Commercial banks make large investments every year accessing, compiling and delivering data. These investments increased dramatically in the past decade, as banks look to source data across deposits, liquidity and payments domains to meet the demands of regulators and clients for balance and transaction reporting.
These two stakeholder groups – regulators and clients – dominate the use cases for commercial data analytics. A range of visualization tools help bring analytic techniques to life. Banks are beginning to recognize the growth potential for leveraging transaction and customer data.
How can commercial banks benefit from graph databases?
The advent of emerging technologies such as graph-based machine learning, or GraphML, as well as related data visualization tools provide commercial banks with new opportunities to reimagine banker experiences and identify opportunities for growing revenues and optimizing efficiencies.
Graph databases depict a collection of interlinked descriptions of entities. For example, the people you connect with on LinkedIn have other connections in your company and industry, and LinkedIn uses these connected data to make relevant recommendations. LinkedIn’s graph database connects members, jobs, titles, skills, companies, etc. that make sense in the user’s context.
In recent work with a commercial banking client with a global footprint, we apply knowledge graph-based concepts to recast commercial banking transactional data into abstracted concepts of nodes (clients) and edges (relationships and transactions) that capture both the details of the entity and the nature of the relationships. For example, Client A (node) is a supplier (edge) of Client B (node). Depicting the data in a graph-based structure helps create a business-centric, real world abstracted view of buyer-supplier relationships.
The adoption of graph-based data models breaks down the data silos across geographies and products (accounts payable vs liquidity vs receivables) and helps bankers get a network-centric view and identify deepening and cross selling opportunities in a client’s ecosystem.
Banks can leverage graph database tools to view client data and the relationships in that data. Relationship managers can use data visualization tools to examine connectedness of financial supply chain data in a specific client context – to identify and deepen high value networks, optimize pricing and identify specific product propositions.
What is involved in client network analytics?
Network analytics requires a mindset shift from client centricity to network centricity.
Commercial banks typically view data at the client level – and sometimes even more narrowly, at the account level. There are good reasons for this, principally that stakeholder groups like regulators ask for very specific data by client and at the transaction level. Banks design their data architecture based on the use cases defined by specific end users.
Network centricity requires that banks think more holistically about client relationships, not just between the client and its banks, but also the client’s relationship with its business partners. Who is the client’s largest and most important customers? Banks can discover the answer to this question by looking at incoming payments, their size and frequency, etc. Does the bank have a relationship with both sides of a given transaction, i.e., Company A pays Company B for goods and services? If so, then the bank can build a more robust view of the client ecosystem. This will place revenue growth on equal footing to analytics use cases in compliance, fraud and risk. The diagrams below give an illustrative view of payment transactions across the financial supply chain in the context of a client.
In summary, taking a network centric view helps bankers identify opportunities across several areas of value chain and have contextual conversations. For more information on how to use graph-based machine learning in your commercial bank, contact one of our experts.
Swaminathan Sundaresan is an Associate Partner at Infosys Consulting and is focused on driving digital transformation and analytics programs in the space of wholesale banking. Swami has worked with a number of leading global banks in delivering strategic programs at the intersection of Analytics and Digital transformation across the wholesale banking value chain
Marc Harrison is a senior principal at Infosys Consulting and is an innovation and strategy leader with more than 20 years’ experience in the financial services and technology industries. Marc helps design and build digital capabilities and leads engagements on a range of topics in retail and wholesale banking: data and analytics, omnichannel customer experience, digital marketing, mobile, product management and fintech strategy.