Open Banking has arrived

The banking industry is on the cusp of a revolution, driven by open banking parameters and new regulations for entry of third-party providers. Introducing open banking could mean enhancing customer banking experiences, reducing overall costs, diverting investments towards technological advancements and more. This induces fragmentation of existing clustered banking markets, enabling accelerated innovation and development. While Open banking is an ecosystem comprising of banks, third party API developers and regulators that makes ‘customer experience’ more holistic and rewarding, it can be effective though only in conjunction with near-real-time data analytics. Analytics is essential to improve risk and compliance in near-real-time by all players on the open banking canvas.

According to a report by World Retail Banking, more than 78% of banks wish to leverage APIs to improve the customer experience through open banking. These APIs should be scalable and have the ability to track and measure monetization.

Analytics is Key

Open banking will relinquish the control that big financial firms had over the current market using open APIs which makes it easier for third parties to access customer data and create services by plugging in directly to bank systems. These trends and developments in open banking will reshape the industry and drive the adoption of open APIs in the years to come.

It is no longer the data you hold but what you do with it that will deliver a competitive edge and an entrenched role in the customer’s life.

The open banking movement has entered a climate where customer relations is fragile since today, people crave connected experiences and are forever lost as customers when brands fail to recognize them as individuals. Having easy access to a customer’s entire portfolio helps devise new products and bolsters customer experience and innovation. This aids the bank to usher in different methods of customer engagement like a chatbot application for instance that personalizes the customer experience. Redesigning a bank’s customer acquisition model can help create agile machine learning models that can adapt with customers when they are inactive.

PWC estimates that by 2022 open banking will open an opportunity of 7.2 billion pounds. Powered by data analytics, the open banking market is set to take banking to the next stage of improved revenue and better customer experience.

Some Predictions for Open Banking in 2019

1. Increased Demand for Screen-Scraping: The Regulatory Technical Standards (RTS) of Open Banking have made it difficult for fintech companies to meet the standards due to a shortage of live open APIs. For providers wishing to embrace the open banking system, using account aggregators that furnish solutions based on “screen-scraping” is a possible alternative.
2. Rise in Account Aggregator Supply: The list of account aggregators in open banking continues to increase. Furthermore, credit bureaus are further pushing for the adoption of account aggregation.
3. Cost of Accessing Data Will Fall: The vast increase in account data will push for more AISP (account-specific information service provider) licenses and aggregators will bring the cost of account data lower globally. Hence, this would further encourage banks to adopt open banking and invest more in building better customer experiences.

A disruptive threat to Incumbent Banks?

With the arrival of Open Banking, customers will be willing-to-change and willing-to-experiment with new ways to manage their money, borrow, and protect their wealth. While Open banking is good news for consumers, incumbent banks have a risk of being left behind by their customers and hence must act quickly to stay relevant. The winners will be only those who will be data-driven and leverage Analytics to rapidly convert customer insight into powerful new experiences that are personal and add real value to daily life.

Despite the threat of extreme competition, banks do enjoy a clear head start over challengers. In reality, it’s an opportunity for banks to evolve digitally. Being an incumbent, they have at least one clear advantage i.e. access to a sizeable pool of existing customers. By keenly paying attention to their customers, they can gain insights into the products and experiences they want most, now and in the future. While open banking regulations work in favor of the customer, they also ensure that ample data is amassed for the benefit of the banks themselves. From behavioral profiling to strategizing, analytics helps in creating better business plans. Using patented analytics tools, banks can understand a customer’s typical purchasing pattern. From spending velocity to the exact hour of transactions and how customers spend money in relevant risk, pricing and cross-selling models, the algorithms help track the exact purchasing behavior of the customer. By using machine learning algorithms, customer profiles can be created, and their hourly & daily transactional behavior tracked thereby preventing anomalies.

Understanding the patterns help devise methods of improving customer experience, while also maximizing the bank’s profitability margin. This improves customer trust and brand loyalty and increases the chances of long-term customer relationships and profitability. This part of analytics can be personalized based on financial and non-financial parameters, helping clarify customer profiles further.

Real-time Analytics to fight fraud

Even though Open Banking is clearly the way forward, there are certain challenges that accompany this new wave. The added conveniences provided to consumer banking will lead to rising in transaction volume which in turn is likely to make banks more vulnerable to fraudulent and illicit activity. But if you catch fraud in real-time, you have the opportunity to stop it before any big damage occurs. It will take human beings too long to parse and interpret information that reveals fraud, thus Real-Time Analytics-Based Detection is Key to catch fraud. Banks can establish a baseline of normal transaction activity and there on identify anomalies using real-time analytics that could signal potential fraud.

