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Based in Europe, our client is the research and development center for one of the world’s largest manufacturers of premium and commercial vehicles. The center focuses on research, IT engineering, and product development.
Financial Services
The solution leveraged artificial intelligence (AI), data analytics, and machine learning (ML) to automatically recognize the client’s supplier invoice formats. This empowered the client to modernize business processes and streamline critical functions. The solution enabled the accounts team to meet processing timelines and increase efficiency with straight-through processing of invoices.
The client’s finance and accounting team was struggling to keep up with ever-increasing volumes of invoices generated from thousands of suppliers. The scenario got convoluted as the types of invoice formats started increasing with the addition of new vendors, creating large sets of unstructured data.
Considering the complexity of the procedure and the sheer volume of unstructured data, we proposed an invoice processing HyperApp approach designed to optimize STP rather than a typical rule-based RPA fix. This is due to the fact that even a minor change in the UI, APIs, or data transposition could break down the bots’ functionality. Such disruptions in automation cause downtime and demand allocation of additional technical resources resulting in backlogs.
The client’s finance and accounting team was struggling to keep up with ever-increasing volumes of invoices generated from thousands of suppliers. The scenario got convoluted as the types of invoice formats started increasing with the addition of new vendors, creating large sets of unstructured data.
Considering the complexity of the procedure and the sheer volume of unstructured data, we proposed an invoice processing HyperApp approach designed to optimize STP rather than a typical rule-based RPA fix. This is due to the fact that even a minor change in the UI, APIs, or data transposition could break down the bots’ functionality. Such disruptions in automation cause downtime and demand allocation of additional technical resources resulting in backlogs.