Displaying search results for ""

Timeline of Digital Transformation from 2015-2019

Timeline of Digital Transformation from 2015-2019

Digitization of banking functions can be traced back to the days of automated teller machines (ATMs). The digital journey for banks began early on with digitization of simple banking functions. The face of banking and financial industry has changed in the last decade. Changing consumer behavior and competitive environments have forced banks to address digital transformation processes head-on.

In the past four years, banks have transformed tremendously with the evolving technology landscape. As per EY, banks set upon a transformation journey by concentrating on 5 key transformation imperatives – the quest for profitable growth, surviving a new era of competition, defining a bank’s new core, adopting advanced technology, and defining a bank’s new structure. The imperatives were to shape the new strategies and operating models of banks over the coming decade.

The driving factor for digital transformation is the paradigm shift in business models from a product-centric strategy to a customer-centric approach. Today’s tech-savvy consumer demands better services, products, and experiences across all touchpoints. But, from an IT viewpoint, aligning customer needs to IT platforms have been a challenge owing to banks reliance on legacy systems. Owing to the cost of transformation, most banks aren’t able to move away from the monolith systems. While other banks are looking at virtualization of IT infrastructure into a cloud-delivery model (private and public).

Digital transformation enabled the development of digital banking. During the period of 2014-15, digital payments in developing markets grew 21.6 percent. By 2016, 40 percent of Americans had shifted to digital banking, reducing walk-ins at banks. Digital banking negatively impacted traditional banking’s brick and mortar centers. Large banks, such as Bank of America Corp., had to downsize and pull out 1507 branches across the US.

In the end, a new payment ecosystem emerged that was driven by increasing consumer demands, rising FinTech competition, evolving regulations, and an increase in payments-enabling technologies. The rise of mobile and digital banking allowed consumers to experience e-commerce-like capabilities with banking functions. Apart from PayPal, companies like Stripe, Dwolla, Apple Pay, Venmo and others provided a variety of payment platforms options for businesses and consumers alike. Peer-to-peer (P2P) payment systems further developed for social media networks, including Facebook, to facilitate quick and easy payment options. With digital banking, P2P payment options grew to introduce more convenient payment options to consumers. For instance, companies like Zelle offered real-time fund availability and fund transfers between bank accounts within minutes.

While technology plays a critical role in digital transformation, another factor dictating the banking sector are the carrying regulations and compliance checks. Since 2018, the EU’s General Data Protection Regulation (GDPR) and the Revised Payment Service Directive are beginning to impact the market. The directive opened up doors for third-party providers (TPPs) to build financial services using application programming interfaces (APIs). For example, YES BANK was among the first banks to build an API ecosystem to enhance its services and expand market reach.

The era of open banking facilitated digital transformation for financial institutions to create next-generation apps, nurture FinTech collaborations, and unlock new channels of revenue streams. Tech giants like Google, Apple, Facebook and Amazon (GAFA) are entering the market with innovative payment channels that are different from the traditional payment processors.

Open Banking is accelerating digital transformation across the global banking industry. Banks are focusing on delivering customer-centric solutions using new business models and open services. With the advent of improved digital banking capabilities, banks are now taking on the role of mediators or institution that supports digital banks. For example, In the US, banks are entering into data-sharing agreements while regulators in India are encouraging a Unified Payment Interface (UPI) to enable interoperability.

In 2019, technologies of artificial intelligence, voice-first banking, and cloud-based banking would gain ground and become core to banking operations. There will be a keen focus on enhancing digital capabilities with a data-driven strategy. The 2019 Retail Banking Trends and Predictions report identifies new technologies to respond to consumer expectations. 54 percent of respondents pushed the development of digital solutions of real-time intelligence data integration using AI, advanced analytics and cognitive computing as a priority.

The future of banking focuses on hyper-personalization at scale and transforming customer experiences. Importance of data and advanced analytics will continue to rise as banks build strong personalized marketing platforms. The digital transformation journey is shifting the center of banking ecosystem away from banks and toward customers. The new center forces banks to adopt a different set of strategic imperatives. As banks continue to strive to attain complete digitization, their success depends on integrating, collaborating, supporting the development of reliable and scalable digital platforms that recognize the market opportunity at every step.


