Enterprises of all kinds often need to devote a costly amount of human resources to manually enter financial information in various processes of financial management and reimbursements. The industry is benefiting from the development of new technologies such as optical character recognition (OCR), natural language processing (NLP) and machine learning (ML), which are increasingly deployed across corporate financial management.
Artificial intelligence (AI) technologies are now widely used in tasks such as reimbursement specifications, automated financial statement generation, and content extraction.
Enterprises are using technologies such as machine learning and deep learning for automation and to provide solutions. The overall market for enterprise AI is showing strong growth and is expected to boom in coming years.
A key future development trend will be in enterprise AI financial management systems. Reimbursement tasks are a particularly important part of enterprise finance, and AI will make them simpler and more efficient.
Identification of data: OCR is a commonly used AI capability. An item such as a bill is passed through an image acquisition device, text is captured by text recognition technology, and a data structure is entered into the financial system. This technology is able to identify, extract and sort key information for example in an invoice.
Analysis of data：Key information identification retrieves structured text data and identifies preset information content or related information content through keyword and word vector information. This technology can be applied in inspection of corporate financial copy norms to avoid risks and losses. Machine comprehension techniques meanwhile can learn rules and review reimbursement application content and identify non-compliant reimbursement content to reduce errors and improve corporate finance department efficiency.
Applications and Scenarios
SAP Concur — By automating expense management processes, Seattle-based spend management solution company SAP Concur simplifies employee expense reimbursement, enabling faster receipt of reimbursement payments while helping companies save time and money.
SAP Concur provides interconnected travel and expense management, enabling organizations to understand expenses in more detail before, during, and after travel. SAP Concur’s cloud-based solutions can provide employees with an easy experience and help enterprises realize digital transformation.
Maycur — Hangzhou Maycur intelligent reimbursement is a one-stop service for intelligent identification, intelligent auditing, and intelligent analysis. It allows employees to easily record consumption, submit for reimbursement and track reimbursement status. The system uses computer vision technology based on deep learning algorithms and convolutional neural networks to greatly reduce the time spent entering and reviewing invoice information.
On the basis of massive data support, the intelligent reimbursement system can learn and train itself. Managers can approve a reimbursement at any time, view statistical statements in real time, and control organizational expense status. The Web terminal platform facilitates auditing and supports the reimbursement of employees to reduce costs and improve operational efficiency.
AppZen — The Silicon Valley startup’s AI-powered platform performs spend auditing in financial services. The system uses computer vision, deep learning, and semantic analysis to help users shrink their spending and weed out errors and waste. The service can identify purchases that need scrutiny by looking at receipt, invoice, and credit card transactions to identify which purchases are within policy. It includes expenses and invoice store, validate matching, fraud detection and reimburse recording.
Enterprise intelligent financial systems have already proven their practical value in the market, and reimbursements and other corresponding service processes and deployment channels will continue to flourish. As more and more enterprises introduce smart financial systems, this will feed huge amounts of industry data into the cloud system to further power AI’s transformative effect on the global financial system.
Author: Ying Shan | Editor: Michael Sarazen
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