By Ryan Stewart, financial services lead at digital transformation specialist Cloud Technology Solutions,
In financial services, the need to embrace digital transformation has never been so pressing.
The increase in the number of new disruptors in the sector has been well documented in recent years. New entrants like Monzo have adopted technology in a way that’s allowed them to bring new and innovative customer offerings to market. As a result, many newer players have enjoyed success and eroded the market share of major financial institutions.
In response, the industry as a whole has recognised the need to digitally transform. The sector’s biggest players are realising that, in an environment where consumers have more choice, a business-as-usual approach is not enough. Failing to respond to the innovations transforming the sector could see slow-moving firms left in the wake of their trailblazing competitors.
Widespread digital adoption has accelerated the growth of FinTech, which now accounts for 33 per cent of financial services’ revenue share. Meanwhile, our survey of senior IT decision-makers found the majority of financial services firms are now using machine learning (ML) in areas like compliance and to improve customer experiences. But when it comes to the extent of the gains to be made here, the work done so far has barely scratched the surface.
Delving into the data
While digital adoption and the use of nascent technology such as ML in financial services is on the rise, there is still a huge opportunity in the sector for further innovation through data.
Currently, the application of data analysis tools has been limited to the structured data financial services firms hold. This kind of analysis has resulted in some genuinely innovative solutions being brought to the market, with ML being used to identify and block fraudulent activity in real time.
However, almost all financial services firms are failing to tap into the potential of their unstructured data, which accounts for an estimated 80 per cent of what they hold and includes things like audio files, emails and image files.
Only three per cent of financial services firms are using ML to analyse this data according to our research. This presents a massive opportunity for ambitious firms to vastly increase the volume of data they are interpreting. By analysing unstructured data, firms can gain significant advantages, such as the ability to identify patterns in customer liaison which could indicate when customers are at risk of leaving or defaulting on their payments. An insight that could help businesses steal a march on their competitors.
Looking ahead, collecting and interrogating unstructured data to unlock this kind of insight is set to become the next focus for the sector. By pivoting to unlock the value of their unstructured data, we can expect firms to uncover new levels of business insights, with potentially transformative effects for the way they operate and engage with their customers.
Many established firms face inherent disadvantages compared to their digitally native competitors and often the size and scale of their legacy IT infrastructure can hold them back.
Legacy IT poses problems because using old systems can result in slow data retrieval times and are often incompatible with new applications and technology. This creates a barrier for firms that want to use ML to analyse their unstructured data but can’t integrate the technology with existing legacy systems and have not yet adopted the cloud-based infrastructure needed to enable its use.
To some extent, this explains why many established financial services firms have been unable to match the pace of innovation set by cloud-native disruptors unbridled by creaking IT infrastructure.
Changing this is essential for those that aspire to remain market leading. While we are seeing greater innovation and new technology in the sector, there are still those with antiquated beliefs that cloud infrastructure could compromise data security. Ironically, this misconception can lead to firms committing to less secure on-site legacy systems, incurring significant operational costs and system downtime that could put data at risk.
However, attitudes towards cloud security have changed in the last two years. This is in no small part down to the robust security credentials of public cloud, which allow firms to benefit from the security protocols of major global providers like Google. As growing trust in cloud security converges with the economic imperative to innovate, data security concerns are likely to become less of a barrier to comprehensive digital transformation in the coming year.
Addressing the skills gap
To unlock the potential of their data, many firms also need to address an existing skills gap within their businesses. This means creating new positions for data scientists and building an understanding of these roles with employees. Firms will need to invest more heavily in data scientists who can build the data lakes needed to store and analyse unstructured data – rather than relying on off-the-shelf solutions designed to do this.
We are already seeing big players in the financial services sector invest in data scientists. Blackstone now has a 14 strong team of data scientists, an increase from having none at all five years ago, and has been able to use the team to help win new portfolio clients by providing unique data-driven insight into their markets.
Having this kind of expertise in-house is essential for firms looking to keep pace with the innovation being driven by data and technology in the sector. And with the majority of data scientists now familiar with working in cloud environments, the move towards investing in data specialists is increasingly tied to shifting from legacy IT to cloud services.
Data and the future of financial services
Looking back across recent years in financial services, there is no question that investment into digital transformation projects has shaped the industry we see today. Use of cloud services and ML is increasingly common across the sector – and there are some genuinely innovative examples of data analysis in the industry.
However, financial services firms cannot afford to rest on their laurels. While many have dipped their toes into the potential of sophisticated analytics and ML, the data held by financial services firms still holds a huge amount of untapped potential. To really benefit from this, firms need to turn their attention to analysing and understanding their largest pool of information – unstructured data. Wider adoption of ML and investment in leading practitioners of the technology is key to do this. If forward thinking firms can grasp this opportunity, we will see the industry transform in the next five years.