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Redefining Customer Experience in Financial Sector with VR and AR

Redefining Customer Experience in Financial Sector with VR and AR

We have come a long way from the first commercial use of Oculus Rift VR headset 0f 2013. Yet, most people associate the technology of Augmented Reality (AR) and Virtual Reality (VR) with the realm of gaming. However, many industries including marketing, healthcare, real-estate are accepting the immense potential of VR to improve their business. A report by Goldman Sachs group estimates the virtual and augmented reality to become an $80 billion market by 2025.

Even financial institutions like the banks are well aware of this conundrum, and many firms are aggressively experimenting with the new coming technology to enhance customer experience (CX). From basic apps that use customer location to help locate ATM branches nearby to promoting banking solutions in an engaging 3D environment. Some financial institutions are using it as a marketing tool, others are using AR to offer customer-centric apps that display real-time cost and other information associated with properties which are up for sale, offer a mortgage calculator and more.

According to a study, ‘AR/VR can transform financial data into a visual, engaging experience and can eventually bring the face-to-face experience into a customer’s home‘. The possibility of hybrid branches is also in the pipeline where physical branches use AR technology to offer self-service like chatbots, or robots to provide information. If required, customers can also connect to an actual bank-representative via video conferences.

All things said and done, the idea of banking in virtual reality is still half-baked and the road to reach that reality is daunting and surrounded by skepticism about the possibilities of virtual banking. Nonetheless, there are a few corners in the financial sector where VR and AR have already made an impact:

  Immersive Experience through Data Visualization

The financial industry has a lot riding on analyzing large amounts of data on a day to day basis. Data visualization helps financial traders and advisors to get a visual breakdown of the copious amount of data and make informed decisions about wealth management. Using the modern technology of VR and AR, data visualization is quicker and easier than ever before.

Remember we spoke about Oculus Rift earlier? Fidelity labs used the technology behind the Oculus Rift to create an immersive 3D environment to analyze data accurately. They created a virtual world where people can talk to financial advisors in virtual reality to learn about the progress of their stock portfolios. Their VR assistant, Cora, will display the stock chart on a wall of her virtual office just like presenting graph on a virtual projector.

   Virtual Trading Workshops

Some financial institutions are using VR to create virtual trading workshops. In April 2017, FlexTrade Systems announced the launch of ‘FlexAR’ – a virtual reality trading application that uses Microsoft HoloLens to offer an extraordinary way of visualizing and presenting trading. It uses components from the real world and allows traders to see and interact with the markets and identify the holistic patterns in the trading environment.

   Virtual Reality Shopping Experience

Taking customer and shopping experience to the next level, in 2017, MasterCard and Swarovski launched a VR shopping app that allows consumers to browse and purchase items from Atelier Swarovski home décor line and immerse into a complete virtual shopping experience. They can use Masterpass, MasterCard’s digital payment service to make payments.

   Security

With biometrics as part of the AR experience, financial services can offer more secure and substantial protection against cybercrime. A number of banking applications already offer fingerprint authentication for many smartphones. With AR, iris identification and voice recognition, are being introduced as well. In 2018, Axis Bank became India’s first bank to introduce Iris Scan Authentication feature for Aadhaar-based transactions at its micro-ATM tablets.

   Possibilities of Virtual Branches

As more and more financial service providers are incrementally moving towards digitized banking, the idea of a virtual bank doesn’t seem too far-fetched. Imagine never having to take a break during working hours and wait in a line at the bank. Now imagine, getting the personalized banking service at the comfort of your home, when it’s convenient for you while enjoying a cup of coffee. That’s what virtual branches have to offer. To aid customer demand for contact anytime, financial institutions are already offering services like Chatbots and are developing solutions to provide banking solutions exclusively in a VR environment. This would be a win-win for both- customers will get their service anytime, anywhere and banks will be able to reduce costs as they will not need to invest in physical locations.

Living in today’s high-tech world, we all know that technology is something that has been and will keep on evolving. With each day passing, reality adjacent technologies like VR and AR are becoming mainstream, and already impacting the way financial institutions operate, manage data, interact with customers and more.