9 data science use cases in banking

9 data science use cases in banking

The banking industry has evolved over the years when it comes to customer service delivery and operations models. However, it is surprising that most banks are yet to embed analytics into the core company culture, decision processes, and business operations. A recent report by McKinsey analyzed analytics maturity of more than 20 banks in Europe, the Middle East, and Africa (EMEA). Among the banks surveyed, only 30 percent reported having matched their data analytics efforts with their business goals.

To succeed in this data-driven world, banks need to leverage data analytics capabilities toward better decision making. We take a closer look at various business functions where data science can be applied.

1. fRAUD detection-01-01   Fraud detection

PwC’s latest survey, Global Economic Crime and Fraud Survey, reports a rise in fraud with 49 percent of respondents stating they’ve been a victim of fraud or economic crime – up from the 46 percent in 2016. To combat fraud, organizations are employing the use of technology and data analytics tools. These include machine learning to identify patterns, predictive analytics to predict the likelihood of occurrence of fraud and other Artificial Intelligence (AI) or advanced analytics techniques.

For example, Danske Bank is fighting fraud with deep learning and AI techniques. The bank struggled with low fraud detection rates (40 percent) and has over 1200 false positives per day. After the implantation of a modern enterprise analytics solution, the bank realized a 60 percent reduction in false positives; increasing true positives by 50 percent.

2.data mgt   Customer data management

The open banking momentum has gained significant ground since the introduction of the Payment Services Directive (PSD2) by the EU. Under the current mandate, data sharing is enabled via public application programming interfaces (APIs). In an open environment of data sharing, the ability to collate, manage, and analyze data is even more important than before.

3risk   Risk handling for investment banks

Singapore’s United Overseas Bank (UOB) has implemented big data analytics to drive risk management solutions. As a financial institution, UOB provides a host of personal banking, investment banking, insurance services among many others. Earlier, it would take UOB analysts several days to calculate multiple risk factors pertaining to loans. Using high-performance analytics – a combination of grid computing, matrix-based calculations, and in-database analytics, UOB was able to introduce near-real-time risk calculations into risk management. The bank has also been able to deploy risk management resources to identify business opportunities.

4marketing   Personalized marketing

Banks can now use insights from data analytics to run more targeted and personalized campaigns. Platform-based personalization is being employed to optimize marketing strategies. The platform improves personalized campaigns and enables faster processing of customer data. For instance, a key focus for mBank, owned by Commerzbank, is personalization. The bank enables personalization through predictive analytics to identify individual customer preferences. The bank combines social and mobile technology to provide an enhanced digital customer experience.

5 Prediction   Lifetime Value prediction

Lifetime Value (LTV) is a measure of how long organizations are able to retain their customers. LTV is used by many banks as a direct measure of customer satisfaction. In this digital age, LTV becomes an important metric. Increasing competition by Fintech and new entrants places a greater emphasis on acquiring and retaining customers. With predictive analytics, banks can know which customers to focus on for new engagement efforts.

6analytics   Real-time and predictive analytics

In today’s fast-paced world, customers have grown to expect instant services and solutions. Banks are adopting real-time analytics to tap into continuous data streams to identify risk and opportunities. Real-time insights help organizations gain business intelligence toward improving business processes, make informed decisions, delivering better customer service, predicting future scenarios, creating new product categories and many other applications.

7 segment   Customer segmentation

Banks find value with customer segmentation, helping them attain a deeper understanding of their customers. Using data analytics techniques, enterprises can analyze customer profitability to offer a personalized customer journey. Leveraging user behavior data banks can strategize IT requirements, marketing campaigns, and efficiently divert customers to their preferred interaction channel. For example, Lloyds Banking Group wanted to understand and know its customers as individuals. With data analytics, Lloyds was able to address the needs of different customer segments and maximize growth in targeted segments.

