With the advent of digital native customers, banks are facing a paradigm shift in their customer behavior. Banks are facing disruption in their business model from fintech companies and other disintermediation encouraged by regulators, and more than 90% payments moving to real time. The data infrastructure supporting their existing business model was not built for real time payments and self-service. To support changing customer expectations and new business models, banks need to modernize their data infrastructure by drastically reducing latency and building intelligence in their existing data infrastructure.
In a series of four blogs, we have identified:
1 the need and purpose of data infrastructure modernization
2. Leveraging emerging technology in data infrastructure modernization
3. Embedding intelligence into data infrastructure at banks
4. Addressing key challenges of data modernization projects
Data modernization is a long journey. The journey needs to be supported by redefining business architecture, technology architecture and people skills or the target-operating model of the bank. Since the changes may take 2-5 years to start showing business benefits, bank managers need to communicate and win trust of the board of directors of the bank to get requisite budget, support and guidance. Adapting an anchor standard like BIAN service model may also help.
Need and purpose of data infrastructure modernization: Banks are facing disruption in their business model from all side- from changing customer expectations, from regulators, from industry and competition and from fintechs and new players.
- Digital Native Customers: In the past 20 years or so, customer demography has completely changed in across geographies. Banks now have 30-65% of digital native customers of less than 35 years of age who practice digital ways of engaging commerce, finance and banking. Bank’s engagement technology and model needs to adapt to engage digital native customers.
- Real Time Service Patterns – To cope with the demands of digital natives, countries with more than 95% of the global GDP have transformed their payment systems to real time payments, creating very different technology patterns for banks. Real time transactions are completed from nano seconds to a few seconds. Technology architecture enabling nano and micro second time window is very different from batch processing.
- Data Format of Customer Engagement is moving to conversational and text: With more than 4 Billion smartphones in pockets today, and more than 6 Billion users on messengers, data format of customer’s engagement has completely changed towards conversational and text from templates.
- Disintermediation enabled by regulators: Regulators across the globe are encouraging and enabling fintechs to disrupt the banking business model for a larger financial inclusion and to reducing banking and finance cost Primary theme of most of the fintechs is self-service, automation and intelligent technology. Banks are integrating fintechs into their technology stack by partnering, white labelling and purchasing fintechs. In either case, banks need to upgrade their internal technology eco system.
- Marketplace economics disrupting business model: Commerce is moving to the Ecommerce market place, creating the benefits of network economics. To win in the marketplace network, banks are upgrading their application technology to platforms and APIs.
- Low Latency is Finance, Regulatory and Compliance Mandate: With the adoption of IFRS 9 and IFRS 17 for finance reporting, BCBS 239 standards for Risk Reporting, and compliance for algorithmic Trading and real time cyber monitoring banks are upgrading technology for a faster near real time data movement from front to mid and back office.
- Data Hunger of Regulators: Regulators, in order to manage systemic risk and monetary policy better, are building data lakes and data science to model early warning signals emanating from the micro prudential returns to manager their macro prudential responsibilities better. To support their mandates, regulators are establishing machine-readable regulatory instructions. Regulators have already conducted proof of concept.
The question which every bank has to ask is whether, their existing systems with completely varying data definition and formats, with data quality and reconciliation managed manually by large teams of analysts, fragmented applications and integrations, monolithic applications, rudimentary and underdeveloped systems to handle unstructured data be able to cope with the surging market demand. Banks need to build a roadmap for modernization of their technology to cope with the threat to their business model.
Let us also keep in mind that banks are very different from any other industry. Why? The most relevant attribute here is the volume. Which other industry will have the following volumes in transactions and analytics?
o Assets Size of the Bank – USD 50 Bn to USD 1500 Bn
o Transaction per day – 2Million - 50Million
o Customer Base 3Million – 400Million
o Concurrent Users for Analytics - 1000-3000
o Analytics jobs to be executed daily 20000 to 40000
o Uptime for Analytical Infrastructure 99.5% to 99.9% o Uptime for OLTP and Product Processors - 99.999% to 99.9999%
To quote Einstein, “We cannot solve our problems with the same thinking we used when we created the problem.”
So, existing technology infrastructure cannot solve the daunting problem of paradigm shift in the customer profile, cost structure and compliance requirements. Banks need to exploit emerging technologies that have matured during the past four years at a very fast pace. In the next blog, we will discuss how emerging technology is being leveraged by banks to modernize data infrastructure.