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Transforming banking: The digital way

Transforming banking: The digital way

Banks and financial services firms today are compelled to go digital. While restraint by some is understandable, the change is both necessary and worthwhile

It has been a little over a year since digital transformation topped agendas in the financial sector, around the globe. Banks and traditional financial services companies have often been slow to adapt to the digital revolution, but Deloitte has pointed out in a report that digital transformation can provide a significant advantage. Financial services companies refrain from adoption because new technologies add complexity and challenges to their core services; however, they can innovate to capitalise on the latest advancements and deliver new forms of value to their customers. Most importantly, digital transformation is not just advantageous for banks but also necessary. With the rise of the digital economy, digitally native consumers expect digital banking, for which digital transformation has become a strategic imperative.

The need behind the transformation

The operational efficiency of an organisation is directly related to its technological maturity. Efficiency is the hallmark of digitally mature organisations, and it lets them maintain low overheads. On the other hand, organisations that continue to use legacy systems tend to be underperformers. The reason is clear – and here’s the proof: A Boston Consulting Group (BCG) survey suggested that digital initiatives can help financial institutions cut costs by as much as 80% with proper execution.

These digital initiatives lead to benefits such as convenience and ease of use for consumers, who have no interest in back-end performance improvements (which drive technology adoption in many organisations). According to a survey by American Bankers Association, mobile payments, online budgeting, video approvals, etc. are features for which customers would switch banks. Some of the advantages of digitisation can allay safety concerns too, such as contactless payments and e-services.

Understanding the deterrents

Transformation of any kind comes with challenges – more so when it pertains to digital, as it threatens the way of working of so many people. In addition to these regular ones, financial institutions must find a way to overhaul their complex business models and legacy systems. The entrenched way of working in a financial institution also makes it difficult to understand the potential of digital transformation.

While innovators and early adopters iterate at exponential rates, leading to products and services that are disruptive, not all people in the financial sector operate at the leading edge of innovation – they are more concerned about the rules and regulations and adhere to them, for good reasons. The safety created by this environment makes it difficult to fully grasp the significance of new technology trends, and institutions must find a way to disrupt while staying grounded.

Custom(er)ising the banking journey

Consumers demand an easy, seamless, and fully customised experience. For them, both grocery shopping and banking are transactions – if they can take care of the former from an app, they expect the latter to provide similar convenience. Four out of five financial institutions agree to this trend, according to BCG, and yet 43% of them haven’t developed a digital strategy.

The solution to this starts with data. Banks accumulate details and information about their customers on a huge scale, which can be utilised to create bespoke user journeys that are intuitive and simple. Data analytics can provide extremely valuable insights to help banks understand and serve consumers better in a non-invasive but a pre-emptive and personalised way.

Embracing open banking

The financial landscape has changed drastically over the last decade: banks are not just competing with banks but also with finance-related apps that make basic banking processes look rigid. Opening up an avenue where information is shared securely through APIs with third parties, after getting the consent of consumers, can pave the way ahead. The data can be used by various mobile and app services to help consumers find the easiest way of accomplishing their goals, thereby simplifying their banking experience.

Succeeding at trial-by-transformation

Despite the challenges ahead, leaders in the financial sector have an incredible opportunity to redefine the customer experience. The effects of digital transformation through experience engineering can permeate all levels of a consumer’s life, considering how banking is the backbone of almost all economic activities. Banks that are most digitally enabled can set new standards at an accelerated pace for the industry while making life easier for both their employees and customers.

Originally published on Dataquest India


Digital in COVID times: Experience engineering through domain led digital solutions

Digital in COVID times: Experience engineering through domain led digital solutions

As If the non-digital businesses needed any more incentive, the COVID pandemic is here.
All businesses must go digital.
Connected devices flourish today – 30 billion IoT endpoints are available today and will reach 80 Billion + by 2025.

Digital businesses that are already blurring the physical and digital worlds are a time-sensitive critical paradigm creating competitive advantage through new offerings, new business models, new customer experiences, all enabled by agile relationships.