Start today

The harsh fact some of the popular surveys have revealed is that Less than 20% of surveyed banks make extensive use of predictive data analytics to power improvements in the customer experience and operational performance Analytics can help banks become smarter in tackling challenges and analytics services in banking is still in its infancy with a lot of untapped value still left to be uncovered. The overall surge of analytics in banking is increasing and is expected to quadruple by 2020.

Analytic solutions today can predict a bank’s profitability, ensure survival and compliance and further foster growth. Instilling a growth-driven mindset to leverage analytics can greatly improve decision making while also taking other ground realities and challenges into consideration. Banks have a lot of ground to cover in the next five years if they are to retain the customer relationship in the era of open banking.

This “start today” approach is recommended.

Continuous intelligence is at the heart of fast-paced digital business and process optimization, decision automation, AI and real-time analytics

In the face of digital transformation, the banking sector has evolved to become a customer-centric and data capable organization. Banks generate large volumes of internal (customer accounts, payments) and external data (macroeconomic variables, customer preferences, social media) across various channels. This is further compounded by the increased velocity and variety of data generated. Banks have to now generate business insights at very fast pace from complex data, of any data source (current and historical), of any data size and format.

Enter Continuous Intelligence!

CI is a new age artificial intelligence (AI)-based advanced analytics to meet the dynamic data demands of the banking sector. Gartner defines as a “style of work in which real-time analytics are integrated within a business operation, processing current and historical data to prescribe actions in response to business moments and other events. It provides decision automation or decision support. CI leverages multiple technologies such as augmented analytics, event stream processing, optimization, business rule management and machine learning.”

CI, also known as real-time analytics, exists in a frictionless state and enables a business to leverage continuous, high-frequency, intuitive insights from data sources. The machine-driven system gives organizations the ability to intelligently merge disparate data sources; providing a complete data story and adaptable action points. According to Gartner, CI is “at the heart of fast-paced digital business and process optimization, leveraging decision automation, AI, real-time analytics, and streaming event data.” It estimates, by 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.

Speed is everything!

In recent years, AI has significantly impacted the banking sector. For organizations, it has become increasingly important to innovate and stay ahead of their competition. CI requires nothing but the real-time availability of data – a challenge for the banking sector that is built on enterprise information hubs, data lakes, and other disparate systems. Banks can get started by bringing in the correct data integration and real-time replication capabilities. AI solutions help speed up data collection across silos and automates the generation of data stories. For example, banks can leverage analytics to determine risk-score of customers seeking loans. With CI, banks can now simultaneously leverage insights across both information asset repository and real-time transactional data.

By combining real-time data with legacy data, banks are in a position to take banking customer analytics to a new level. Contextual insights would help drive a multi-dimensional decision-making process. For instance, Korea’s NH Nonghyup Bank is transforming its business operations with machine learning from SAS. NH Bank is adopting machine learning and AI to better understand its customers and provide a personalized customer experience. To meet the speed, agility and growing needs of the company, NH Bank looked to advanced analytics dashboard that provides a complete view of each customer. The portal is said to support both structured and unstructured data allowing business users to run and generate a variety of visual reports.

Acquire, Identify & Respond quickly

Complex Event Processing (CEP) is an example of application of CI that uses information contained in business events (streams) to identifying event patterns (extract), analyze and provide insights into the changing condition. In retail banking, CEP can be deployed to aide challenges of fraud detection and cross-selling. For instance, the system can detect a series of unauthorized micro-transactions across multiple locations or cards in real-time. Suspicious transactions can be flagged immediately; reducing losses to the customer and ensuring timely corrective actions.

The contextual nature of CI helps organizations to provide a better personalized and interactive experience to consumers. Consider retail shopping – a consumer using an ATM at a shopping center is offered discounts or deals based on their location and the stores nearby, in real-time. The offer provides a better contextual relevance to the customer than the conventional e-mail sent a few days later. The real-time contextual insight can help develop better omnichannel marketing strategies.

Continuous Intelligence turns data into outcomes

Gartner has Identified Continuous Intelligence as a Top 10 Data and Analytics Technology Trend for 2019. Today’s modern applications are driving the development of digital services that are reliant on CI. Across industries, machine learning is easing the journey toward autonomous data. Undoubtedly, AI-based data analytics is of utmost importance to financial institutions and their analytical models. The major shift in data analytics is the accessibility of insights and intelligence to every business user and internal stakeholders. By developing tools around CI, banks can better manage the flow of business models, right from ideation to production.

