Data Engineering
Big Data Analysis to Optimize Cashflow Within Client Network
Context
A financial services company brought us a Big Data problem: to help them appoint where the company's resources were effectively being allocated, taking in consideration the cash conversion cycle and other financial variables such as EBIT and Risk Exposure, and what would be the relative importance of each sector, segment or size.
Method
Manipulating the client’s large internal database containing all sales, transactions and cashflow data, through in-depth Exploratory Data Analysis (EDA) and advanced Data Viz, we identified which segments were the riskier ones and which clients caused biggest float-related issues.
Benefits Achieved
Our clients could then finely adjust their policies regarding some specific segments or clients who were consuming the company’s cashflow, freeing an estimated USD 2 million on May 2021 only from the operation. An interesting final result: we uncovered that the company was not getting good returns from its “best client” because it was excessively financed but this fact was unnoticed until then.