Partner Content Articles and Reports

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.

Artificial Intelligence has many applications in marketing and advertising. However, it may prove useful in the near-term future for businesses to start looking into AI solutions for after they generate a lead. AI already has applications for converting leads into paying customers and making progress on various steps in the sales process overall.

According to a 2017 report by the Financial Stability Board, artificial intelligence (AI) and machine learning firms managed assets of over $10 billion in 2017, with further growth projected in the next five years. The reasons for this are clear; AI can now present wealth managers with new capabilities to enhance and further personalize their services, at scale.

Data is essential when it comes to building machine learning models for business applications. A strong AI strategy is predicated on data that is specific to the business problem a company is trying to solve, as outlined in our recent article: Data Collection and Enhancement Strategies for AI Initiatives in Business. When it comes to executing on that strategy, oftentimes the first thing a company needs to do is collect some or all of the data it will need to build the right machine learning model for its use case.