The days of “business as usual” are over for financial institutions. Retail banks, insurers, investment firms, and wealth management companies alike are all under pressure to deploy new “anytime- anywhere” digital services. Not surprisingly, according to Ovum’s recent ICT Enterprise Insights, the banking sector recorded high digitization maturity-progression of 42 percent, second only to the telecoms sector at 43.9 percent.
The most evolved enterprises know the potential of their data as a competitive lever and for insights into their customers. They also know that not all data is created equal. To build the most modern IT environments and derive the maximum value, there must be an acknowledgment that data types have different needs for access, storage, and management. This is where there is some catching up to do in the finance sector. To survive and thrive in today’s era, financial organizations must become 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 source—including IoT devices―financial services firms can now accelerate processing, adopt automation, deliver personalized 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.
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.
Every type of investor faces constant pressure to improve performance and top-line revenues. To address this pressure and keep pace with ever changing markets, more firms are deploying models powered by analytics that produce AI and ML driven insights. In fact, according to a report from Accenture, AI will contribute US$1.2 trillion to the financial sector by 2035.
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 optimize 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 optimize 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, organizations 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. To cite an example, 78 percent of Swedbank’s contact center interactions are resolved on first-contact by AI. 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 US$270 billion annually. In addition, according to a recent Thomson 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 percent of respondents to the same Thomson 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 competitive marketplace, innovation is now more crucial than ever. Digital transformation and capitalizing on data provide a vital opportunity for organizations to focus on emerging 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 specialized external sources. It also requires a financial institutions’ data platform to collect, aggregate, and process data in any format.
The starting point for all this is a high-performance, highly agile data storage platform. Modern IT environments should consist of data strategies based on flexible consumption models across on- premises, hosted, and public cloud—aligning application workloads with the most effective infrastructure. Importantly, modern IT environments should work harmoniously with a common management interface, 100 percent non-disruptive architecture and proactive/predictive support services. This kind of infrastructure will be able to share data from a multitude sources and facilitate the creation of data pipes for AI workloads.
It’s time to put your organization’s data to work.