CI is achievable! Business leaders must take the initiative to leverage their data through new technologies.

In the past decade, digital transformation has been an integral part of the banking revolution. Digital drives the need for banks to better engage with their customers and improve overall customer experience. In today’s fast-moving millennial world, a shift in user power demands better engagement and real-time interactions. In this environment, open banking brings in new possibilities of creating a customer-centric ecosystem.

With the introduction of the Revised Payment Services Directive (PSD2) in Europe and similar legislation around the world, open banking has gained significant ground in the past year. Open banking driven by mapping and understanding a customer’s journey to enable better interaction and experience. The bridge being – application programming interfaces (APIs).

APIs lies at the core of open banking, emerging as the main mechanism for data interactions between banks and third-party providers (TPPs). On one hand, they have been able to provide the much-needed integration between various systems and on the other, they have been facilitating data integration across surround systems. Organizational structure and culture are shifting to support rapid product development and innovation in the financial sector.

Globally, banks are recognizing the potential of Open Banking in enhancing their service offerings, customer engagement, and increase revenue potential from new channels. But open banking demands new approaches to drive customer engagement. To meet the increasing demands of the market, banks have to build on

• Better anticipation of the customer through better insights
• APIs enabling engagement through customer interaction
• Driving impact through ongoing innovation

Better anticipation of the customer through better insights 

APIs enable banks and financial institutions to push past the current banking system and develop and deploy products based on what customers will need. Banks are tapping into social media and other digital channel data to determine customer needs and behavior. With the right insights, banks can develop a deeper understanding of what customers anticipate and create micro-segments to improve marketing, operations, and business focus. For example, if a customer’s digital footprint indicates an impending property purchase, banks can make a timely offer of attractive housing loans.

Many banks are introducing AI-powered chatbots, backed by conversational AI abilities, to improve engagement. Financial advisory bots, such as Eno from Capital One or Ally Assist from Ally Bank, offer financial management solutions and easy banking facilities. Chatbots utilize APIs to integrate with data management platforms; allowing banks to analyze the extracted data and derive insights to anticipate customer behavior.

APIs enabling engagement through customer interaction 

APIs are helping banks achieve better transparency and visibility, drive innovation, and improve collaboration. For instance, APIs are enabling the creation of vast customer data repositories to help deliver a highly personalized experience to consumers. Banking platforms provide TPPs access to multiple data sources, including banks direct deposit accounts, credit cards, investments, and other financial data for developing innovative financial applications and services. With quick integration and seamless data access, financial APIs can be used to construct a detailed customer profile and personas to increase convenience and connecting them with the right products or services. In its recent whitepaper, FICO categorizes the new-age consumers into five categories – success-driven savers, precarious passives, ambitious adopters, delayed dreamers, and fiscal futurists. These categories are based on user behavior and attitude toward financial institutes, products, and services.

Canada’s digital-native bank, Tangerine, partnered with IBM to develop a mobile banking app and provide a ‘shake to feedback’ feature. This capability offers customers an easy and accessible medium to provide personalized feedback directly to the bank; effectively engaging with the customer and gaining insights to improve the overall mobile experience.

Driving impact through ongoing innovation 

For long, banks have been held back in the technology race due to their monolithic legacy systems and data silos. Migrating to new systems is an expensive affair that most small to medium banks cannot afford. A second challenge is staying relevant in the presence of technology giants Google, Apple, Facebook, and Amazon (GAFA).​Deloitte estimates that 75 percent of millennials would be more interested in new financial services from GAFA than banks.

To survive in the current digital atmosphere, banks have to modernize their legacy systems to become more agile, flexible, and support scaling up plans. With an API strategy, it has become possible to adopt a bi-modal IT to improve speed and efficiency. Using APIs, banks can repackage core system assets to create new and innovative systems of engagement. Banks are now able to connect legacy systems for better operations and simultaneously improving their front-end for better customer experience.

Considered an enabler of innovation, APIs are expanding banking ecosystems to include more financial services and products that emphasize consumer value propositions. For instance, a Germany-based FinTech and fully licensed digital bank, is helping companies become financial service providers. Using its regulatory and technology infrastructure, the firm has developed a modular banking kit that includes APIs for account and transaction services, compliance and other services.

