Automation in the banking sector began early with computerization of key functions across departments. Advanced ledger posting machines were introduced to automate account-related functions that formed the core of banking operations those days.

Over the years, banks slowly moved to improve customer service through branch automation. Banks were able to connect disparate branches, automate back-office functions and facilitate information sharing, without disrupting banking functions. The ‘Single Window Service’ facilities delivered all banking services at a single counter; significantly reducing transaction processing times.

Although slow to adopt, the banking sector embraced the IT revolution to develop interbank connectivity. The multi-channel banking approach placed emphasis on building a centralized data infrastructure which consolidated databases across banks into a single large database. The system was crucial in improving services and lowering costs of service providers allowing banks to explore multiple delivery channels such as automatic teller machine (ATM), net-banking, telebanking, and mobile banking.

Introduction of ATMs radically transformed banking relationship between the customer and bank. The user-friendly technology complemented a bank’s branch functionalities making it accessible to customers who no longer required to enter a bank for basic financial transactions. Banks benefited by introducing a new customer service interface that significantly reduced operations cost.

The slow shift to customer-oriented services helped develop core banking applications which not only integrated enterprise software to in-house applications but also automated multiple delivery channels at once. Banks were able to build on cross-selling products such as insurance, bonds, credit cards, and other financial products.

In India, banking technology accelerated with the establishment of the Institute for Development and Research in Banking Technology (IDRBT) in 1996. The institute launched three important technology infrastructures, namely, Indian Financial NETwork (INFINET) in 1999, Structured Financial Messaging System (SFMS) in 2001, and the National Financial Switch (NFS) in 2004. These services formed the backbone of IT implementation in the Indian banking and financial sector. As a digital certification authority, the institute implemented high-end Public Key Infrastructure (PKI)-based services and solutions to secure transactions through INFINET.

Over the last decade, the banking industry has witnessed the impact of disruptive technologies and also of non-banking entities such as PayPal, Google Pay, and Amazon. As the internet became more accessible, banks moved to realize their dreams of total bank automation with internet banking and electronic fund transfer. To address the development, IDRBT promoted the Indian Financial Technology and Allied Services (IFTAS) to provide IT-related services to Reserve Bank of India (RBI), banks and other financial institutions. In 2016, IFTAS took over the operations of INFINET, SFMS and the Indian Banking Community Cloud (IBCC). India is also a member of the Committee on Payments and Market Infrastructure (CPMI), formerly the Committee on Payment and Settlement Systems (CPSS), to monitor and promote efficient payment and settlement systems in selected counties.

As the industry heads into the next digital revolution traditional banks face stiff competition by newer technology-driven start-ups and Fintechs. Historically, financial institutes have dictated how customers transacted/interacted with them. Non-banking entities have essentially changed the relationship wherein the tech-savvy consumers have more control over their financial transactions. This rapidly shifting consumer behavior is placing importance on operational and analytical customer relationship management (CRM) systems that can target customers effectively while providing operational support.

The API era

In this competitive era, banks are starting to use Application Programming Interfaces (APIs) to link their products, services, third-party value providers and customers – at one place. The adoption of APIs is a shift from the bank’s traditional process-led automation system to a customer-centric system.

From a consumer’s perspective, APIs enable users to access banking and financial services anywhere, anytime. From online shopping to creating new accounts, consumers enjoy banking at their convenience. For banks, APIs enable banks to connect new modules to their existing legacy systems while continuing to provide updated real-time services. As per a survey by Softwareag, 95 percent of executives from Europe, Asia-Pacific, and North America are already setting up APIs for internal systems, while 81 percent share their APIs with trusted partners. The survey also states that in the next 2 years, 50 percent of banks are planning to start supporting open-banking platform through published APIs.

The API-era brings with it the concepts of open technology and open platforms; concepts that threaten the longstanding tradition of closed innovation. More and more APIs are enabling cross-business collaborations to create a value-based market offering for customers. Fuelled by new regulations, such as EU’s PSD2 directive, open banking has gained significant traction around the world. The regulations aim to level the playing field for all financial bodies to innovate and compete in an open financial market.

The push for data sharing in open banking is removing cloud adoption barriers that have plagued the industry. The EU’s continuous effort to improve digitization and cloud computing laws for its financial institutes is a start to the industry’s willingness to adapt to new technology.

