Companies are embracing AI and its applications to detect anomalies and frauds in invoice process

Companies across the globe are using some form of technology to carry out various business activities. Unfortunately, some of the areas like Accounts Payable (AP) and finance managements deal with a large set of complex information that makes manual anomaly detection impossible. Therefore, telecommunication and healthcare industries are embracing AI and its applications to detect abnormalities in the invoice process.

Anomaly detection is the identification of rare items, events or observations, which raise suspicions as they differ significantly from the majority class. Some of the general anomalies in business are transaction fraud in consumer financing, rare medical conditions such as malignancy in tumour or specific behaviour traits of employees or customers which are rare. Unfortunately, anomalies are hard to detect as it barely takes place in less than 5% in the entire data. That too in dense fields like telecommunication and healthcare, the detection of anomalies by manual labour seems nearly impossible. To make matters worse, the intrusion of 5G made the billing and anomaly detection even more complicated. Accounts Payable (AP) teams can’t always assure that they can find all the anomalies as large enterprises pay thousands of invoices each month. With such large volume of invoices, companies leave themselves at the risk of overpayment or potential invoice fraud.

A report shows that 95% of the companies now view big data and real-time analytics as a necessity to keep up fraud detections and anomalies. However, humans can’t be experts in detecting these abnormalities. Leaving the issues unattended for a long time will cause massive havoc to the company. Lagging application performance can cost a fortune for businesses. For example, a one-second delay in Amazon to detect the anomaly will cost the company US$1.6 billion in the annual sales. Most of the companies have a mix of human workers and technology to detect invoice anomalies. The manual process involves going through all the data one-by-one. It relies on sampling techniques based on organisational policies, resource availability, individual skills and experience. Henceforth, the process is comparatively very slow and lacks coverage across the entire set of generated invoices. The only way out of the trouble is to use Artificial Intelligence (AI) and its applications to tackle the mounting anomalies.

Role of AI in finding anomalies

Artificial intelligence delivers real value in detection and resolution of anomalies for upstream transactions involved around the procure-to-pay process. Each node such as order, acknowledgement, shipping, invoice and pay identifies a transaction point in the process. Then AI goes through the velocity, volume and value of the data to find anomalies. Machine learning can also help enhance the accuracy, speed and quality of anomaly and fraud detection in any organisation. As abnormal patterns can emerge from any type of data, companies are switching to AI-powered anomaly detection.

Telecommunication: Telecom invoices are one of the most complex types of invoices generated in the industry. It involves a lot of numbers and a wide range of products and services. At such an entwined source, errors are inevitable. Henceforth, telecom companies are embracing AI to explore network anomaly detection, aimed at improving infrastructure health and gaining insight into the operations.

Healthcare: Medical fraud detection is a difficult task. Henceforth, healthcare institutions are adopting supervised and unsupervised AI to tackle the challenge. Intelligence algorithms are being used to find fraud and abuse in medical payments and insurance fraud.

Examples on how AI detects abnormalities

Businesses mostly run on buying from manufacturers and selling its consumer. They mostly have a fixed price for a certain unit of goods they purchase. However, it is not sure that the price of the materials stays the same forever. If the product from manufacturer increases its cost, the chances are high that it goes unnoticed in a business organisation. The careless mistake costs a huge amount when it goes out as a whole unit. Remarkably, AI can manage the situation by detecting the change of cost and bringing it to the concerned person’s notice. Besides, AI can also find more subtle changes like a gradual increase of bill amounts over the months or years.

Not all organisations are honest in doing business. Some might plan to trigger you in an unnoticed way. These fictitious companies initially submit low-value invoices to see if they get paid and slowly increase invoice amounts over time. They might even send several invoices with consecutive numbers to avoid detection. However, AI can clearly analyse the invoices and report on the abnormalities.