Partner Content Articles and Reports

I hope that by the end of this episode of the AI in Industry podcast, you'll not only be able to hire better data scientists who will be a fit for your business problems and build better data science teams, but also pick the AI applications and use cases that you should bring into your business versus those that you shouldn't.

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 the last year, interest in so-called “autoML” has risen greatly in part due to its promise of bringing artificial intelligence to businesses that have been blocked from accessing it due to its serious time, talent, and budget requirements. Although machine learning may still be widely unavailable to small businesses, medium-sized businesses may find that autoML allows them to make use of it in the coming years.

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.

The insurance industry is dominated by large global firms that deal with thousands of customers filing insurance claims every day. Claims processing is a huge part of the insurance business process and improving turnaround time for each claim is critical to reducing operational costs at insurance firms.

Along with the rise in popularity of chatbots and simple conversational interfaces, there is growing interest around other natural language processing (NLP) capabilities in the banking, finance, and insurance industries.

The insurance sector is highly competitive, and there seems to be a consensus among experts that customers in the industry favor insurance products that are tailored to their unique needs. Large insurance firms could deliver personalized customer experiences and improve their operational efficiency by adopting AI.

Banks and investment organizations usually have large research teams that are tasked with investigating and monitoring events that might affect financial trading markets. Investment research is a business function in these firms and is a fundamental part of what is required from analysts, equity managers, investors, and traders.