Analytics in Payments SystemsSubmitted by admin on Thu, 2017-11-16 02:32
Analytics in Payments Systems: Wealth of Data
Payment providers all over the world have a wealth of data. This data is also unique because it can reveal valuable insights into merchants as well as consumers, and the relationships between them. Indeed, a lot of providers have tapped into the opportunity to mine this data with advanced analytics. This opportunity has taken them beyond the traditional reporting needs of a payments business to generate insights ranging from customer behaviors to fraud patterns. These insights have in turn benefitted the client businesses of the payment providers. One reads of analysis of payments data helping businesses reach out to untapped market segments or start a new revenue stream through product cross-selling.
So far, payment providers have tapped into this opportunity predominantly by running analytics out of historical data. Even if the objective of analytics has been to predict outcomes in a real-time context, the analyzed data has always been data at rest. Payment providers however, provide their services near real-time - at least the first two steps of a typical payment process i.e. authorization and posting, happen in real-time across a lot of payment systems. Hence, given that the line-of-business data of a payments business is composed of data streams, there is also an opportunity to analyze data as it enters into and travels through the payments system before landing up on a hard drive.
Streaming Analytics: Suited for Real-time Payments
Such an analysis done close to the source of data creation has been variously termed as streaming analytics or real-time analytics or the analytics of data in motion. Streaming analytics has seen a lot of adoption in the financial services over the last few years. This is because this industry has seen rapid augmentation of new data streams. This has led to fundamental and expeditious shifts in operational procedures within this industry. Retail banking operations, for example, are nowadays built to analyze and predict every customer transaction as it takes place on any of their digital banking channels.
Streaming analytics is a powerful way to get real-time insight from a digital payment and identify it as a possible opportunity or a risk. The utility of such an insight has a short life but it provides the immediate context for real-time decision making, something immensely valued these days in the highly competitive financial services industry. While risk was always perceived as the ‘metric’ that had most relevance in a real-time context, these days even opportunity is being capitalized in real-time by marketing teams with cross-sell or upsell actions.
Still steaming analytics is not just about automating operations. In the payments industry, it is the shift to a ceaseless and real-time view of the payments system in its entirety based on which the system stakeholders can take real-time decisions with as much confidence just as they would based on backend algorithms running for hours or days. Hence, the possible use cases for streaming analytics would emerge broadly from the very functions of visibility and control in a payments network e.g. continuous payment monitoring, continuous liquidity management, continuous risk management, continuous financial planning, and continuous performance management.
In this paper we propose selected use cases for the application of streaming analytics for providers of payment systems, specifically financial institutions in the U.S. offering the Automated Clearinghouse (ACH) payments to their business and individual customers.
But first, let's look at the ACH ecosystem today.
FedACH and EPN: Automated Net Settlement Payments Ecosystem
Worldwide most countries have followed a dual system of a real-time gross settlement system (RTGS, e.g.Fedwire) and a net payments settlement system (e.g. FedACH and EPN). The net payments arrangements catering to the high-volume low denomination segment of payments, are the result of years of evolution mainly driven by the effort to reduce transaction costs. Unlike RTGS, net settlement arrangements do not face an explicit intraday liquidity constraint, rather, they often allow a commercial bank to clearly trade-off between the opportunity cost of holding idle liquid funds for settling future payments and the cost of 'losing' clients because they are unable to process their payments.
The net settlement system in the U.S. is provided by two different Automated Clearinghouse (ACH) operators, the Federal Reserve Banks' Automated Clearing House(FedACH) network provided by the Federal Reserve Banks, and the Electronic Payments Network (EPN), the only private owned network provided by The Clearing House Payments Company L.L.C. Both the networks are administered by the National Automated Clearing House Association(NACHA) though the rules applying to the two networks differ to some extent. As of 2017, EPN managed approximately 50% of all commercial ACH volume in the U.S.
(Figure 1. Illustration to be rendered based on above content. The above diagram is only for reference, not to be reproduced)
FedACH or EPN, the networks work on the same ACH principles as illustrated in Figure 1. An individual or a firm or any other entity, the Originator, originates either a Direct Deposit or Direct Payment by placing a request with their Originating Depository Financial institution (ODFI), typically their bank. All ACH transactions transmitted electronically are either debit or credit payments. The ODFI enters the ACH entry and aggregates all such entries from its customers. At regular predetermined periods, the ODFI transmits them in batches to either of the two ACH Operators. The ACH operator receives many such batch entries from all ODFIs. It sorts the ACH transactions and then makes batches of these transactions available to the Receiving Depository Financial Institution (RDFI). The RDFI credits or debits the Receiver’s account, who could be an individual, firm or any other entity. As of September 2017, both ACH operators offer same day credit and debit transactions, i.e. transactions are completed the same day they are initiated.
Direct Deposit has been used commonly for payroll, government and Social Security benefits, mortgage and bill payments, online banking payments, person-to-person (P2P) and business-to-business (B2B) payments, to name a few.
Over time the ACH networks have built in various capabilities including near real-time payment status visibility, automated exception handling, and same-day processing.
In 2016, NACHA reported enabling more than 25 billion ACH payments and having moved $43 trillion in funds. This was a 5.4 percent growth in volume of ACH transactions over 2015 and a 5.1 percent growth in funds moved over the same period.
In 2017, physical checks accounted for almost 50 percent of overall payments (down from 63 percent in 2014); ACH 32 percent (up from 22 percent in 2014), cards 11 percent (up from 8 percent in 2014), and cash and wire 8 percent (up from 7 percent in 2014). By 2020, ACH is estimated to account for 45 percent of payments, while checks will fall to 34 percent, cards 12.5 percent, and cash and wire 8.5 percent. (Source: Credit Research Foundation (CRF))
Also, as a comparison, the cost to process an accounts payable item through ACH costs lesser than to process by Checks or a wire transfer payment.
In September 2016 NACHA launched the optional Same Day ACH credits, by July 2017, 42 million of such transactions had taken place with a corresponding total of $57 billion. Following the Same Day Credit payments, NACHA launched the Same Day Debit payments in September 2017. Of the total Same Day ACH volume Direct Deposits accounted for 52 percent, business-to-business (B2B) transactions made up 32 percent, person-to-person (P2P) payments constituted 13.5 percent and consumer bill payments made up the remaining 2 percent.
By 2013, Direct Payments for Bill Pay through AXH networks exceeded bill payments through credit cards and mailed payments combined.
Streaming Analytics Uses Cases for the ACH Payments System
While proposing these use cases, we believe that the streaming analytics of the ACH data streams will open up new data monetization possibilities for providers especially useful when returns from payment services are under margin pressures. The value from such analytics offered to their customers will also address challenges arising from commoditization and lack of differentiation in the ACH offering. Though not limited to data monetization, streaming analytics use cases can also address the objectives of ODFI/RDFI to meet customer expectations, lower transaction costs, improve operations efficiency, raise margins, meet NACHA guidelines and reduce financial risk.
Given the value ACH providers could deliver to their customers, rather than giving access to third party players (usually startups) who do not own the primary customer relationship, ACH providers will greatly benefit by implementing the data monetization use cases in-house. These data monetization opportunities could be offered to existing business customers through APIs either bundled with the ACH service or stand alone.