8 rec engines   Recommendation engines

The rise of e-commerce and retail industries has improved the prowess of recommendation engines. In the banking sector, recommender systems are being employed to identify behavioral patterns and recommend services or products. For example, BBVA, a multinational Spanish banking group, uses a recommendation engine to offer personalized suggestions or advice to their users based on their behavior and needs. The bank has introduced services such as Baby Planner, Bconomy, and Commerce360 for managing personal finances and expense control.

9 support-01   Customer support

Delivering superior customer support is a major part of customer service. Banks are moving from the traditional service-oriented model to a customer-centric model. In order to stay ahead of their competition banks are using analytics to help customer support agents be efficient at their jobs, decreasing resolution times, and improving overall customer experience.

The growing capabilities of data science would help banks rapidly innovate solutions and parallelly refine processes and products based on collated data. But despite the growth in big data analytics, banks have been slow in adopting this technology owing to the sensitive nature of data. Collectively, banks have started laying the analytical foundations, but there is room for improvement. Banks need to dive deeper into data analytics to realize its true potential.


Choosing the right cloud strategy for superior digital banking experience

Choosing the right cloud strategy for superior digital banking experience

Cloud computing has made inroads into the banking sector, but adoption has been slow. Owing to the sensitive nature of data, banks have been wary of adopting cloud solutions, especially public and hybrid clouds. But with the recent introduction of EU’s Payments Services Directives (PSD2), banks are more confident in incorporating cloud solutions into their core operations. The European Banking Authority has also issued an exhaustive guidance document for the use of cloud service providers by banks and other financial institutions.

As banks recognize the potential of cloud computing their digital strategies are evolving to encompass cloud solutions. With multiple providers, a one-size-fits-all approach does not meet the requirements of this highly regulated industry. To meet the growing demands of the tech-savvy customer and facing stiff competition from Fintech, banks are looking to the cloud for improving their digital banking experience. But how can cloud enable superior digital banking? We take a look at cloud strategies to ease your cloud decisions.

Cloud for front-end and core banking products

Digital transformation has helped banks offer innovative solutions to customers through mobile banking, chatbots etc. Most banks have relied on digitizing the front-end to revamp banking applications and providing the customer varied interaction touchpoints. But in the race to digitize their services, back-end or core applications of banks have remained relatively untouched.

As we move deeper into the digital transformation era, the need of the hour for banks is to align their front-end initiatives with back office services. The major roadblock in this journey is the continued use of legacy systems. These monolith systems have to be upgraded or replaced to support computational and operational needs of the cloud.

By integrating cloud solutions into core banking applications banks can look beyond the fluff of snazzy user interfaces to create a truly superior digital customer experience. For example, Temenos recently announced the launch of two new products – Temenos Infinity and Temenos T24 Transact, both designed for cloud-native and API-first technologies. By implementing cloud for front-end and core operations, the products can be deployed on-premise or in a customer/Temenos cloud. The cloud-agnostic solution can run on Microsoft Azure, AWS, and the Google Cloud platform.

Private, Hybrid or Multi-cloud strategies

Typically, service providers deploy clouds either on a private, public or hybrid cloud. Private cloud infrastructure is a dedicated platform for a company to completely manage, maintain, operate, and secure its IT infrastructure. The ability to fully control access (physical and digital), deploy applications in minutes, and ensure security makes private cloud seem an ideal solution for financial institutions. The pain point of such a system is in achieving economies of scale. As a company expands, its servers have to scale accordingly, which can be a costly, time consuming, and labor-intensive process.

Hybrid clouds offer the features of public and private clouds for running applications and enabling data portability. Hybrid clouds and multi-cloud strategies are quickly becoming the norm for banks and financial institutions owing to the flexibility, scalability, and minimal set-up costs. Leveraging best-of-breed services, hybrid/multi-cloud strategies avoid vendor lock-in and data sovereignty across applications. Both strategies help in de-risking business decisions and offer a greater choice of platform-agnostic services.

The pitfalls of a hybrid or multi-cloud adoption come with the hidden costs of every service. It’s easy to get lost trying to keep track of multiple cloud platforms and vendors. Deploying hybrid or multiple clouds would require complex and at times expensive IT infrastructure.

