Article by By Fiona McNeill, Principal Product Marketing Manager, Red Hat
Incumbent banks should know they have to modernise their organisation to compete in a world where customers want better and more personalised digital experiences. Eager to realise the cost-savings and increased revenue that can result from micro-targeting products and services, they can adopt next-generation technologies to transform their businesses to lead their market.
Digital leaders are focused on end-to-end customer experiences. Processes, policies, and procedures defined for branch networks are being reimagined to support new digital customer engagement. By modernising the back office and business processes, banks have an opportunity to streamline, codify, and thereby automate – which, in turn, can reduce friction caused by manual checks and inconsistent policies. This can enable more seamless customer experiences and speedier customer service, with transparency into servicing while reducing operational costs.
Artificial Intelligence (AI) is one of the leading digital technologies that’s captured the attention of financial services firms. While a number of use cases have emerged, one at the top of the list is its ability to help detect financial crime.
With increasing stores of event data, banks are challenged to analyse it given the old ways of storing, then analysing data. Modern technology can help discover and predict anomalies in data without storing it first. Ultimately the goal is to do real-time detection as triage to help minimise the number of false positives investigated.
According to an article from Deloitte , it is the cognitive capabilities associated with machine learning and natural language processing that are expected to make fraud detection models more robust – stronger and more accurate. As described by the Cognitive Computing Consortium, by their very nature cognitive systems can be distinguished from other forms of AI in their ability to adapt and learn from iterative human interaction.
Ultimately, it is the results that matter, reduction in false positives of 95 percent to 50 percent, along with a reduction of 27 percent in manual effort have been cited in a case using modern machine learning techniques – helping discover the undefined unknowns in data. However, it remains to be seen how much better over time these systems will become if AI and cognitive systems come together, with experts who can label data and teach the algorithms iteratively, like that of machine learning techniques in which an algorithm seeks to maximise a value based on rewards received for being right.
We are seeing financial firms marry operational efficiency efforts with AI/machine learning/cognitive computing – creating an additional layer of automated insight that is designed to optimise bank service processing. Part of that optimisation can also come from hybrid cloud adoption, in which AI and machine learning models are available to operational systems in the data centre and/or in a public cloud.
Native cloud adoption can include the use of Linux containers containing the libraries, dependencies, and files teams need, and these containers can be spun up and down on-demand. Just imagine: analysts can define the rules that automatically execute business decisions, informed by insights from embedded algorithms. Those algorithms, in turn, are part of the pre-approved library defined by AI and domain experts. All of this could be from a self-service environment that doesn’t require your technology organisation to spend time provisioning the tools, the data, or the processing capacity.
Of course, bringing these kinds of capabilities into new products beyond operations is within the realm of open banking. More banks seem to be realising the value of co-creating products and services to expand their market reach to help them achieve new value streams. Combining back office operational efficiency and embedded intelligence with data sharing via open banking APIs should further propel digital leadership in financial services.
These technologies hold much promise, and banks should understand they need to rethink their technology investments to include them. But knowing what they need to do and figuring out how to do it can be two different things. Banks will have to be sure to aim and hit the digital high points that best fit with their long-term business plans aligned to customer journeys at the core. Today’s dynamic customer environment should only continue, with new entrants and new ways of providing banking services. Perhaps the most prudent strategy is to plan for change.
These technologies have one thing in common. A successful return on technology environments that are mutable to business needs often depend on a willingness by the firm (and its leaders) to accept the cultural, process, and policy metamorphosis necessary to make them – and the larger digital transformations they can facilitate – work. This is a culture change for a traditional long-standing industry.
It’s going to be challenging to digitally transform banks, yet a path must be chosen and navigated, all while the banking landscape continues to change.