There is no doubt that the financial industry will need to integrate this new science into banking operations. Not only will this help them attract and retain customers, enrich the customer’s user experience (UX) but also help in operational cost reduction. Failing to do so, their customers are most likely to move toward non-financial institutions that offer ease of use and flexible services that they demand.

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Best Practices to watch out for when implementing AI in Customer Experience

Best Practices to watch out for when implementing AI in Customer Experience

With the evolution of digital banking, newer tools and technologies are being adopted every minute to enhance customer experience. According to a report by IDC, worldwide spending on cognitive and artificial intelligence (AI) systems went up by 54.2% from 2017 to 2018 to an estimated $19.1 billion. The figure is predicted to grow to $52.2 billion by 2021.

As every industry sector is adopting AI into their daily operations, it is crucial for organizations to evaluate not only their AI investments but also their implementation. In a hyper-connected world it takes one bad customer review to bring down a brand’s reputation. While AI is reducing customer engagement costs, companies have to routinely check its performance and be ready to provide support

What does one have to watch out for while implementing AI into their customer experience journey? We take a look at the challenges that the banking sector face and how to tackle them:

  • Breaking company silos: Banks are notorious for operating in silos, with every service/product being handled by different departments. The silos prevent effective inter-departmental communication and also sharing of key data insights that could be used to improve customer experience.

Recommendation: Forming cross-functional teams with shared responsibilities to create a unified multi-channel approach. Some banks have a centralized CX team that handles several enterprise-wide initiatives.

  • Holistic approach to AI implementation: While banks explore chatbots and virtual assistants to take over a small portion of their business, they fail to leverage AI into the entire banking ecosystem. For effective AI operation, banks need to integrate data points, invest in cloud technology and enable a secure network to support its implementation.

Recommendation: Banks are investing heavily in AI-driven applications that are transforming how customers engage across the financial sector.

AI

 

  • Re-skilling the workforce for AI: Most banks train their workforce based on their assigned department. Mix in intelligent technologies, such as AI and blockchain, and employees are left with a skill gap that can’t keep up with the evolving technology. As per a report by Accenture, banking CXOs believe that only 1 in 4 employees are ready to work with intelligent technologies. Only 3% of these banks are planning on increasing their investment in reskilling their workforce.

Recommendation: Reimagine work to understand human-machine interaction, create new roles or redeploy your workforce based on their skills, and encourage talent development with increase in transformation investment.

  • Aligning AI with business goals: Datasets are the backbone of AI operations. To be able to derive prediction with high accuracy, companies need to define their datasets. This is possible when a company is able to strategize and define clear business goals or mission statements. AI alignment helps in delivering accurate information to businesses, creating a transparent operation model with defined outcomes. Companies have to focus on aligning business, digital and AI strategies to prioritize digital transformation initiatives.

Recommendation: Develop a clear strategy to link data sources for accurate AI operation of data mining and analytics. A dedicated group of business and technology leaders would aide in developing strategic business measures and also monitoring platform performance based on set goals.

  • Lack of emotion in bots: Although banks have implemented chatbots, many customers feel the lack of emotional intelligence that can only be provided through human interaction.

Recommendation:  Invest in sentimental analysis to understand customer response and sentiments. The analysis would help in improving customer engagement and receiving real-time customer feedback on products and services.

  • Collaboration issues: As the financial sector deals with a complex web of transactions, it’s important to implement fail-safe mechanisms. Multiple AI systems are deployed to handle specialized tasks, fitting into a streamlined process.

Recommendation: Develop AI-enhanced cognitive collaboration tools that have built-in expert systems. These systems would have their own knowledge base to deal with complex issues in a particular domain.

Despite its challenges, one can’t deny that AI is here to stay and will continue to significantly affect the banking sector. Its rewards of efficient systems, cost reduction and improved customer experience outweigh the risks of implementation. Moving forward, AI is going to play a key role, forming the digital backbone of banks. This would require a change in organizational structure, business models and customer acquisition and retention.