According to IDG as part of its 2019 digital business surveys reports key findings. Decision-makers across 700+ companies were surveyed, each with an employee size of 14,000 or more.

  • 95% of start-ups have digital business plans compared to 87% of traditional enterprises that were founded 50 years ago or later
  • 55% of start-ups have already adopted a digital business strategy compared to 38% of traditional enterprises.
  • 62% say delivering an excellent customer experience as measured by customer satisfaction scores defines success as a digital-first business.
  • Start-ups can increase revenue by 34% relying on digital-first strategies; other enterprises increase revenue by 23%.
  • Big Data/Analytics (58%), mobile technologies (59%), private cloud (53%), public cloud (45%), and APIs and embeddable technologies (40%) are the top five technologies already under mass implementation.
  • 49% of IT executives confirm the Internet of Things (IoT) plays a key role in their digital business strategies.

It is understood that adoption will only rise in 2021 and beyond, but it is worthwhile to consider ‘what’ a Digital-Business means to the many.

Does digital business exist for meeting customer expectations, or does it enable worker productivity (through mobile apps, AI-assisted automation), or does it assist in managing business performance through data availability and analysis?

There are no wrong answers here because the term – digital business – also includes digitally modifying business processes, developing new revenue streams, and achieving top-line growth through new digital or data-driven products and services.

Following the footsteps of mainstream adoptees mentioned in the survey, are five digital technologies that pilot and prototype in the background. As discoveries come to light, these are arguably covering up new ground and already command our interest. These are Artificial Intelligence, Machine Learning, Internet of Things, Software-defined networking, and Software-defined storage.

The usage of the term ‘digital’ is indeed ubiquitous.

Changing tracks let us highlight critical areas that digital transformations promise to alter.

In a business irrespective of the organizational function the digital transformation targets – Research, Development, Execution, Integration, or Maintenance – the big underlying question never changes. It is:

Are my digital transformation efforts radically changing my customer experience?

It is neither a simplistic answer nor can it be answered in the binary.

To answer the question with a reasonable degree of balance requires leaders to step into deeper terrains. Furthermore, this deep dive perforce calls for an examination of associated strategy areas. Namely,

  • Are the digital transformation journeys domain-led? Said another way, are your digital efforts consciously contextualized to your industry, sector, customer set, and their unique pain points?
  • Are your digital investments guided by customer journeys (which are an assimilation of dynamic repositories of your customers’ touchpoints and personas)?
  • When it comes to service delivery, customers are increasingly adopting digital channels. Does your domain expertise translate these adoption insights to digitize end to end customer journeys with distinctive omnichannel experiences?
  • Does your organization draw advantages from rapid prototyping and agile-first methodology to accelerate the creation of MVP (minimum viable product)? Being able to do this collapses time to market and overall development costs.

All the above-mentioned aspects constitute what can be termed as, ‘Experience Engineering’.

How does one define Experience engineering?

Without going into specific details, experience engineering is a discipline that brings together the art of reimagining digital transformation by architecting and optimizing unique customer journeys.

When such transformations are domain-led, organizations can translate their strategic possibilities into cutting edge practice. This impact happens, more often than not, when customer user experiences are leveraged to unlock the businesses’ true potential.

How does experience engineering become a game-changing practice?

Apart from the competitive edge, as an organization embraces digital transformation it benefits from enhanced employee productivity. The innovative mindset consequently finds its way into more efficient internal processes. One culmination point of this positive momentum is in the creation of ‘data-decision-making’ cultures.

In the longer run, measured through enhanced customer experiences and improved workforce efficiencies, it is experience engineering that is at the heart of a successful digital transformation


FinTech Will Lead India’s Financial Formalisation

FinTech Will Lead India’s Financial Formalisation

The Indian FinTech market has scaled great heights in the past few years, both in terms of funding and adoption of emerging financial services solutions. India ranked 2nd globally in FinTech adoption with the percentage of users reaching 57.9%.

