Abstract

One of the operational challenges that the Investment Banks (IBs) are facing today is to maintain a highly accurate and consistent data across various systems and databases. The IB’s are generally slow in adapting to a newer and more robust platforms and systems. Though there are several such next-generation platforms offered by the service provides in the market, due to high cost and legacy information these changes will span over anywhere between 3-5 years. As a simple use case of onboarding a new customer onto the Bank’s trading platform would require average 6-7 different downstream systems. And all this is done manually. In the recent few years, IB’s are opening up to the concept of Robotic Process Automation (RPA) and in some cases leveraging Artificial Intelligence (AI). The potential for big data analytics is becoming very prominent in the coming days and years. This paper intends to cover a simple use case for predicting transactional errors while processing for an on-boarding cum fulfilment function across various products like Equities, FI, Foreign Exchange and Money Market Instruments (FXMM) and Over the Counter (OTC).

Due to its simplicity and ease of understanding by the non-analytics folks (pure operations folks), decision tree algorithm is used as the technique for building the classification model. Overall the precision-recall rate is coming up to ~70% on the test data. An access database is developed to run the logic from Decision Tree to identify probable bad transactions on a daily basis, based which the 4i (QC process) will check for accuracy and take corrective actions. This, in turn, would help the process to control a number of bad transactions to be flown into the system. Overall there is a reduction in remediation effort and improvement in First Pass Yield (FPY) which is one of the key KPIs for the vendor.

Presented and Published at: Proceedings at 5^th International Conference on Business Analytics and Intelligence on December 2017 at IIM Bangalore