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Straight Talk Technology


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Straight-through-processing (STP) has been a consistent objective in financial services operations and technology strategies for many decades. The ability for transactions to be dealt with through automation allows institutions to scale efficiently, allowing them to create operating “jaws” where revenue growth increases faster than operating costs. The importance of automation has only accelerated in recent years, with the shift in customer interactions to digital channels creating a demand-led requirement, as customers expect the near real-time speeds that automation allows.

The challenge for banks is that while automation is relatively straightforward for high-volume, standardized, and structured transactions (such as card or ATM payments), it is generally trickier for low volume, variable, task or document-led transactions, particularly if there is an element of judgement/expertise involved. In such situations, the costs of developing, but perhaps more significantly, maintaining the required automation often outweighs the benefits. Institutions often end up with people-led processes supported by elements of automation, such as workflow or productivity tools, or stitching together of applications that span across a business process.

The prevalent approach of most institutions to date has been to focus on “routine” transactions first for automation, encouraging customers to go through digital channels for self-service. With the 80:20 rule applying in most situations, it clearly makes sense to focus on higher volume areas first, particularly the where the degree of commonality in processing is high. The aim being to automate processes for the majority of transactions, focusing manual efforts on dealing with exceptions. The subsequent focus has been on exceptions management, with aim to increase STP rates. However, there is often a trade-off in the increasingly the level of automation with adaptability, as increasing the complexity in automation rules significantly increases the maintenance and change overheads.

The impact of the recent COVID-19 lockdown on the banking sector is a good case in point here. With most governments creating the requirement for institutions to provide some form of loan payment break to affected consumers/businesses, there has been a massive surge in loan modification applications. For most banks such requests sit outside what is considered “routine,” with typically volumes relatively low and change requests fairly bespoke. Consequently, banks have struggle to cope. This is in both the ability to deal with initial application volumes—as queries initially routed primarily through the contact center rather than digital channels—and then in operational capacity, as systems were generally not set-up to support such non-standard loan modification requests or deal with the scale of applications.

For banks, there has unsurprisingly been a rapid reprioritization of automation efforts towards this, illustrating the point that platform adaptability in automation should be given a high weighting in systems selection. While delivery of high STP rates is important, the ability of platforms to cope and support change effectively is fundamental to long-term success.

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