Hotspots for AI implementation in Finance & Procurement

Finance and procurement transactions have a crucial impact on the overall fiscal health of any company. This makes them some of the most important, most comprehensive business processes, regardless of company size and industry. Given that the number of global transactions keeps rising, procurement and finance teams are facing the challenge of accelerating operational processes and increasing efficiency. In addition, the need for continual risk mitigation is putting strong pressure on these back-office business areas. Therefore, companies need to recognize the importance of integrating digital transformation into these processes as well.

Growing need for smart processes in finance and procurement

Finance and procurement teams have always dealt with quantitative data and thus became a target for digitization very early on. Today, their activities involve extensive data assets which other business areas, such as governance/compliance/risk, draw on. However, traditional reporting and descriptive analytics only scratch the surface of the insights that can be gained to drive improvements. Thanks to the new possibilities offered by artificial intelligence (AI), companies are now taking the next step.

Recent advances in natural language processing (NLP), machine learning (ML), and pattern recognition have opened the door for companies to gain insights into hidden data to support decisionmaking across businesses. AI, including machine learning and deep learning, is by far not the only kind of embedded technology that modern applications are equipped with. Robotic process automation (RPA), for example, is another important topic when it comes to automating repetitive, lowvalue tasks in order to be able to focus on more strategic work.
 

Hotspots for AI implementation in Finance & Procurement


Hotspots for AI implementation

The overall impact of AI on finance and procurement processes across the value chain is growing, and there are many ways in which AI is able to support departments with their day-to-day challenges and with making processes more efficient.

Here is a first insight into how AI can be used to advantage in the context of four major components of the value chain:


Procure-to-pay (P2P)

P2P is an end-to-end process covering all activities of the procurement process, from ordering the raw materials for manufacturing a product or service to paying an invoice.

  • Sourcing and contract negotiation: Natural language processing (NLP) can be used to efficiently scan and analyze large volumes of unstructured data from various sources. This enables procurement experts to negotiate and collaborate with suppliers in a more intelligent way.
  • Invoice processing: Intelligent bots can mimic human activities such as recognizing invoice information, extracting and processing data.
  • Payment processing: With the help of intelligent bots, payment processing can be automated. Machine learning allows to take into account information from previous payment cycles, such as date or type of payment.


Order-to-cash (O2C)

The O2C cycle is an important part of any company’s operations: orders are made and fulfilled, bills are sent, disputes are resolved, and payments are received.

  • Customer data management: AI can help companies to assess the creditworthiness of customers and predict their ability to pay.
  • Order processing: Intelligent automation bots can extract and process order data from unstructured sources, such as spreadsheets or e-mails. Also, the sentiment of an e-mail can be processed, and it can be suggested which e-mails to answer first


Financial planning
 

  • Budgeting (spend analytics): AI technologies facilitate expenditure analysis and planning. In procurement, AI can assist sourcing professionals in predicting purchasing patterns in order to make the right decisions on volume commitments and timing.
  • Forecasting: AI can improve forecasting models by utilizing huge amounts of historical internal and external data to better calculate performance or demand, since spreadsheet-driven forecasting can only process a limited amount of data.


Financial reporting
 

  • Accounting: Accounting departments are increasingly faced with the challenge of becoming more efficient. AI can automate administrative tasks to relieve staff.

  • Reporting: The process of creating an annual report is complex and time-consuming. When preparing a report, RPA can be used to generate a draft by taking the previous year’s report, adapting the figures, and sending it to the accounting system.


Get more insights

If you´re interested in the overall impact of AI on finance and procurement processes and ways in which AI is able to support departments with their day-to-day challenges and with making processes more efficient, read our SITSI InBrief Analysis. It also covers the vendor landscape, use cases that have been adopted in the market today, and PAC´s recommendations for users and service providers.