Deloitte opines that a new global economic order seems imminent in 2023.
The ripple effects from a more fragile and fractious global economy will be felt disparately across the global banking industry, noted the consultant.
“Over the long term, banks will need to pursue new sources of value beyond product, industry, or business model boundaries. The new economic order that will likely emerge over the next few years will require bank leaders to forge ahead with conviction and remain true to their purpose as guardians and facilitators of capital flows. Banks should be bold and stay ahead of the curve, proactively shape emerging forces, and envision the possibilities beyond the current fog of uncertainties.”
Deloitte
In its 2023 banking and capital markets outlook, Deloitte says banks are eager to seize the seemingly limitless potential of new and disruptive technologies. At the same time, however, they remain vigilant against introducing emerging risks.
Limitations of legacy systems
With the global financial services industry (FSI) growing by 9.9% in 2021 and expected to reach US$28.6 trillion in 2025 according to the Business Research Company, financial services organisations, who often rely on legacy technology, need to modernise their data platform to improve their capabilities in storing, accessing and analysing massive real-time data.
Harry Ault, worldwide leader of field and field engineering at DataStax, says many organisations today are drowning in the amount of data they generate. However, unlike ordinary businesses, financial institutions face this deluge while having to navigate stricter governance, regulation, and monitoring.
“The ability to extract value from data will provide a significant business advantage in areas such as personalised marketing, new business segments, new product offerings, automation of processes, and finally, fraud detection and prevention.”
Harry Ault
He posits that when financial institutions (FIs) can leverage real-time data or data in motion in an instant, they can make intelligent and automated decisions resulting in superior customer experiences. “This empowers financial institutions (FIs) to be more competitive in the marketplace with new and improved real-time services that meet and exceed customer expectations through seamless omnichannel banking platforms,” he continued.
This is particularly important to financial institutions, as the World Retail Banking Report by Capgemini and Efma revealed that FIs are lagging in personalised experiences despite increasing consumer demand for rewarding and engaging experiences.
Ault believes that for financial institutions to deliver seamless real-time experiences, investing in a multi-model, distributed database is no longer a 'nice-to-have' luxury.
Enabling low-latency and high-throughput data, FIs can revamp the experience from the bottom up, eliminate buffering, and ultimately deliver a cohesive, seamless, and superior customer experience. The right digital initiatives will position FIs with the right competitive advantage in any kind of competitive environment.
Data challenges facing FSIs
Ault contends that reliance on legacy technology has resulted in FIs’ data being locked in various silos that do not interact with one another; limiting the potential of that data.
He posits that an open stack helps the business resolve this data fragmentation, by consolidating the data on an open-source framework from silos using ‘hooks’.
“Consolidated data 'at rest' is available for real-time queries for data insights and improving customer experiences. Changes to the operational data are captured in real-time and streamed as 'data in motion' to applications, data lakes and artificial intelligence as well as machine learning engines for processing and analytics,” he elaborated.
He says this automated process provides agility to the FIs to innovate and move away from high operation costs and slow development cycles. For FIs to truly succeed in a hyper-competitive marketplace, their data needs to be liberated and ready to power the next generation of offerings, services, and experiences.
Transformation and modernisation risks
Digital transformation is forcing technology leaders to find effective ways to modernise legacy systems. Modern architecture provides financial institutions with the agility to adapt to ever-changing consumer expectations, innovate with data insights and deliver timely and relevant consumer experiences.
That’s the promise. The journey however is not without risks, as revealed in a global survey which noted that up to 74% of legacy modernisation projects fail to complete. Ault warns that rigid legacy banking systems will face various challenges in scalability, flexibility, reliability, and complexity.
He attributes this, in part, to the way data platforms are built. The right data platform must be able to support a variety of initiatives to be considered a proper digital foundation, he argues.
“First, financial institutions need to consider how their core data platform continues to support regulatory requirements such as anti-fraud across a data-rich, AI/ML-powered cloud environment. This requires a solid, tried-and-tested foundation that works in the real world and not just on paper,” he contends.
Ault says a full migration can be costly and high risk but can be mitigated by modernising 'in-place'. By offloading around 60 to 90% of its MIPS (millions of instructions per second) to a modern, cloud-native data platform, financial institutions can leave their mainframe in place for the time being, and instead, use that cloud-native data platform to build applications at a lower MIPS cost, for immediate and more effective return on investment.
Real-time data trends
Ault says by 2025, we expect most data to be processed and delivered in real-time, with vast networks of connected devices that gather, and transmit data and insights almost instantaneously.
Technologies such as kappa or lambda architectures would dramatically transform the way data is generated, processed, analysed and visualised for users.
“FIs will gain the ability to automate key processes, bringing insights to life in a split second. The competition in the cloud computing space will drive down prices, making sophisticated advanced analytics more affordable, and potentially enable more use cases and more opportunities for profitability,” he opines. “Time-series, graph and NoSQL databases will improve data management and organisation for data practitioners to leverage.
“Speeding up the discovery of insights drives innovation through new AI capabilities, enabling teams to query and identify relationships within your organisation's data quickly.
“Real-time data capabilities will also extend the current capabilities of the enterprise in personalised experiences - running concurrently with real-time analytics and graph capabilities. All of these are captured and displayed in real-time on a dashboard and provides a 360-degree view of the customer and batched data for comprehensive drilled down insights,” elaborates Ault.
Real-time data in fraud management
Every dollar of fraud costs FIs US$4 in associated costs, according to a study by LexisNexis Risk Solutions. The study noted that banks earning more than US$10 million in annual revenue have seen monthly fraud cases rise to 2,320 in 2021, up 14.9% from 2020 levels.
Ault noted that traditionally businesses would deploy relational database management systems (RDBMS) for fraud prevention. However, in today's competitive environment, RDBMSs are not able to keep up with the amount of transaction volume nor scalable enough for them to be effective. Organisations would have to make compromises with partition data for performance, but RDBMS still limits the amount of data they can analyse in real-time.
Ault says the implementation of a proven and secure scale-out operational data layer changes all of this and opens new AI/ML innovation opportunities to propel the business forward for the next decade.
“Any kind of data, including transactional, demographic, help desk, shopping cart, historical trends and even alternative data can be analysed in real-time, and help financial institutions have a real weapon against fraud.”
Harry Ault
“At the same time, businesses can deploy artificial intelligence and machine learning algorithms to be even more effective against fraudulent activities be it detection or prevention.
“Businesses can also take a more proactive hands-on approach to prevent fraud. By setting up rules for transactions, FIs can receive notifications of transactions that do not follow past transaction trends, enabling transactions to be "frozen" in real time for further investigation later,” concluded Ault.