Despite the challenges, banks are adopting hybrid cloud models to increase agility, quickly deliver application updates or new releases, and ease of scalability (upsize or downsize) based on enterprise requirements.

Steps to evaluate cloud strategy

Irrespective of the type of cloud service adopted, CIOs need to thoroughly evaluate their cloud strategy before implementation. While there are no set evaluation guidelines, the following are essential steps for evaluating a cloud strategy:


Choosing the right digital cloud strategy can be a daunting task. In order to enable a superior digital banking experience, organizations need to enable a rapid shift to the cloud. Meanwhile, cloud strategies are evolving with new cloud service providers entering the market. With time and appropriate regulations, banks can accelerate their adoption of cloud solutions; choosing the best fit for their organizational needs.



3 Cloud Adoption Challenges in Banking and Financial Services Industry

3 Cloud Adoption Challenges in Banking and Financial Services Industry

Cloud computing and its associated services are rapidly transforming industries across all sectors. The Banking, Financial  Services and Insurance (BFSI) sector is the most active user within cloud due to the rise of mobile banking, Fintech, and virtual transaction services of PayPal, Google, Amazon etc. As per a report by IDC, the banking sector is forecasted to spend $16.7 billion on public cloud services, growing at 23 percent CAGR.

Despite the explosive growth and multiple cloud strategies in place, cloud uptake is slow in the banking sector. We take a closer look at the three main challenges impeding cloud adoption in the banking and financial services industry

3 challenges

Data and Security Privacy

Dealing with sensitive data places banks at greater risk of data leaks and cyber-attacks. IBM reports the global average cost of a data breach has increased to 6.4 percent, translating to $3.86 million. The loss/theft of a single record comes up to $148.

With any technology security remains an issue, cloud is no exception. As recent as last year data breaches have been plaguing banks. Even financial services giant, JP Morgan, could not escape being hacked. In 2014, a cyberattack resulted in a massive data breach, affecting 76 million households and 7 million small businesses.

Data security incidents are inevitable, but preventable. As technology evolves, so must security protocols. Organizations have to emphasize on threat and vulnerability detection in all spheres of cloud. When considering industry-wide technology transformation customer-data confidentiality is to be placed at the core. In its new guidance, the European Banking Authority (EBA) recommends banks to address this issue before considering outsourcing cloud services to third-party entities.

Reporting and Compliance

While data and security are top priority for banks, navigating the changing compliance regulations  and reporting standards cannot be overlooked. It was only recently that the regulatory landscape for cloud computing in finance was established through the Financial Conduct Authority (FCA). A similar mandate was issued by the European Banking Authority for institutions intending to adopt cloud services. In addition, last year’s second Payments Service Directive (PSD2) and Open Banking regulations introduced new data protection laws.

With changing regulations the BFSI sector is forced to stay on its toes and catch any non-compliance issue immediately. The legal framework is not matching its pace with fast-moving technological advances. Countless reports identify the benefits of cloud computing for the financial sector but its adoption is met with resilience due to fear of non-compliance and cloud risks.

In this atmosphere, it is clear banks must take regulatory environments into consideration before widespread adoption of cloud services. Regulators continue to express concern over storing of sensitive data on the cloud, especially with non-banking companies entering the same space. Most financial institutions are advised to take a risk-based approach before implementing any third-party cloud functionalities or relationships.

Lost Productivity and Competitive Edge

Banking and financial institutions are eager to jump on the cloud bandwagon. But most organizations don’t have the expertise or financial capabilities to implement cloud solutions. Most banks are still grappling the decision of moving their monolithic legacy systems to the cloud. Running on legacy systems, organizations lose out on productivity benefits of cloud applications. In their race to cloud migrations, banks face hours or days of server downtime; affecting customers and employees simultaneously. Shadow IT is a growing concern in organizations, with employees accessing unauthorized cloud tools for completing tasks. NetEnrich’s recent cloud adoption survey identified Shadow IT as one of the top concerns for IT in 2019.