As technology develops, Fintechs and banking organizations will leverage APIs, the cloud and its associated innovations to deliver a host of products and services around individual customer preferences. The future of banking will heavily depend on how financial institutes leverage digital technology and data analytics towards building new services.

India’s digitally-powered convenience economy is rapidly becoming the new normal whether it is e-commerce, transport or food. It is against this backdrop that the country’s banking and financial services industry is also leapfrogging into a new digital era.

Today, most traditional banks offer their customers web and mobile sites. When it comes to digital payments, new age fintech startups have defined the space with millions of active users, incredibly user-friendly interfaces and extremely fast service deliveries. While traditional banks initially treated fintech startups as competition, today the mantra is collaboration and co-creation.

Between the two, they’ve ensured banking and financial services are among the earliest adopters of digital strategies. Still, the country’s BFSI digital revolution has only just begun and several aspects are yet to be digitized. The future that the BFSI industry is likely headed toward holds in store the rise of the digital-only bank. It also begs the question – will the bank account remain the cornerstone of all transactions in the future too?

India has over a billion mobile phone connections, nearly as many connections as the population of the country. Of those, the smartphone user base is expected to grow to 520 million users by 2020, predicts a report on digital payments by research firm BCG and Google, according to an Economic Times article. The case for building digital-only solutions is strong, therefore, but it is imperative to examine how mobile banking has worked out so far, what a customer’s future expectations are likely to be, and the challenges of digital banking.

Smartphone owners have their mobile phone either on their person or no further away than 6 feet at most at all times. This, along with the rise of the likes of Google Wallet and Paytm, has pitted traditional banks against the might of technology giants including Google, Apple and Amazon. For now, traditional banks still have the upper hand because all transactions are, at the crux, tied to a bank account.

Whether that will continue to be the case remains to be seen. Banks have invested heavily in mobile banking to both retain existing customers and capture new ones. They initially used mobile banking to let customers check balances, transactions and statements etc, but the kind of services they offer has since evolved. Today, customers can transfer money within and outside the bank, pay bills, recharge their phones, add payees, use mobile wallets and perform several other tasks on their devices. Even so, finance ranks a low 15 out of the average 18 apps that every user downloads on their phone.

If that metric were to improve, the banking and finance industry has to stay abreast of customer expectations in future too and work backward to integrate it into its current digital strategy. That future could involve several additional areas of BFSI being digitized – from personal finance management and card activations to cardless ATM transactions and social media integration.

But there are challenges. For starters, not all mobile banking users download a bank’s app and that skews the data on app versus website usage. The perception of mobile usage also differs globally based on region and age. For Gen X, mobile banking is just an extension of the traditional bank while Gen Y takes a relatively more mobile-first approach. But Gen Z, waiting in the wings, will be the truly mobile-only generation and will take connectivity and online presence for granted. It is for this generation that a digital-only bank probably makes the most sense. Of course, experiments on that front have already begun with Singapore-based DBS’ Digibank, which was launched in 2016. For Gen Z, digital banking cannot merely be an additional feature but a fully-integrated mobile experience in which customers use their smartphones to do everything from opening a new account and making payments to resolving credit card billing disputes.

Traditional banks could lose up to one-third of their market share to digitally-oriented competitors or non-banking competitors by 2020, according to a study by Accenture. Still, jumping headlong into creating digital solutions isn’t the answer. Banks have to consider internal complexities, including the risk of cannibalizing existing business and prepare to transform their culture into one that is more agile, creative and flexible.

In order to build successful digital banking businesses, they have to focus on a few key areas. Well over half of the revenue pool in India comes from current and savings accounts (CASA) and digital strategies should be aligned accordingly. Customer experience must be constantly refined using research and deep real-time understanding of behavior and pain points. New technology and its architecture must be fused with an individual bank’s design, brand, and its business model.

Collaboration and partnerships between banks and startups is the new norm and must continue for the ecosystem to grow and evolve. Banks could consider building a two-speed IT operating model to implement the test-and-learn approach and shorter release cycles – one is the traditional slower but more secure and stable legacy back end, and the second is a rapid, flexible, customer-centric front end. Finally, taking a cue from the pages of the convenience economy startups, banks in the digital era should place due emphasis on marketing their products and services more creatively.

