Published : 5 years ago, on 19/12/2019

By Elliott Limb, Chief Customer Officer, Mambu 

Elliott Limb

As the financial industry is growing more complex, it is becoming increasingly difficult for businesses to deliver seamless customer experiences and consistency across channels. Flexible, scalable and collaboration-based business models are the way to go and the technology to support that must quickly address this shift and give a platform for this unknown market.

Main challenges SaaS providers will face in 2020

• Building for a future you cannot predict – In this area of continuous innovation, how can we deliver the best customer experience and build for the future we don’t know. Today, to support their business models, companies need to build systems that they cannot even think of. Yet such systems may eventually become the BAU operation in the next 3 to 5 years.
•  SaaS is still in its early stages – The obvious answer to controlling a cost model that is managing risk and supporting growth, is a move to a cloud-based offering. However, there is a lot of uncertainty involved and businesses are waiting for evidence and proof points to trust that a fully SaaS model is going to provide the solution. While the cost base is obvious and a continuous delivery model is what they are looking for, many fear that there is a trade-off to risk. Besides, this is driven by the regulator positioning, and therefore differs per country.
•  Choice fatigue – Even when a business chooses SaaS and the regulator supports it, businesses find that the choice of vendors, architecture, approach and technology stack is too vast. Finding the right fit is becoming increasingly difficult.

2020 trends 

•  Customer centricity – A trend that has been gaining pace over the last few years, we really are seeing the customer put at the centre of business models and decision making. The entire concept of a return to relationship banking (possibly with a commoditised product delivery around it) is something that many banks are starting to embrace with their digital offerings. The AI space in this area will be very interesting to watch as individuals get what they need, when they need it and how they want it.
•  Collaboration – This is the power shift in the market, with a realisation that just technology collaborations or “bank-led” collaborations are not enough. There has to be alignment from strategy through to value realisation in the entire ecosystem for collaborative models to work.  As this develops the support to the customer experience and the ability to deliver better products faster at a lower cost will be key.
•  Flexibility – Composable business architectures. This is how the market will deliver a framework for collaboration in order to support customer-centricity.  We also call it ‘composable banking’ which refers to the quick and flexible assembly of independent systems. Having an architecture where everyone is “the best” at what they do, but with an easy integration that allows banks to build solutions and products for an unknown future is going to be a big differentiator.
•  Speedboats will become battleships – A lot of the composable banking and collaboration is happening in the start-up and neo-bank space, but the larger banks are launching or have launched “speed boat” digital banks to prove the model.  Over the next few years, these solutions will become the central process of the bank, removing the legacy and on-premise monolithic technology stacks and related business architectures with an agile, fit for purpose and composable solution that is adaptable to future business needs.
•  An even more advanced tech – AI, machine learning and cloud technology are all areas that are moving at pace. On the cloud side, we already see ecosystems built around platforms such as Mambu that enable banks to move to a complete SaaS model and therefore have a composable ecosystem at a predictable cost base, with future-proof business models. AI is only just warming up and I see this becoming more immersive in the customer journey, allowing bespoke experiences while allowing the banks tighter control of risk and cost, providing a more predictable and sustainable banking model. In terms of banking use cases, machine learning is even more in its infancy. In the coming few years I expect to see the continuation of operational automation, however, I believe there are several areas where this is yet to really make an impact (expect more movement towards customer-facing machine learning use cases).
• Customer intelligence – The rise of the Chief Data Officer and the AI models that facilitate predictive modelling of debt defaults (i.e. less risk on the loan books), spending patterns (targeted sales and savings) and many other use cases are offering a fascinating insight – data itself was never the key, analytics was. That realisation and shift in focus will drive the move to personalised banking and will become one of the main drivers of banking transformations that the industry has ever seen. Having a business and technical architecture that supports this unavoidable acceleration of innovation and gives flexibility in delivering future, currently unknown, customer experiences will be the platform for growth that banks will need.