AI Articles and Analysis in Finance

Optical character recognition (OCR) is a subset of machine vision technology that focuses on recognizing written letters and characters and reproducing them digitally for later use. This opens up many possibilities for the banking industry, including some security solutions, and notably, document digitization.

Since the early 2010s, major banks have used anomaly detection - an AI technique for identifying deviations from a norm - for automating fraud, cybersecurity, and anti-money laundering processes.

The financial sector has been an early adopter of AI. It is likely that the use of algorithms in trading and the fact that most large financial firms already have teams of software developers aided the transition into data science and AI applications in the industry.

AI applications for the banking and finance industry include various software offerings for fraud detection and business intelligence. There are also predictive analytics applications outside of these that help banks automate financial processes and services that they offer their customers and provide internal analytics.

In recent years, the demand for greater cybersecurity has risen even among the everyday citizen. This is especially true for one’s personal and financial information, which fraudsters are constantly finding new methods of breaching accounts to find. This need for security has also risen in insurance, and numerous AI firms are selling claims fraud detection solutions to the insurance sector.

Applications for artificial intelligence technology within the financial industry are most often focused on document search and fraud detection. This is especially true for large credit card companies such as Visa and Mastercard, who themselves provide technology to smaller businesses and card issuers.

Machine learning has far-ranging applications in the finance space broadly from document digitization to document search, chatbots to fraud detection. The insurance space in particular, however, stands to benefit from AI and machine learning applications in a few unique ways. They could help insurance firms with a challenge that’s at the forefront of the insurance world: attracting and meeting the needs of millennial customers.

In recent years, there seems to be a sense of urgency for banks to go digital and expand into new communication channels. In ten years time, physical brick and mortar banking might not be the preference of the majority of customers. To attract younger millennial customers, banks seem to be realizing the need to understand their preferences and interact with them in the way they want to be communicated with.