The future of banking would be a new paradigm driven by emerging technologies, non-traditional competitors, and deregulation of the sector pushing for openness and transparency. In order to serve customers efficiently, banks have to emerge to manage relationships among multiple stakeholders making up the banking ecosystem. With APIs, banks can truly become a digital platform and a central hub for TPPs to interact and attract more customers.

In today’s age of hyper-personalization, banks and financial institutions are constantly adopting new technologies serve customers better. The need for superior computing prowess is seeing the emergence of edge analytics.

But what is edge computing? Gartner defines edge computing as solutions that facilitate data processing at or near the source of data generation. Instead of bringing data to the Analytics, we need to bring the Analytics to the data. The model pushes computing applications away from centralized nodes to the edge of data networks, leveraging device resources that are not connected to the network.

Cloud to the Edge

With digital transformation, banks and financial institutions have rapidly adopted cloud technologies for storing and processing large amounts of data. This has constrained the cloud infrastructure with massive data loads and congestion. Hence, edge analytics is growing as an alternative to big data analytics and is poised to take over cloud data-mining analytics. A major objective of edge computing is to provide the benefits of cloud computing and big data processing while minimizing the use of an organization’s IT infrastructure. For instance, instead of installing ATMs, banks are placing interactive kiosks to deliver an omnichannel banking experience along with conducting financial transactions. Virtual reality and augmented reality technologies are being explored to enhance the customer experience. Financial service firms can utilize real-time data generated from edge computing to create a single customer view.

Edge computing follows a topology-based computing model to enable and optimize decentralization. The model places nodes closer to the data sources resulting in reduced latency and localized data traffic. Unlike cloud computing, edge analytics computes data in real-time on edge devices instead of sending data back to the cloud for computing. By carrying out computing closer to the edge of the network helps in analyzing data in real-time – crucial for data-driven decision making in the banking sector.

Gartner research revealed that only 10 percent of enterprise-generated data is created and processed outside a traditional centralized data center or cloud. By 2025, it is expected to reach 75 percent. The rise of edge analytics is attributed to increasing adoption of the internet of things (IoT) and the requirement of workplace performance enhancements. For instance, the Commonwealth Bank of Australia (CBA) is testing end-to-end banking solutions with 5G banking and edge computing. The system has the potential to enhance the availability, stability, and performance of CBA’s network infrastructure and support a range of software-defined networking solutions.

The major benefit edge computing offers is the ease of scaling operations with each new device that is added to the system. For example, banks are empowering their staff with smartphones or tablets to provide personalized service to customers at the branch, reducing wait time and increasing efficiency.

Insights from edge analytics help banks in understanding their customers better. Using location-based suggestions and customer recommendation banks can deliver transactional behavior in real-time. For example, banks can collate anonymized data (via mobile apps and near field communications technology) to create personalized signages to cross-sell products. This edge-to-edge intelligence addresses the near real-time computation needs of the digital landscape and provides a seamless user experience.

The recent introduction of GDPR, edge computing addresses the issues of storing data on the cloud. In edge computing the data is stored onsite, giving banks control of user data enabling better data security from reduced risk of data loss and theft.

Continuing growth

According to Market Research Future’s (MRFR) recent report, the global edge analytics market is predicted to reach $11 billion by 2023. Moving forward, a key question to address is how the industry adapts to edge computing pertaining to infrastructure, operation, integration micro-data centers, and decentralizing public cloud footprint. Currently, the downside to edge computing is the lack of platforms that can handle machine learning on distributed, federated data. Organizations can look at a hybrid edge-cloud solution to deliver fast experiences to customers and provide the flexibility to meet industry requirements. With the proliferation of IoT, the introduction of 5G networks, and the increasing amount of data generated from connected devices, edge computing has the potential of disrupting banking industry and integrating latency-sensitive microservices.

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Digital transformation is the order of the day everywhere with most businesses talking about going digital. However, a majority of them don’t realize that it encompasses processes far beyond just upgrading to the latest IT system.

Businesses must realize that in order to be a digital business in its truest sense means more than just adopting new technology. It’s all about taking the right advantage of the waves of transformation surrounding us. According to a recent research by McKinsey Global Institute (MGI), after looking at the condition of digitization in sectors across the U.S. economy, it was found that there is a growing breach between sectors, and between companies within those sectors. Companies that have digitally transformed themselves have witnessed an enormous growth in productivity and profit margins.