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.

• 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.

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:

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”.

Robotic Process Automation (RPA), which many in the financial industry believe to be the Secret to Digital Transformation, is fast emerging as a highly efficient way to help financial institutions support their digital initiatives.

RPAs are software robots that can be deployed to automate specific tasks a human agent would otherwise carry out. Banks and financial institutions are today building localized centers of excellence deploying bots that can eliminate the issue of monotony by taking over repetitive work and reduce human error. With RPAs human tasks and decisions can be automated freeing human resource that can be utilized for higher level tasks.

There are specific areas where RPAs especially thrive. Processes such as Billing, KYC (Know Your Customer), rule-based lending decisions and Anti Money Laundering (AML) screenings are being revolutionized by intelligent software bots. RPA is the secret sauce banks are turning to make innovation possible without cost overheads.

While RPA is deployed to automate routine, human-intensive tasks, these days it works along with a wider ecosystem made of Cognitive automation which deals with automating non-routine tasks that augment human capability using AI and ML (Machine Learning). Cognitive automation helps organizations discover a new opportunity that is lying within their existing context. A third paradigm RPA works side-by-side is now termed as social robotics which deals with a combination of physical assets, AI, sensors and mobility. This brings up an environment where machines interact with humans increasing the overall capability of the domain.

Where RPAs Thrive

RPAs are already taking over the task of telecalling agents with the deployment of AI Chat boxes. With NLP (Natural Language Processing) AI chat boxes are able to converse with a banking customer as real as a human while being more accurate and fast. From online chat, other point of contact system such as mobile apps, ATMs, information kiosks are also driven by RPAs.

RPAs are especially well suited to handle the explosion of unstructured data which come in handy while triangulating with other data during KYC or AML screening.

Many banks are deploying RPAs in its billing section where every contract is digitized, classified and through AI assisted bots, they are able to automatically generate accurate invoices and also detect revenue opportunities based on SLAs. There are now sophisticated OCR and ICR tools which can read a contract and apply rules without human intervention.

With NLP and self-learning abilities, automated bots are proving to be saviors in a world where competitiveness and regulatory compliance are becoming paramount concerns. Many banks have reduced errors in billing and potential lawsuits from erroneous billing.

RPAs are typically deployed in by financial institutions such as banks and insurance companies to address tasks which involve gathering documents, organizing, searching, real-time matching of events, and analyzed for particular instances. RPA delivers a high degree of operational efficiency, auditability, security, and reliability. In back-office operations, RPA thrives since it can be incredibly precise, and provide detailed analytics of its own actions. BPOs have deployed RPA in varying degree of maturity.

One interesting facet of this “RPA can provide detailed analytics of its own actions” is that HR departments in financial industry are using it to train their staff. RPA can record and re-play the process step-by-step which comes in handy in developing self-learning modules for staff. So a learning and self-evaluation model is thriving in many banks and insurance companies.

The Gateway to Digitization

RPA ability to automate small repetitive tasks to large complex ones make it a perfect vehicle to launch the digitization in banks. Given the fact that redundancy is vital for banks, RPAs help in augmenting the human abilities bringing in a higher degree of check and balance and redundancy.

For banks, RPAs bring an easy alternative of gradually renovate and evolving their processes into digitized versions or launch a completely new entity with RPAs taking the lead role and human agents becoming enablers. There is a thriving marketplace for RPAs from where banks can buy useful bots and integrate with their banking software.

Omnipresence automation

RPAs are not just being deployed in the back end. Client user experience on the web or apps  and contact points are highly customized for each customer. On the fly upselling products and services, alerting compliances, generating invoices on demand is where RPAs shine adding a competitive edge.

RPAs are also enabling display of customized role-based User Interface and service menu options when customers visit their websites.

Human Machine Interaction

Does RPA conjure up apocalyptic imagery where humans are replaced by robots at workplace? This is misguided at its best. RPA augments human capability; it is essentially freeing human endeavor to focus on more important things. Moreover, RPA requires human intervention in decision making and overseeing its operations

Automation of tasks is not a new phenomenon but cognitive AI driven, NLP enabled, self-learning social bots are proving real game changers in the banking sector.