Ash Finnegan, digital transformation officer at Conga
Many firms across the financial services sector have felt the pressure to ‘digitise’ over the past decade or so, in order to keep up with consumer demands as well as the rise of fintech rivals and the latest challenger banks. As revealed in a joint report by the Alan Turing Institute and Bank of England, institutions have felt the need to invest in the latest artificial intelligence (AI) and automation solutions in order to stay competitive, and machine learning (ML) adoption has increased considerably since 2008.
Back in 2019, two thirds of companies reported they already used ML in some form, and this was expected to double over the next three years.1 However, the pandemic has brought a new sense of urgency. In fact, according to The World Bank, COVID-19 has completely changed how consumers interact with financial services and banking providers, with digital asset exchanges and online payments increasing by as much as 13 percent.2 Similarly, as a result of social distancing measures, many customers were unable to visit local bank branches, which placed a heavier burden on contact centres and core systems.
Out of necessity, digital transformation journeys across the sector have accelerated exponentially, with automation being more of a focus. Financial leaders have invested heavily in artificial intelligence and wider automation technology, digitalising customer channels and entirely restructuring their back office in order to deliver their services remotely – chaos has been the driver of change. In fact, an earlier report by IDC revealed that 83 percent of banks in EMEA are still focused on business continuity and building resilience into their operations.3 However, that does not mean that each of these automation programmes have been well executed or delivered effectively.
How to approach automation
Many financial leaders have struggled when it comes to scaling AI technologies across their organisation. As a report by McKinsey revealed, the most common obstacles hampering banks’ automation efforts, are the lack of a clear strategy for AI, weak core technology and data backbone, and an outmoded operating model.4 In general, initiatives are rushed or short-sighted, and COVID-19 has only accelerated this issue. As Conga’s own research indicates, whilst the pandemic has accelerated 71 per cent of companies’ digital transformation plans, only 36 per cent of these initiatives have proven successful.
Most aspire to be disrupters, picking a technology and implementing it at speed in order to keep up with competitors. They want to adopt the latest AI programme, be that robotics process automation (RPA) or natural language processing (NLP), with no real idea of how this will improve their overall services or operational model. Whilst AI offers many competitive advantages, that does not necessarily mean it is easy to implement or deliver as part of a wider transformation project.
AI is only as good as the data provided and if there are bad processes in place, particularly between departments – whether contracts or loans managed by the sales and legal teams – automation will only accelerate this issue.
Establishing ‘digital maturity’ – when and how to adopt AI
Before considering any new or transformational technology, firms need to review their current operational model and establish where they currently stand in their own digital transformation journey, by considering their own digital maturity. Given the speed at which most institutions adjusted to remote working last year, departments may have stumbled across a number of bottlenecks or unnecessary processes, and this will have affected overall workflow.
By taking a step back and reviewing the operation model, leaders will have a clearer picture of the current state of their business, and what the next stage of their digital transformation journey should be. After identifying any operational issues and reviewing legacy systems, leaders can then establish clear objectives, whether that is improving customer service and speeding up response times, or unifying systems of record and streamlining data flows between teams. Only then can leaders consider incorporating AI or automating elements of their business, streamlining the processes that matter and helping them to achieve these goals.
As institutions proceed along their digital transformation journeys, they will streamline processes, break down silos and enable cross-team working across departmental boundaries. It is vital that at every stage of that journey, leaders fine-tune the basic workflow to ensure any inefficiencies are removed.
AI is not a silver bullet or a ‘quick fix’
If companies think automation will solve all their problems, they are approaching transformation all wrong. Financial services firms, just like any other business, need to fully optimise their commercial operations process, before considering any new technology. It is crucial that leaders and IT teams establish the company’s digital maturity – where they are and where they need to get to – and review the operational model throughout every stage of their digital transformation journey, from foundation to full system integration.
Furthermore, they need to consider each phase of their operations – front to back office. By streamlining their operational model and unifying systems of record, companies will have far greater insight into data streams, and this will empower AI, taking their business to a true state of intelligence.
