Sharing data for digital services requires an efficient data architecture
Customers have become even more dependent on online and mobile apps as quarantines and social distancing practices mandated during the Coronavirus are driving increased digital engagements. It’s only a matter of time before the demand for more advanced services will become a standard request for all financial services customers. This will move the open banking trend forward in the US, despite the fact that unlike Europe and in Canada, open banking regulations don’t seem to be on the radar in the near future. Applying a broad-based standard to enforce APIs and data sharing for over 10,000 financial institutions is technically and logistically challenging.
Fear of Infection Drives Digital Banking
The Coronavirus has resulted in the massive closing of branch banks. New York City based Citigroup temporarily closed 100 branches and JP Morgan Chase, the largest bank in the country, announced plans to close about 1,000 branches. Those branches that are open will have reduced hours for receiving customers.
Approximately one-third of retail banking customers plan to increase their use of online and mobile banking services post COVID-19 as more services, such as loan and mortgage requests and payments, are becoming virtual and digital on-boarding is available for new accounts. Mobile payment applications are shooting up in popularity as people are reluctant to touch cash or credit cards. These numbers are expected to rise as the pandemic continues to spread, and especially if there is a second wave resulting in another round of restrictions and closures.
However, there is more to digital banking than replacing services that were previously provided in branch banks. Open banking can power new innovative digital services to give banks a competitive advantage by using standard APIs to capture data from customers’ accounts. For example, with access to a comprehensive view of a customer’s data, banks can allow payments from accounts held elsewhere. Digital personal advisors powered by machine learning based applications can analyze all of the savings and investment accounts and customer’s personal goals to recommend optimal opportunities. Financial firms can also use data provided to identify the most relevant service for a customer, the most competitive service pricing, and the most advantageous terms and conditions for investments, loans and mortgages based on advanced risk models and market offerings. Just think how easy it could be to convince customers to move to your bank by showing them how you can improve their earnings or reduce their expenses.
These services are also not limited to B2C. The Bank of America has recently unveiled digital tools designed for small enterprises that help streamline transactions and generate cash-flow projections along with easy connectivity with financial consultants for quick advice.
Towards a More Efficient System Architecture
As more and more people and businesses rely on digital apps for their banking services, the number of online transactions continue to grow; putting a strain on existing IT computing resources. The massive increase in the number of queries is resulting in bottlenecks that can degrade the performance of applications and affect customer service levels. When customers wait too long to complete a transaction or receive approval for a loan, or if they understand that they can receive better conditions from another bank, they are more likely to switch. Thus, banks are faced with the need to scale up their expensive legacy infrastructure to provide the expected user quality of experience, or to find modern solutions that can elastically scale to manage this data at the required speeds, with an optimized TCO.
In many cases large financial services organizations are limited by tangled and archaic systems that are too complex to optimally manage, process and analyze their huge amounts of data from different sources. This was revealed recently in a BIAN survey where over 60 percent of respondents expressed concerns that banks will struggle to open up their APIs because of the “current state of banks’ core architecture.”
There are however, innovations designed to help banks process more data, more cost effectively without the need to rip and replace existing platforms. For example, some in-memory computing platforms can co-locate all data models (structured, unstructured and semi-structured), service business logic and analytics in an in-memory data core and distribute it across a horizontally scalable architecture for exceptionally fast processing times, while eliminating the need to invest in excess capacity.
These modern data and analytics platforms support agile development and deployment of applications that involve interactive queries, analytics and even machine learning running simultaneously on mutable streaming, transactional, and historical data that is stored on external data lakes and data warehouses. Applications are able to access data on external data lakes whether they are on cloud, on premise or hybrid 100 times faster which not only speeds up queries and BI reporting, but also improves the accuracy of time critical applications and decision making services, such as risk analysis, fraud detection, loan approvals, dynamic pricing and personalized services.
Digital Banking Will Become the New Norm
Digital banking will become the new norm. However, financial services will need to adopt new technologies to leverage and monetize their data to maintain their market leadership. The full potential for digital banking can only be realized if there is a high-performance data management infrastructure in place that can enable queries and analytics to be completed quickly and reliably for time critical applications at an optimized TCO. COVID-19 may have resulted in closed branches, but it can accelerate the adoption of open banking, to bring convenience and new applications that will change the way customers do their banking.