Deloitte has already pegged FinTech as one of the fastest-growing sectors due to increasing number of preferred digital channels for financial investments and wealth management[2]. In fact, the overall transaction value in the Indian FinTech sector is projected to reach $137.8 billion by 2023, as opposed to $66.1 billion in 2019[3]. The weather has also run favourably for the sector on several fronts.

First, customer experiences have undergone sweeping transformation by the non-financial tech firms, leading to the rise of increased digital expectations from financial firms/service providers. Second, the Indian regulators have now enabled a positive environment of knowledge sharing for FinTechs in addition to several initiatives aimed at enhancing the country’s digital infrastructure. Finally, the adoption of new technologies such as artificial intelligence (AI), machine learning (ML) and big data, fueled by the rising internet and mobile penetration has empowered financial organisations to tackle the pressing pain-points of the time.

Quite understandably, innovation in the FinTech sector has taken the world by storm. Moving forward, the open-API economy may even see surprising participation from the non-financial sectors such as telecom, retail, and power, who can leverage open-data as a means to boost their portfolio by foraying into financial services.

Before we get ahead of ourselves, let’s dive into the key technology advancements India needs to be ready for along with the need to propel indigenous R&D and IP creation.

How FinTech is leading the revolution

A large portion of India’s population are excluded from the formal financial system, due to a multitude of reasons. Lack of awareness about the benefits of financial services products, the inability of traditional financial players to serve this segment in a cost-effective manner and the lack of a national infrastructure to support future progress being the key.

However, since the launch of schemes such as Jan Dhan Yojana, and Direct Benefit Transfer, there has been a significant rise in the awareness of these products. The introduction of the Goods and Services Tax (GST) regime has also been a mindful step towards formalizing the unorganized sector of the Indian economy with several FinTechs leveraging its generated digital footprint (i.e. GSTN), standing at an impressive ~1.21 crore registered entities. Jan Dhan Yojana, the flagship initiative by the government has also seen a significant uptick in the number of people with bank accounts in India, currently standing at 320 million. Finally, access to platforms such as UPI through the ‘Digital India’ initiative has allowed banks, wealth, lending and insurance players to innovate freely around the pressing consumer problems.

These, in turn, have led to the rise in demand for financial services solutions, thereby creating viable market opportunities for FinTechs.

Driving excellence through collaboration

If we were to take part in the collaborate-vs-compete debate, the growing trust in the FinTech industry has brought challengers and incumbents together to explore more opportunities for new revenue streams, and rapid go-to-market solutions. In fact, FinTech challengers today have emerged as sophisticated competitors. The interactions between them, incumbent players and global experts are forming ecosystems that are replacing traditional bilateral partnerships designed to solve problems. It is interesting to see so many partnerships and the launch of new brands such as Kotak 811, iMobile and SBI’s YONO. ICICI powered Neobank initiative called ‘Open’ is also an example of a similar move.

To achieve the middle ground between innovation and regulation, RBI’s proposed first-of-a-kind regulatory sandbox for FinTech start-ups offers the right kind of boost needed to help this industry achieve its potential.

That said, the road to collaboration is not free from hurdles. While incumbents struggle with the pace of innovation and the obsolescence of their legacy systems, startups are feeling the brunt of bureaucratic, legal and cultural issues when working with these institutions. Nevertheless, the maturity of collaboration is all part of the ‘one-step-at-a-time’ revolution, sure to lead to a promising avenue for growth and financial inclusivity.

Each must welcome the wave of change with a humble heart, open mind and embark on the journey of unlearning the traditionally accepted models.

This article has been published in BUSINESSWORLD.



Evolution of chatbots in financial services with Natural Language Understanding

Evolution of chatbots in financial services with Natural Language Understanding

Technological advancements over the last few decades have prompted the exponential growth of customer service across industries. The Amazons and Alibabas of the world have set new standards for customer expectations. With real-time updates, ease of information exchange, and round-the-clock customer service, industries are building solutions for seamless transactions. To meet these demands, artificial intelligence (AI), machine learning (ML) and chatbots are becoming a priority for businesses, including the banking and financial sector. Chatbots are helping organizations reduce costs, provide quick services, offer transactional support, and serve as a medium for upselling products.