Banks and other financial institutions are contemplating renewing their core systems with digital technology, realizing that their legacy technology will not be able to support their changing needs and will have to be changed in the next few years. But digital transformation in the banking and financial sector isn’t as easy as it sounds. Banks will have to implement a more flexible banking platform on top of the traditional core banking system in order to achieve complete digitization.

Digital core: How does it lead to digital transformation?

What the other sectors lack is not processes, people or tools. It is a performance infrastructure that wires the people, processes, and tools allowing and sustaining digital transformation. It’s the digital core.

A digital core is an innovative technology design that provides businesses with real-time visibility into all mission-critical business processes and all other processes that encompass customers, workforce, Big Data, suppliers, and the Internet of Things (IoT). This cohesive system empowers companies with the required data to predict, simulate, plan and even antedate future business results with unparalleled accuracy in the digital economy.

A digital core can be utilized to overcome complexity in the IT infrastructure of companies. It can provide real-time perceptibility into all the crucial processes that concerns its customers, workforce, data management, suppliers, and devices.

The in-memory computing helps businesses support the consumers and enables end-user decision making. The digital core then changes the system of record into a system of advice, fast-tracking the speed of the business on the basis of real-time, up-to-date decision support.

Core transformation in the banking and financial sector

Core transformation in the banking sector has always been kind of a taboo. The fear of the unknown, comfort level with existing technologies and the luxury of overlooking operational inefficiencies have ensured that core transformation doesn’t take place in the sector.

After the economic crisis of 2008, financial institutions have been streamlining their business and operating models for both monetary reasons and to lessen organizational complications. While historically, banking institutions were held in high regard, the financial crisis hit all financial institutions right where it hurts. Most banks are yet to recover from the damaged reputation they faced during the catastrophe. In comparison to other industries, banking institutions have experienced the least growth in brand value over the last 10 years. With digitization of other industries, consumers now expect next-generation banking experiences to reproduce those in other industries.

The times have changed and in the current competitive environment, it is essential to align the IT strategy of banks to their business goals. Core banking transformation seems to be the only way out.

Technology plays a strategic role in the banking industry. From its core operation to distribution channels, a bank is dependent on the IT. Hence, replacing the core system is a huge exercise for a bank. The bank must have a clear objective, make a simple plan and stick to it. One of the main challenges is to keep all systems up and running during the replacement procedure. Furthermore, keeping costs low is critical because every bank’s key objective is to increase revenues and reduce expenses.

By embracing the digital core, banks can cut their costs and restructure their processes. This endways integration leads to a more seamless, engaging customer experience for the customers and it provides opportunity for further business alteration with new digital technologies like blockchain and artificial intelligence.

Principally, a bank should strategize an IT architecture that reduces complexity. They should use technology solutions that are based on open standards and can be implemented quickly.

To keep up with the rapidly evolving business and operational requirements along with shifting customer demands, the banking sector needs to constantly upgrade its practices and processes. This is possible only if banks regularly augment their core systems and associated applications. Hence, a large number of banks are considering the transformation of their existing core systems with contemporary vendor solutions.

Some BFSI organizations are already leveraging the blockchain technology to alter their business processes as it provides safe, convenient substitutes to traditional banking processes. Off late, blockchain has been the order of the day because it has reduced fraud in the financial world. Other technologies, such as machine learning, are also being utilized widely to automate manual processes, fraud management, and customer segmentation activities.

However, transforming this complex web of applications with new core banking solution is not an easy process. There is a fundamental need to meet functional requirements, cleansing of data from old systems, transformed and then migrated to the new system, and so on. The processes driven by older applications also have to be altered and users need to be re-trained on the new application and processes.

Due to the highly disparate systems in the banking sector, this process is time-consuming and costly. In the last few years, a few banking institutions have used a Line of Business (LOB)-oriented migration approach. This approach helped them experience the benefits of the new core banking solution rather early in the project. This is where the Service-Oriented Architecture (SOA) features as a helping hand in the migration process.

It works as an integration framework that combines the internal and external services to create a solution. With SOA, instead of focusing on different applications that are a part of multiple disparate systems, the emphasis is on business services that denote several different underlying applications.

With the introduction of high-end technologies and global best practices that offer enhanced dexterity, efficiency, CRM capability and faster deployment cycles, banks need to be aware of the challenges that plague the core banking deployments. Once these challenges are understood and alleviated properly, digital transformation is achievable and perfectly manageable.