The Intricate Selection Process of a Core Banking System

The Heart of Modern Banking

Core banking systems are the foundational transaction processing engines for banks and financial institutions – consisting of a centralised back-end system that coordinates banking transactions including deposits, loans, interest calculations, and credit processing, across a decentralised network of bank branches. These core systems are essential in digital banking technology, enabling customers to transact anytime, anywhere, and empowering customers with greater control and ownership over their personal accounts. For financial institutions, these systems help reduce the manpower required, bring down operational costs significantly, shorten turnaround time for transactions, and allow banks to expand their outreach to remote places.

With the huge customer base of financial institutions, these core banking systems handle a high volume of transactions and are required to be highly stable and reliable to prevent interruptions, outages, and any security breaches. As such, financial institutions place a significant importance on selecting the right core banking systems, and they spend millions of dollars maintaining the systems annually to avoid regulatory scrutiny, loss of customers’ trust, as well as the potential loss of revenue.

Challenges in Choosing a Core Banking System

As financial institutions move towards adopting a modern IT infrastructure, many face the problem of selecting a suitable core banking system from a plethora of service providers for financial institutions to choose from – including solutions such as Finacle Core Banking, Flexcube by Oracle, SAP Transactional Banking, and more. This process is made complicated as the core banking system usually interfaces with a wide array of systems and APIs, and financial institutions need to carefully evaluate the options to properly integrate the solution with their legacy banking functions.

For instance, apart from selecting the right core banking system, financial institutions will also have to select vendors who can provide services to support the core system and integrate the modular components into a customised end-to-end solution. With excessive undocumented customisation, the code base can become a complex system that is difficult and risky to change in the long run.

Secondly, financial institutions will have to consider the technical characteristics of the available core banking system – whether the system adopts an open or closed architecture, affects tasks of business users, and if there are potential outages. Risks will also have to be evaluated to ensure that the system knowledge, especially the complex customisation, resides within the organisation, while receiving adequate support from the selected vendor.

Thirdly, financial constraints have to be taken into account – evaluating the cost as a percentage of IT spending. As core banking systems are expensive, stakeholders involved may opt for cheaper options that may not be sustainable in the long run.

Lastly, banks also have to ensure that the selected vendors and their proposed solution meet the regulatory requirements of the country. A tedious process of regulatory audits and reporting will ensue to confirm that the selected systems can handle the high volume transactions securely. The bureaucratic process may extend the duration required for the core banking system implementation.

Emergence of Next Generation Core Banking Platforms

With the considerations above, the selection and implementation of a core banking system requires a significant amount of time and monetary investment, optimal stakeholder and vendor management, and the ability to integrate into existing legacy banking functions. As such, solutions such as the next-gen cloud based core banking systems or open core banking systems that enable rapid financial product development, reduce licensing costs, and provide ease of integration, are gaining traction.

One example is Fern Software’s Quantum AI – a loan decision engine for rapid and accurate loan application decisions, that can integrate seamlessly with existing core banking systems to support the loan transactions. Powered by Salesforce Einstein artificial intelligence, the solution provides highly accurate loan predictions by utilising the financial institution’s customer loan portfolio book while relating it to financial services inputs from Open Banking and Credit Referencing Agencies to score an applicant and decide on the loan offer. With a prediction precision of 92.7%, Quantum AI enables loan volumes to increase by over 100% while reducing operation costs by up to 80%.

Banks with traditional core systems – usually designed for reliability instead of an open architecture, will need to adapt to the emergence of alternative solutions. In an evolving financial environment, banks who do not catch up with innovative tools may lose customers to their competitors.

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