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AI Series I – The Evolving Role for AI in Customer Experience

AI Series I – The Evolving Role for AI in Customer Experience

From chatbots to automation, artificial intelligence (AI) is reshaping enterprises and the way it interacts with its customers. According to a Gartner’s report, the global business value derived from AI is projected to total $1.2 trillion in 2018 and forecasted to reach $3.9 trillion by 2022. The report identifies customer experience as one of the primary sources of AI business value, followed by new revenue and cost reduction.
AI has been around for decades, but it’s in the fourth industrial revolution that AI is beginning to take root in almost every business application. IDC predicts that by 2019 “40% of digital transformation initiatives will be supported by cognitive/AI capabilities. The push for AI comes from how consumers are engaging with businesses on a daily basis.

While there are many uses for AI, there are three themes that we come across:

AI 1            AI 2              AI3

 

Use of AI for Data Insights

For every organization, gathering customer behavior datasets and making sense out of it is a challenge and also cumbersome. As customers expect omnichannel experience, AI and customer journey analytics are key components to deliver the experience. Most of the current AI use is restricted to the front-end or pre-purchase phases of the customer life-cycle. But AI can go beyond to cover the entire lifecycle, from product usage to account management and logistics.

High-performing banks are already using AI to assess their customers with pre-built predictive analytics and AI-enabled customer journey. For example, The Royal Bank of Canada and Israel Discount Bank have an AI platform called Personetics to help their customers manage their finances. The Self-Driving Finance Platform, promises a safe and effortless guide for consumers to manage their day-to-day finances through cognitive systems and a fully automated savings solution. With AI, the banks are able to track every customer interaction, spending patterns and find its relationship in a dataset – aiding prediction of future behaviors. These insights help in building an optimal CX by providing actionable insights.

Customer Service and AI

For most banks, customer service involves the interaction of employees with customers across various touch points. To meet the increasing demand for customer interaction, companies are looking at chatbots and virtual assistants to enhance their customer service. As per Juniper Research, “chatbots hold the potential one day to replace the task of many human workers with AI”. It forecasts that by 2022 chatbots would be responsible for cost savings of over $8 billion per year, up from $20 million in 2017. This trend is being observed in the banking sector as well with a prediction of 93% of successful messaging banking bot interactions in 2022.

Chatbots are helping organizations reduce costs of implementation, introduce an easy-to-use conversational interface to customers, take care of simple account creation/ KYC requirements and also be able to provide 24/7 digital support to its customers. For example, Wells Fargo& Company uses an AI-driven chatbot plugged to the Facebook Messenger platform. The chatbot responds to natural language messages and is accessible to its customers directly on social media, regardless of the device being used. Similarly, Bank of America is rolling out their AI-platform, Erica – an intelligent virtual banking assistant. The platform would be integrated into the mobile banking app to enhance customer engagement.

The potential of chatbots go beyond just a messaging system. Future chatbot technologies would include facial recognition to enable zero-click transactions, application of virtual reality for better data visualizations and using IoT-enable devices for voice interactions.

Mass Personalization with AI

Today’s digital customers demand hyper-personalization at every step of their journey with a brand. This level of personalization goes beyond personalized emails and suggested products lists. With predictive analytics and AI, banks are able to deliver individual customer preferences at scale, integrating with their daily lives.

For banks, personalization is a viable growth strategy, providing opportunities for up/cross-selling products and services. This would require the integration of multiple datasets to provide an overview of a customer’s credit history, spending habits, interests and life events. Personalizing the user experience is also delivered through hyper-customized content, improving click-through rates.

When it comes to personalization, mBank is seen as the leader in innovative banking solutions. The Poland based financial institution uses predictive analytics to identify customer behavior and use the information to initiate direct conversations and other marketing efforts. For added security some banks, such as Royal Bank of Scotland and NatWest, use biometrics for authentication systems in mobile banking.

