About the Company
Background of Business Problem
- The major custodian faced critical challenges with an outdated Net Asset Value (NAV) system, dependent on 38 data sources, prone to quality issues, causing discrepancies that compromised financial accuracy and eroded investor confidence.
- This necessitated additional time and manual effort to reconcile discrepancies, impacting operational efficiency.
- The outdated system's inefficiencies hindered the custodian's ability to deliver timely and transparent financial reporting.
Our Approach & Solution
Infinite used AI and RPA to automate three processes:
AI-Based NAV System
Infinite implemented an AI-driven Net Asset Value (NAV) system, leveraging machine learning to enhance accuracy in NAV calculations and address historical data quality issues. We used AI Model to find data issues by creating a system for quick processing of big data, like stock market info and daily trading data.
Data Quality Enhancement
AI models were used to improve data accuracy, consistency, and reliability throughout the custodian's operations, ensuring trustworthy financial reporting and bolstering transparency.
Robotic Process Automation (RPA)
RPA was used to streamline data gathering from multiple sources, automating tasks and reducing manual effort.
AWS-EMR for Data Processing
AWS Elastic MapReduce (AWS-EMR) facilitated efficient data processing, enabling rapid computation and analysis of extensive datasets crucial for timely NAV valuation.
Business Outcomes
95%
Reduction in NAV processing time. From 3-4 hours per client -> to less than 5 mins
~Zero
data discrepancies helped eliminating process failures
> 30%
Improved efficiency of Compliance team by reducing the time needed to generate compliance rules from over a month to less than a week.
> 90%
Accuracy in price moment prediction