The challenge for those attempting to launch new payment alternatives has always been the need for an "ignition strategy". Simply put, it means making sure that there is a critical mass of payers and payees at the same time for the new payment vehicle. Attempts at achieving a payments "big bang" have often fizzled due to a basic circular conundrum. Users are reluctant to embrace a payment method, unless they are sure there is a critical mass of entities that will accept it. At the other end of the telescope, potential acceptors need to be convinced that the new alternative will have a large number of users. Many ideas have fallen between this chicken and egg.
Financial institutions are best positioned to break this deadlock. They have access to enormous amounts of data on the purchasing habits of their customers. Nevertheless, institutions have been slow, if not reluctant, to convert that data into useful insight through Analytics. The barrier is not technological. Powerful models exist that can sift through vast amounts of information to predict behavior, while keeping false positives to a minimum. Technology is available to bridge data silos through transaction hubs. The impediment to greater use of Analytics by financial institutions is driven for the most part, by concerns about consumer privacy.
While it is important to safeguard the trust equation that consumers have with banks, there are low hanging fruit that can be picked. Take P2P (person-to-person or peer-to-peer) payments for example. PayPal has been the runaway leader in this category, but banks are in position to ignite a P2P revolution. Most P2P payments replace cash or check transactions- paying friends, children, relatives, gardeners, babysitters etc.. Many of these are recurring payments that take place on or about the same date for similar amounts. In many cases, payers use their online bill pay systems to schedule payments which are sent as paper checks by financial institutions, because the receiving entities are not bonafide billers. In other cases, payers write checks and put them in the mail. In both cases, financial institutions bear the cost of paper handling. Checks also get lost in the mail, leaving the payer scrambling to get the money to someone in urgent need- I can attest to the latter, having had it happen repeatedly through "automated" bill pay.
It is possible to reduce cost and improve customer retention or "stickiness' with a little bit of analytical deftness. Technology exists to recognize payee names from check images. It is not difficult to analyze consumer payment histories to identify recurring payments to individuals. Similarly, an examination of online bill pay behavior can identify those consumers that are using checks to bridge the "last mile". These are consumers that may be receptive to P2P. Once target consumers are identified, financial institutions need to put incentive marketing in place to get them to enroll their payees into the P2P program. The payees are likely to react positively to overtures from someone they know. Handled properly, the targeting and enrollment of both payers and payees can be effected without raising privacy concerns.
This is but one simple example of using analytics as an ignition key to start emerging payments engines. There is more gold in them thar data mountains. What it requires, though, is a paradigm shift by financial institutions.
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