Core Revival for Legacy Banking Systems: Why is it Important? 

Finance leaders considering modernization can finally look beyond large-scale migrations

Legacy systems are machines of the past, that are still capable of leading us into the future. Mainly because it has worked as a base for data processing to date. In sync, modernization, a buzzword for big banks looking to compete with innovative tech disruptions, is currently doing the rounds once again. As of today, a few new additions to legacy systems are expected to transform the strategic outlook for upgrades.

Being called ‘Core Revival’ – the process makes cloud implementation, blockchain capabilities, and big data analytics a priority for strategy. As going digital is in its most complex phase ever, banks are realizing that modernizing needs to help with more than just the obvious. To avoid the same mistake that made legacy systems a burden, the newer trend professes the adoption of flexible systems and web-based systems that factor in future needs of collaboration and upgrades.

Identifying Opportunities: Tech Modernization for the Long Run

Core revival initiatives introduce leading-edge technological changes into existing IT ecosystems. Here, risk officers should account for risks when old and new technologies intersect; where they can work closely with CIOs to anticipate and manage risk in a manner that doesn’t hamper growth in current and future situations.

Finance leaders considering modernization can look beyond large-scale migrations. For example, creative deal structures with large cloud vendors are a common practice that helps with one component at a time. Moving existing capabilities to low-code platforms can also lead to cost-neutral, easily-adaptable options for establishing a future-ready foundation within core systems.

The Pandemic Push: A New Outlook for Accelerated Functionality

Even as we get back to the new normal, the accelerated rate of business transformation, read: digitization will stay the same. Apart from promoting a collaborative atmosphere, businesses have realized the importance of preparing for disruption.

Legacy systems can be majorly transformed with add-ons of SaaS and cloud solutions. Since replacing existing systems are the costliest transformation taking place, solution integration offers value with its new-age alternative of cloud-based services. Banks can now deploy systems quicker, increase scalability, and magnify analytical capabilities in a cost-efficient manner.

Real innovation isn’t about the next best thing; it’s about developing an ecosystem that supports continuous innovation. Accordingly, these functions brought in by core revival will prepare businesses for the next level of banking –

Cloud Systems for Easier Collaborations

Migrating major functions to a cloud helps businesses share business functions easily. Instead of just being able to share data, businesses can now allow other companies to use their systems and business models. For a smooth transition, banks can decide whether to approach the migration through any one of the two possible migration methods – the lift and shift model and transformation model.

Either way, businesses have to be meticulous in the selection of the offerings it selects from their cloud provider, which are –

  • Software as a Service – Applications that end users can access through navigators, browsers, and web interfaces using Internet protocols

  • Platform as a Service – A container within a programming environment, libraries, and tools that support the application developments.

  • Infrastructure as a Service – Virtual machines, storage memory, database service, and network components.

Integrated Data Systems with Revamped Architecture

Banks need teams that understand and make use of the democratization of analytics. Well-planned solution architecture helps with democratization, which means data is made available at multiple levels, for those who have the skill sets to derive meaningful insights.

As all big data analytics is driven by metadata; all records are linked based on combinations of attributes within the data. Using statistical techniques for integrated systems, collective entities are identified and collapsed to produce single views of vast datasets through a single interface.

Insight Generation from Reconstructed Data Flow

Insight Generation systems for IRAC Norms, Ind AS Reporting, and IGAAP Reporting with AI systems utilize actionable data. Automation of multiple data collection processes is possible only when actionable data is generated and available systems can use the data for evolved analytics.

New-age systems for insight generation utilize integrated solution architecture, multiple data sources, and data lakes, and also deploy advanced AI, ML, and NLP functions for higher-quality data.