Natural language processing (NLP) has evolved rapidly over the past couple of years with the support of AI algorithms. In a short time span, it has evolved from an R&D topic to an established technique for firms globally. Knowledge graphs, intelligent document extraction, and AI-based text analytics make NLP technologies more applicable across verticals and business processes.
Banking, financial services, and insurance verticals have actively explored NLP use cases as part of their digital transformation initiatives. However, it’s often challenging to identify opportunities and move projects involving NLP technology from proof of concept to live production. Leading firms have successfully built NLP solutions to:
- Deepen understanding of customer intents. Every customer experience or marketing executive dreams of being able to infer meaningful customer intentions to serve them better and grow their business faster. NLP can help firms move closer to this goal. It has the cognitive capability to extract and infer intentions from conversations and interactions in functions like billing, underwriting, disbursements, and policy servicing.
- Take operations to the next level of efficiency. Fast-growing companies are facing the challenge to efficiently scale up operation capacity with manual processes. Effective NLP solutions support business growth by reducing cost for routine process and augmenting their staff.
- Reveal potential risks through relationship analysis. Financial services firms analyze risks based on complex networks of information. Expertise also correlates external market information with targeted objects. Recent NLP solutions leverage knowledge graphs (KGs) to represent and index financial relationships. Firms can visualize and understand the logic of suspicious insights through the knowledge graph.
Many solutions have already adopted the technologies we introduced in AI 2.0. Transformer models such as BERT have proven the ability to achieve high accuracy in many solutions. Federated learning can help leverage data from multiple agencies but still ensure privacy. If you’re interested in learning more from the nine use cases we studied, please take a look at the new “Natural Language Processing Use Cases For Financial Services In Asia Pacific” Forrester report. And if you’d like to share some insights about your company’s strategy for emerging technologies, feel free to reach out to me directly or set up an inquiry by emailing email@example.com.