The digital-era calls for instant communication and 24/7 connectivity. Chatbots enable a better communication channel for users to engage with business services. By using application programming interfaces (APIs), chatbots integrate with data management platforms to analyze data for end users. In the financial sector, consumers of data insights range from end customers to banking CXOs.

Essentially, chatbots are artificial intelligence (AI) powered virtual assistants that are trained with a conversational dataset to respond to questions. Initially, chatbots were trained on a fixed set of questions (rules-based chatbot) with which customers could interact like a frequently asked questions (FAQs) system. But as technology evolves, chatbots have evolved from sounding like a robot to emulating human speech. Natural language Processing (NLP) plays a major role in developing conversational analytics platform to provide real-time insights for decision making. NLP provides an efficient means of monitoring end user sentiments.

In everyday banking, chatbots find their use across varied functions and operations; eliminating the need for customer service agents. Gartner predicts that by 2020,  85 percent of customer service interactions will be handled by chatbots. The technology is being used to provide customers with a 24/7 support system, help with customer onboarding, KYC forms, resolving queries, and selling new products or services. According to a study by Juniper Research, as automated customer service evolves, by 2023 bank operational cost savings via chatbots will reach $7.3 billion. Future banks would be saving 862 million hours, equivalent to approximately half a million working years, as per Juniper.

Ally Bank was one of the first few banks to implement a chatbot for their customers. Introduced in 2015, Ally Assist – a virtual assistant within the Ally Mobile Banking app, helped customers request account summaries or transaction history, make payments, peer-to-peer transfers, deposits, and monitor savings. Using machine learning, Ally Assist analyzes spending patterns to predict customer needs and provide relevant help topics and messages. The assistant uses NLP to address common customer service queries.

Bank of America introduced Erica – a virtual financial digital assistant to allow customers to access information on account balance, credit reports, send notifications of account changes, and schedule transfers among many other functionalities. Six million people are reported to be using Erica and over 35 million client requests have been processed through their mobile app.

While chatbot technology continues to advance, it comes with its own set of limitations and learning that are changing over time. As more and more organizations develop use cases and expand functionalities, chatbots get smarter. The major limitation of a chatbot is still in its dialogue capabilities. Conversations with a bot do not feel natural, is impersonal, and lacks empathy and context. Bots cannot process multiple questions or conversations at the same time. They are programmed to give the right answer only when they are asked exact questions. In the real world, this creates a problem wherein different dialects, accents, and jargons/slang aren’t picked up by chatbots.

To better understand these complexities, chatbots are integrated with natural language understanding (NLU). A subset of NLP, NLU focuses on handing a narrower data set, converting unstructured inputs (human speech) to a structured form that is machine-readable (structured ontology). NLU communicate with regular end users to understand their intent; going beyond understanding words and interpreting meaning. With NLU, chatbots can enable better conversational flow using clarification techniques, detecting sentiments, and predicting customer questions. The bots now have the ability to understand context and deliver ‘contextual insights’ based on conversational banking. Fidor’s chatbot uses conversational AI and NLU to deliver real-time personalized and efficient interactions with users. It is one of the first digital banking service providers to implement virtual assistants within its technology stack. The chatbot is programmed to detect sophisticated language nuances to understand user requests and have a natural conversation.

As chatbots continue to advance, banks will need to shift their focus from mobile banking to a friendlier conversational user interface (CUI). Conversational interaction is seen as the natural evolution of mobile baking. For banks to succeed with this technology, a fundamental change is needed from both, consumers and banks, in the way they interact with each other. Not all banks are ready to make the shift to chatbots and consumers are just warming up to the idea of speaking finance to a bot.


Robotic Process Automation in Banking

Robotic Process Automation in Banking

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.