Beyond basic customer and account details, banks have a wealth of information just waiting in their payments data. This data paves the way to developing a dynamic understanding of their commercial customers and their supply chains, moving away from static sources such as firmographics and reported financials.

 

Despite having access to all this information in their payments data, banks struggle to extract real value from it. They’re typically limited to ad hoc analysis of historical data extracts, with little operational use of this data. By applying dynamic graphing capabilities, banks can analyze payment networks in real-time to generate timely insights and enhance operational decision-making. 

 

This dynamic understanding of customers is critical to enable timely and meaningful engagement with clients, and the COVID-19 pandemic has served as a reminder of how quickly the commercial environment can change. This rendered many traditional, static data sources (such as historical reported financials) and machine learning models obsolete, but payments data continued to provide timely insights into company performance to drive proactive client engagement and support. 

 

 

Static View with Limited Use of Payments Data

 

Generally speaking, customer and product data is more readily available to a bank’s analytics and data science teams. It’s straightforward to manage, lower in volume, and therefore easier to process. On the converse, more complex payments data is often spread across different systems. It’s very high in volume and has limited data on originators and beneficiaries, making it difficult to process, understand, and generate useful insights from.  

 

The challenges in generating value from payments data often exist in the way banks manage it and the tools at their disposal to process it: 

  • Payments data tends to lie in multiple data stores, each of which provides only a partial view of a client’s overall activity and is difficult to connect. 
  • Banks lack the graphing capabilities required to map out data relationships and build an understanding of supply chain networks. 
  • The large volumes of data are difficult to process. Based on data extracts, banks can only do ad hoc analysis, which lacks any real-time capability to support timely customer engagement. 

 

These issues keep banks from looking beyond the simple analysis of a set of transactions to build a dynamic, network view where they can extract more valuable insights. At most, for example, they can profile a customer’s payments by simple attributes like value, volume, type, region and currency, or identify top originators and beneficiaries. However, the most crucial element that’s missing is an understanding of who the counterparties are and the context of the broader supply chain.  

 

Dynamic View with Supply Chain Analytics Insights 

 

Banks need a customer intelligence platform that connects their payments data to their broader internal and external datasets to create a holistic view of customers and supply chain networks. They also require dynamic capabilities to generate timely insights and keep these networks current as payment data changes.  

 

With this holistic view, banks also gain an enriched view of payment counterparties that includes: 

  • A company’s activity, industry, and financial performance 
  • Any related news articles  
  • Whether they’re an existing client and any products they’re using 
  • Any other organizations they transact with 

Understanding this information creates a better sense of the type of businesses a bank’s clients are dealing with. By analyzing the payments network for a particular client, they gain insights into the broader supply chain, including: 

  • Key suppliers and buyers that a client interacts with  
  • Supplier’s suppliers, the buyer’s buyers, and further hops through the network 
  • Overall status and health of the supply chain 
  • Identification of clients and non-clients across the supply chain 
  • Cashflow predictions based on historical payment behavior across the network 
  • Connections to significant events (such as company liquidations) and to high-risk organizations (such as sanctioned entities).

 

 

The result is a holistic view and understanding of the supply chain network dynamic knowledge graph that connects all clients and non-clients based on payments relationships, enriched with internal and external data. Based on changes in the payments data, the bank can flag clients it wants to engage with in a more dynamic and proactive way, such as in the following examples: 

  • Business growth: If a supply chain is healthy and growing, a bank can try to acquire more customers across that network and offer them relevant products.  
  • Cashflow: A bank can predict customer cash-flow issues, enabling proactive engagement with customers to offer relevant support and solutions.  
  • Tailored Trade and Supply Chain Finance solutions: A bank can provide its Corporate clients and their suppliers with bespoke solutions which optimize working capital and unlock preferential pricing for Corporate buyers, allow early payment for suppliers and mitigate risk of failure across the supply chain 
  • Credit risk: Analysis of second-order and third-order supply chain connections and payment behavior can provide early indications of credit risk to allow early intervention.  
  • Financial crime: By analyzing payment networks, banks can identify patterns in the flow of funds that they can then use to detect money laundering and highlight connections to known bad actors. 

Linking information to provide these types of insights is possible only with a customer intelligence platform. 

 

Customer Intelligence Built on Entity Resolution, Network Generation, and Analytics 

 

Customer intelligence platforms create supply chain analytics in the following way:  

  1. Create a single, enriched view of customers and counterparties. Entity resolution creates a single view of customers and counterparties by processing poor quality and sparse data. It even handles the limited data that’s available in payment records to connect to broader internal and external datasets
  2. Build a data network. Network generation provides a scalable graphing capability to build the full supply chain network and understand the broader customer context described previously.  
  3. Analyze supply chain networks to generate insights. With the analytics framework, banks analyze supply chain networks in batch or real-time to automatically flag risk or opportunities according to changes in behavior or the broader supply chain. It takes banks beyond a one-off, ad hoc view to a dynamic customer understanding that prompts proactive engagement.  

 

Banks can deploy this customer intelligence platform on-premises or on a private cloud and integrate it with other systems, such as customer relationship management, customer lifecycle management, or marketing automation tools.       

 

​Read more about how we help financial institutions harness the power of their data.

 

Dynamic Understanding of Customers from Supply Chain Analytics 

 

Analyzing data that’s updated only every week, month, six months, or year isn’t enough for today’s banks. The COVID-19 pandemic has shown how quickly customer situations can deteriorate as supply chains break down. That reliance on static data isn’t sufficient to pre-empt or quickly catch these scenarios as they unfold.  

 

A customer intelligence platform provides continuous supply-chain monitoring to generate timely insights that drive proactive management of client relationships. By using this platform, banks more effectively manage risk, identify new growth opportunities, and improve client experience through timely, meaningful engagement.  

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