In this digital era the financial sector is changing, and while customer service is a priority, security and regulatory compliance also need to be looked at. Banks have to strike the balance between the two and transition smoothly into adopting technologies. How can one achieve this balance? Read about it in our blog “Best Practices to watch out for when implementing AI in Customer Experience”.

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Microservices II – Is it possible for all banks to adopt microservices?

Microservices II – Is it possible for all banks to adopt microservices?

Enterprises have been struggling to develop applications that are agile and quick to change. Microservices architecture provides a way to overcome this challenge and has therefore caught the attention of enterprise IT teams.

From a technical angle, microservices avail service-orientation principles and functional partitioning to “monolithic” applications so that the systems work as relatively independent services. From a business benefit point of view, it makes companies digital ready and provides numerous advantages to them like allowing experimentation and innovation, sanctioning more autonomy and accountability, enhancing the resiliency of mission-critical systems, by scaling and reproducing parts of systems rapidly, and enabling cloud adoption and cloud-native architectures by deployment of services over multiple VMs or containers that function together.

Bank regulators are looking to drive better deals for the clients by generating more competition by innovating customer information sharing, transaction initiation, and payment mechanisms through open APIs. With an open API economy, banks get an option to introduce their customers to new products and services through collation among the business units within a bank, across industries, and between banks and other complementary sectors of the economy, especially data businesses and technology.

Microservice splits monolithic applications into a set of services that communicate with each other through open APIs. Each service acts out one particular function pretty well; the service and its API are a product that is detectable, well-defined and carefully maintained. These self-contained services are then constituted as needed so as to deliver complex functionality, even if they are deployed independently of each other. Services can calibrate independently as well, making the software flexible at runtime. And if a single service folds, it doesn’t mean it will bring down the entire system because of the flexibility built into microservice-based digital banking solutions.

It has come as a blessing for the banks because this approach will provide favorable circumstances to deliver value at subsequent intervals and not wait for a massive technology shift to happen. Open API banking combined with microservices-based architecture will define the success in banking space in the near future, where banks would be in a better position to accelerate time to market in the real sense.

While microservices promise a better future to the banking industry, it would require banks to rethink their operational support, delivery methodology and required skills. Only that banks that will consider an upgrade in people, process and technology will be able to leverage the power of microservices and utilize it to the maximum.

Examples of banks that have benefited from adopting microservices

Nordic APIs recently worked with the Canadian Imperial Bank of Commerce (CIBC), who have set out on their own microservices journey to get rid of their existing monolithic backends. One crucial tenet of this journey is a microservices framework Light 4J, that CIBC built themselves. They’ve assembled it from open source components instead of trying to customize a commercial offering. Nordic APIs was assigned the task of reviewing its features and functionality to both validate their progress and provide constructive feedback.

By developing their own microservices framework, CIBC acknowledged the need for microservices in a standard, reiterating framework of development that can be embraced by the all within an organization. Cultivating a microservices framework from the beginning connotes that it can develop side-by-side with CIBC’s microservices maturity. This is crucial because it allows the organization to learn lessons and adjust their approach accordingly. With these benefits, CIBC is well-placed to adapt and experiment with their architecture, both in terms of domain and technology. This kind of flexibility lends itself to the Evolutionary Architecture style. In the process of building the Light 4J framework, CIBC is using a standardized microservices frame that deals with cross-cutting concerns as a key tenet of the framework. The homemade approach is also beneficial when it comes to introducing resiliency in the style of API that can be supported.

Another example is the Monzo Bank, who created their backend systems using microservices architecture and implemented using Google’s Golang. The memorable part of this project was Golang’s brilliant concurrency primitives that make this language consistent for creating ‘high volume, low latency, distributed applications; the use of a microservices framework like Monzo’s open source ‘Typhon’ framework in amalgamation with the CNCF ‘linkerd’ proxy, being highly advantageous for implementing core communication concerns; and enabling distributed tracing through context propagation being a key enabler for observability and debugging distributed systems.

Will the microservice architecture become the preferred style of developers in future? It is a question that still remains unanswered. But it’s clearly a potent idea that provides serious benefits for designing and implementing enterprise applications.

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