Being somewhat of a closet Parrot Head (a fan of easygoing singer-songwriter Jimmy Buffett), I find it amazing where my brain takes me. In a recent focus group of accounts receivables managers, while they discussed the pain associated with cash application, my brain suddenly retrieved a line in Buffett’s song, “Fruitcakes,” which goes: “Tell me what’s goin on; I don’t gotta a clue.”
Meeting participants recounted their daily difficulty with making sense of the vast amount of data and the efforts to update their accounts receivables systems. They talked about possessing no control over the manner in which buyers send the remittance data associated with their payments.
How does that relate to this article? My previous article, “Data, Data Everywhere…Where Do Banks Start?”, highlighted the challenges that completely unstructured and random transaction data spark when banks’ analytic engines are designed. The challenge is easily aggregating the data and then applying big data once it’s captured. This requires justifying the expense to solve both customer and internal data problems and address regulatory compliance requirements when budget dollars are scarce. This situation only can be resolved if a bank’s internal data needs are tied with those of the customer.
Polar Opposites Attract
A customer’s data needs start with the underlying bank transactions. A bank’s internal data needs begin with the aggregation of the data into meaningful information (i.e., total transactions processed to determine a customer’s bill; aggregated data for developing a product-pricing model; payments trends of customers for either lending or compliance needs; etc.). And these needs couldn’t be more opposite.
What practical examples emerge of spanning the data needs of customers and a bank’s data needs? How do you serve both masters to generate the funding required for the necessary investments?
A Journey Starts with One Step
Continuing the accounts receivable example, practical automation tools exist that a bank can provide its customers to reduce some of the manual effort associated with organizing and applying their data to an A/R system. For one thing, the data can be aggregated for deeper analysis to address some of the systemic issues, including sources of poor data, which arise. This analysis can yield opportunities for the customer to improve its receivables process.
Consider drawing meaningful insights from such measures as the percentage of payments by payment type seem rather rudimentary. But deeper value begins to emerge when compared to the performance of other companies within their industry. This greater insight derives when comparing remitters’ (buyers’) payment patterns with each customer (seller) to that of other bank customers. This delivers an actionable benefit to the customer to prod them to negotiate better payment terms with their buyers.
In the process some interesting insights are learned and the banker/client relationship is fundamentally changed. Envision this conversation from a banker with their manufacturing customer:
“As a result of analyzing your payments, we looked at your top ten remitters. When we compare your payments versus our other clients from the same remitters, we see that on average you are being paid two days later.”
This is the type of insight that on its face alone is potentially transformative for the client but opens the door for discussions by the banker to directly pinpoint solutions that benefit the customer. The effort to achieve this level of transaction data abstraction through mass data technology sets the foundation for further benefits to the bank.
Climbing the Hill
Corporate lending, especially asset-based lending, relies heavily on a company’s financial statements to determine the loan-commitment amounts. In secured-receivables financing, it’s common that a lender request that the company have its receivables transactions processed by the bank, ostensibly to increase the bank’s visibility to the borrower’s payment flows. Beyond the comfort of the deposit being made with the bank, little is done with the transaction data unless the receivable is discounted.
As secured-receivables financing has matured into supply-chain financing, transaction data possess new value. Harvesting a customer’s underlying transaction data provides the ability to identify receivables investment pools to provide financing alternatives. As an example, segregating the receivables data by the investment grade of the buyer provides the seller the ability to reduce the cost of receivables financing by using the credit rating of their buyer. The underlying data continues to be harvested as it replenishes syndication pools or auction sites.
Again back to our banker/client conversation:
“In addition to noticing the days sales outstanding improvement we uncovered, we noticed you have a large concentration of receivables from buyers with attractive credit ratings. I think we should explore how this can be turned to your advantage and improve your funding costs.”
This type of conversation is directly juxtaposed to an open ended dialogue initiated by the banker which has no fact-based direction.
Reaching the Top
In these rather focused examples, the development of a data strategy highlights the ability for each of the stakeholders to receive incremental value with each investment. The customer initially gains insight into their current receivables patterns and deeper insight as investment alternatives are defined. And, while no bank is sitting back eating cheeseburgers in paradise, a well-defined data strategy moves the bank from a pure transaction processing provider to a value-added partner.
Lawrence F. Buettner is a senior vice president at Wausau Financial Systems, which provides receivables technology for financial institutions. He has 30 year of experience in financial treasury management, and was an SVP at First Chicago.
[Check out ways companies are Navigating the Big Spectrum of Big Data’s Solutions at Interop, which runs from September 30 through October 4 in NYC.]