaS) feature frees up asset managers’ time and allows them to focus on other critical tasks.
To learn more, visit IVP Reconciliation Solution or contact firstname.lastname@example.org.
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