Services
The client is an American, multinational investment bank and financial services company. It needed a data strategy that could easily and accurately identify audit problems and save costs.
The client was facing audit-related issues, which led to both regulatory action and drops in market evaluation. Besides, discovery of audit issues late in the process required deployment of additional resources under time-constrained circumstances.
We, at Mphasis, implemented a data strategy that predicted audit issues early in the process and ensured proactive allocation of resources. This avoided embarrassing audit problems that negatively impacted the client’s brand. We achieved this through:
• Providing strategy and proof of concept (POC) solutions
• Implementing XGBoost and Python technologies
Through our data strategy solutions, the client received benefits such as:
• Minimized audit errors as XGBoost predicted audit problems with 76 percent accuracy using test data
• Reduced risks to revenue, by minimizing chances of customers deserting the business with real or apparent audit/legal issues
• Increased operating margins, as cost savings increased because resources were allocated only on a need basis
• Enhanced shareholder and customer satisfaction, as good auditing provided a strong brand image and stable stock prices
• Increased quality and effectiveness as auditors spent more time on complex issues identified by machine learning, thus resulting in greater quality and effectiveness