Banks spent the last decades gathering data. The data, however, is often disconnected and stored in silos, thereby making data management difficult. As a result, banks find it challenging to make bank-wide research and discover insights from data with the help of Artificial Intelligence (AI) applications. A knowledge graph, which is a model of a knowledge domain, could help banks overcome this challenge.
A knowledge graph maps all business objects and concepts an enterprise works with, together with their interrelations. Structured as an additional virtual data layer, the knowledge graph lies on top of existing databases and links both, structured and unstructured data together at scale. This ability to link both structured and unstructured data will allow AI applications to use the information available inside databases and in text documents.
In practice, knowledge graphs can be used to improve personalized customer services. For instance, knowledge graphs can be used by banks to build recommender systems. As they link data in smart ways, they allow recommender systems to make much better recommendations than pure machine learning.
Banks are deploying these recommender systems in their self-service portals that show customers a personalized view of information, such as new offerings and services. Similarly, banks are incorporating knowledge graphs in online portals to improve customer financial literacy. Banks are also building digital assistants to help customers acquire financial knowledge through semantic search over knowledge hubs.
Some banks are implementing knowledge graphs to support their AI strategy including automated enrichment via relationship discovery, content contextualization and a better understanding of the meaning of data. Furthermore, knowledge graphs are being used for optimizing processes relating to compliance, fraud detection, risk assessment, lease agreements, and even loan applications.
To get started with knowledge graphs, banks should begin by defining a concrete use case with a specific goal. By working on a defined project, banks can understand the full potential and see other opportunities to apply and eventually deploy knowledge graphs throughout the organization. It is, therefore, necessary to evaluate the usefulness of knowledge graphs based on individual use cases while building up enough knowledge to be able to embed the methodology in a more comprehensive AI strategy.
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