• James Petter, VP International at Pure Storage


• 02.07.2019 01:45 pm
Digital transformation

Digital disruption is occurring at every level of the financial sector. Retail banks, insurers, investment firms, and wealth management companies alike are all under pressure to deploy around-the-clock digital services, or risk falling behind competitors. An overcrowded market, new technology, and rising consumer expectations are forcing financial institutions to pursue large-scale change efforts. It is therefore unsurprising that EY’s Global Banking Outlook survey finds that 85 per cent of banks cited implementation of a digital transformation program as a business priority over the past year. While 62 per cent of global banks expect to be digitally mature in 2020, compared with just 19 per cent in 2018.

The most evolved enterprises recognise the potential of their data as a competitive lever, offering valuable insights into their customers. They also know that not all data is created equal. To build the most modern IT environments and reap maximum value, there must be an acknowledgment that data types possess different requirements for access, storage, and management. This is where there the finance sector lags behind. To stay ahead in today’s era, financial organisations must become ‘truly’ data-centric for managing all key tasks, from customer-facing services to internal back office operations.

By adopting a data-centric approach, using application performance transformation, and by consolidating critical data from multiple sources—including IoT devices―financial services firms can now accelerate processing, adopt automation, deliver personalised experiences, and slash costs. Much of this will be achieved through artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and advanced analytics.

Tapping into these innovations, however, requires highly scalable, high-velocity access to vast amounts of customer and business data. This all begins with employing modern data strategies in four critical areas.

Boost Performance

To accelerate their digital transformation journeys, financial enterprises require a data platform that can consolidate, connect, and accelerate data from both historic and current data sources. The platform must have the capacity to accommodate streaming data from IoT devices and telemetry. It must also deliver analytics and AI/ML applications on-demand.

Financial services firms are now deploying data hub platforms to accelerate data in a way that improves both performance and profitability. It has enabled some, in fact, to reduce database job latency to below 1ms.

Improve Intelligence

Every type of investor faces constant pressure to improve performance and top-line revenues. To address this pressure and keep pace with constantly changing markets, more firms are deploying models powered by analytics that produce AI and ML driven insights. A research report by PwC UK revealed that UK GDP could be around 10% higher in 2030 as a result of AI, and the biggest sector gains will be seen in financial services, as well as retail and healthcare.

As examples, quantitative analysts are sifting through big data, using AI and ML to create more immediate investment strategies that identify profitable opportunities and balance risks. AI, ML and predictive analytics also add new capabilities to improve forecasting and optimise trading decisions. But none of this can be done without fast processing of large volumes of data from multiple sources.

Firms need scalable storage for constantly growing data volumes. They also require high-performance processing to optimise machine analysis and get human beings the answers they need.

Reinvent the customer experience

Online and mobile banking customers demand convenient services, whenever and wherever they want them. For financial institutions to stay competitive, delivering such experiences is now strategically critical and customer loyalty depends on it. The opportunity now exists to reimagine the entire customer experience, from core banking to mobile to call center. With analytics-driven ML, organisations can transform their retail applications, including conversational commerce, voice interfaces, and virtual assistants.

Financial firms can now obtain more value from the data they store and protect. They are now embracing AI, ML, and predictive analytics platforms to leverage their vast data sources, add new value, and deliver experiences that help them attract and keep customers. In fact, Santander recently partnered with IBM to drive business transformation. The new AI, blockchain and big data offerings will help the bank improve response time by predicting 75 per cent of specific system failures, understand online user behaviour and preferences, and reduce up to 30 per cent of client queries through online services powered by Watson. Data storage must be able to keep up with these new capabilities by offering fast access to all data resources with scalable processing power.

Transform Governance, Risk & Compliance (GRC)

Regulatory compliance is now a huge expense for financial institutions. Analysts estimate it costs the banking industry £211 billion annually. In addition, according to a recent Reuters report, compliance and risk practitioners expect that price to climb. Stopping fraud and criminal activity remains a top, yet expensive, priority. As does risk management. Both require more immediate access to data and intelligence. Consequently, many firms are accelerating the identification and reporting of liquidity, counterparty, market, and credit exposures.

The ultimate challenge, however, is to make all data immediately available to all applications in a cost-effective way. Financial firms can’t afford to devote more people to these problems. Instead, many are turning to new regulatory technology powered by AI and ML to automate some of the processes and complement existing systems.

In fact, 45 per cent of respondents in the Reuters study expect to invest in automated GRC solutions by 2021. These solutions enable a comprehensive governance, risk, and compliance automation strategy that operates across all functional silos and delivers round-the-clock compliance monitoring, scalable capabilities, and lower costs.

If financial services firms are to differentiate themselves in the fiercely competitive environment, innovation is critical. Digital transformation and capitalising on data provides a vital opportunity for organisations to shift focus onto changing customer needs, uncover deeper insights, and mitigate risks when launching new services. This, however, will require the use of data enrichment with “alternative data” from geolocation, demographics, medical records, retail foot traffic, and other specialised external sources. It also requires a financial institutions’ data platform can effectively collect, aggregate, and process data in any format.

The key to unlocking digital success starts with a high-performance, highly agile data storage platform. Modern IT environments should prioritise data strategies based on flexible consumption models across on-premises, hosted, and public cloud - aligning application workloads with the most effective infrastructure. Most importantly, modern IT environments must  work seamlessly  with a common management interface, 100 per cent non-disruptive architecture and proactive/predictive support services. This kind of infrastructure will be able to share data from a multitude of sources and facilitate the creation of data pipes for AI workloads.

Now is the time to put your organisation’s data to work.