In the face of cloud challenges partnering with a vetted cloud service provider would be ideal. External cloud experts, specializing in banking and financial services industry, would be able to provide the essential tech support and implementation norms – without compromising on data security or regulatory compliance. With the right migration strategy in place, along with right cloud tools and partners, banks can look to the cloud with ease.


Event-driven architecture for banking

Event-driven architecture for banking

In the face of digital transformation, banks have significantly transformed their business models, culture, and operational models – taking them beyond the traditional banking systems. Faced by ever-changing regulations and a rapidly evolving technology landscape, banks are breaking down the legacy-era monoliths and adopting microservices as the core banking platform.

The digital world brings with it the complexity of handling large amounts of data generated from millions of users. Despite the rise in real-time analysis and superior computational algorithms, traditional data architecture cannot capture all enterprise and external data. To complement existing data architecture services, banks require event-driven rules to unite functionalities.

Traditionally, banks have followed a request-driven model wherein a rigid architecture defines tasks. These systems are efficient in developing simple and set tasks, but fail to react to variable cases of the digital era. For example, a user accesses your banking portal for fund transfer. During their time on the portal, they might get interested in a different product. They seek out the product, read more about it, but eventually forget about it/lose interest and move on. The traditional request-driven architecture would fail to identify this opportunity of business interaction; leading to loss of potential sale.

In order to prevent such loss of data, developers are moving to an event-driven architecture (EDA) system. But what is EDA?

Gartner defines EDA as “a design paradigm in which a software component executes in response to receiving one or more event notifications. EDA is more loosely coupled than the client/server paradigm because the component that sends the notification doesn’t know the identity of the receiving components at the time of compiling.”

An ‘event’ is a notable thing that can occur inside or outside of a system, triggering a set of services, business processes or operations, while event processing deals with detecting and responding to events that have meaningful business outcomes. Taking up the previous example – With the EDA setup, your customers’ interest in products gets registered as an event by the system. Based on the event captured, banks can now categorize customers into prospective clients and open new lines of business interaction for your sales or business development teams.

Rabobank, a Dutch multinational banking and financial services company, has been continuously working on its real-time event-driven bank. Moving away from the traditional batch processing, Rabobank is building a Business Event Bus (BEB) to share business events across the organization. Effective communication with its millions of customers was a scalability and flexibility issue that the bank was able to overcome by adopting EDA and event programming into their mainframe. The bank developed ‘Rabo Alerts’ – a system to alert customers in real-time whenever an interesting financial event occurs and thereby, drastically reducing customer alert timing from 5 minutes to just a few seconds.

In the past few years, EDA has increased in popularity owing to changing markets, connected consumers, and mobility. The architecture is used to build reactive applications that are event-driven, scalable, resilient, reliable, distributed, and interactive. The real-time application communicates asynchronously across systems wherein the sender and receiver, both, remain anonymous. Since systems are triggered only in case of an event, EDA enables loose-coupling between components; eliminating dependency and lowering operational costs.

While consumer service is a push for EDA, regulatory compliance is also becoming a data-driven discipline. Banks and financial institutions continue to struggle to comply with regulations such as Bank Secrecy Act/ Anti-Money Laundering (BSA/AML). The high costs of non-compliance is not a factor for consideration for any agency. Issues from lack of uniformity in the KYC process to lack of periodic assessment of vendors are all areas of potential non-compliance. But at the core of the system lies KYC.

After onboarding a customer, the KYC system needs to perform regular on-going monitoring, periodic customer risk re-assessment, and re-certification. For an event-driven enterprise, this does not pose a challenge as an EDA would take care of submitting events to a monitoring platform. The system will be able to filter events of interest and submit them to respective business units – setting off a cascade of business processes to reassess customer’s risk and re-certify the customer.


By becoming event-driven, enterprises specifically banks improve their scalability and regulatory compliance. The evolution of EDA to integrate microservices or API management solutions adds another dimension to the development of a smart Service-Oriented Architecture (SOA). Increasing adoption of IoT and big data will further boost EDA in the banking sector.