Customer Service. Expectation vs. Reality.

As an enthusiastic worldwide traveller, I wanted to find a financial product that I could use abroad without paying the exorbitant exchange rates and conversion fees that would further inflate the spending on my plastic cards. After some research, I decided to switch my current account to another bank that offered free cash withdrawal, zero fees on overseas card usage, and other benefits, such as travel insurance. I decided to do the application online. After filling out all the information, when I clicked the submit button, I received a pop up message on my screen saying “Please visit your local branch to complete your application”. I was really disappointed that today, in the 21st century, when there are so many emerging technologies in the market, I still had to go to the branch to finalize my application. Needless to say, I didn’t complete my application and never switched to the other bank.
According to a 2011 American Express Survey, 78% of consumers have bailed on a transaction or not made an intended purchase because of a poor service experience. Furthermore, according to Ruby Newell-Legner in “Understanding Customers” it takes 12 positive experiences to make up for one unresolved negative experience.

I was determined to see what else was out there, believing that FinTech companies would offer a product that I was looking for. One day, chatting with a friend over a glass of wine, I heard about a digital wallet that she learnt of during a start-up conference in Portugal. After researching the company and product, I decided to give it a go. I was super impressed that on-boarding took literally 5 minutes. The wallet application used facial recognition, and its algorithm was quickly able to match my face with my “ID” photograph to validate my identity. I completed the whole process using my mobile, without leaving the house and right away had the option to exchange money in more than 130 currencies and wire transfer money abroad without any fees.
I found myself wondering why banks struggled to offer a similar seamless customer experience. The answer lies in complexity: While banks aim to offer great customer experience over all touch points – online banking portal, mobile app, call center and branch – their operational complexity (so much more than that of a FinTech company), hampers them from adopting the right technologies and processes with speed. Says Klaus Schwab, founder and Executive Chairman of the World Economic Forum, in The Fourth Industrial Revolution, “Disruption also flows from agile, innovative competitors who, by accessing global digital platforms for research, development, marketing, sales and distribution, can overtake well-established incumbents faster than ever by improving quality, speed or price at which they deliver value. This is the reason why many business leaders consider their biggest threat to be competitors that are not yet regarded as such.”
Today, customers expect to have a meaningful conversation with service agents and have their problems solved quickly, without waiting in line or being handed over from one executive to another. Yet, according to consumers, customer service agents failed to answer their questions 50% of the time (Source: Harris Interactive). This is the result of the fact that agent performance is measured by average handling time, rather than the success rate of interactions with customers.
The “every time right” multichannel customer experience is a top priority of most banks. Working with financial services clients in Europe, I have observed that there are two ways in which banks adopt disruptive technologies to improve customer satisfaction:

  • Strategize in the boardroom until all the relevant stakeholders agree on a solution to provide excellent customer experience on all banking channels. This is an ideal scenario, where all the stakeholders are on-board, and in agreement. Unfortunately, the parties rarely reach universal agreement, because they are usually guided by different priorities and agendas.

  • Go for the low hanging fruit and implement small chunks of technology, such as RPA, augmented desktops for agents, chatbots, facial recognition etc. Such implementations bring quick wins such as cost reduction, lower error rate, fewer customer service interactions, faster resolution, and most importantly, high quality engagement and experience.

The second option, in my opinion, is what banks should take to improve customer experience. Since existing customers are more likely to purchase other products and services from the bank, even at a premium, the importance of serving them well cannot be overstated.

‘Innovation Investment in Banking’ Gets the Dollars It Deserves!

Investment in innovation is on the rise, and the results are starting to show. Banks are changing the customer experience far more systemically than in the past and the race for customer excellence is hotting up. Winners and Losers are on the horizon.
SitenYonet by Deniz Bank in Turkey is a holistic residential property management solution that has replaced multiple standalone web applications for communication, online monthly payment and residential debt follow-up in the apartment and site management realm. It has brought all of these services together in a single offering.
Intesa Sanpolo in Italy has launched its ‘Customer Journey’ project to create a differentiated digital experience, offering multi-channel communication and service delivery opportunities. Communication supported by big data analytics and a customer-centric CRM infrastructure that operates in real time, allows Intesa to provide significant customization based on a customer’s interest, use of bank’s products and information on life events.
With the integration of digital technology in the front, middle an back office of the bank, day-to-day operations have undergone a seismic shift in some Financial Institutions and the long term strategic vision for the future is beginning to take shape. Large technology organizations such as Google, Amazon, Facebook and Apple (GAFA) are emerging as strong service delivery options that champion customer experience. Threats from these organizations combined with those from nimble fintech companies are compelling banks to invent ways to engage deeper with the customer, exemplified by initiatives such as Deniz Bank, Intesa and many others.
Banks and Financial Institutions are adopting a three pronged approach to ensure their long term viability. These are – evaluating all the possible disruptions; how these disruptions impact their revenue, positioning, and operating model, and how they can create options and hedges against any negative shifts while taking advantage of new opportunities.

To navigate and deliver the future state model for the Financial institution requires dedicated efforts and significant systemic investments. A recent survey by Infosys and EFMA on digital banking examined how banks are investing in innovation. A chief innovation officer or executive to spearhead the innovation process is key for any organization’s innovation strategy to succeed. Research findings reveal that the presence of such an executive has a direct correlation with the size of the organization. About 67% of the large financial institutions (>$50 Bn) surveyed had a chief innovation officer as opposed to only 9% firms in the <$1Bn bracket.

Banks have been progressively increasing their innovation investment over the years, but in the past 12 months this has accelerated. About 79% of the respondents surveyed increased this investment this year. The areas that saw the maximum increase were channels and customer experience. Large organizations (>$50 Bn) reported the greatest increase in innovation investment in the area of customer experience, followed by channels and processes. Amongst the biggest challenges cited in pursuing the innovation strategies were legacy technology, system integration and the resources of time and cost.

With an increasing threat from new players, banks are being pushed to change. As their traditional services get commoditized, those that will survive and thrive will be those that successfully move from catering to the pure transactional financial needs of a customer to purposefully integrating services to engage customers’ holistic requirements around a topic or life stage. An example of this is a home loan provider partnering with a property dealer to become the one-stop-solution for a customer’s housing needs.
Banks and Financial Institutions are suitably investing in their innovation strategies to get there, as seen in various cases across the globe and as corroborated by research findings.
To power this innovation, investment in emerging technologies has also seen a significant upturn. According to the Infosys and EFMA research, traditional technologies such as Security, Data Analytics, and Cloud currently score higher on the investment priority of organizations when compared with disruptive technologies such as Blockchain, Artificial Intelligence, Robotics, and the Internet of Things. However organizations are also investing in these, testing them in specific but varied scenarios across the industry.

  • AI and Machine Learning are witnessing abundant application in customer service enhancement through chatbots and in fraud management.

  • RPA has been successfully employed for automation of alerts and notifications in several organizations.

  • IoT is being used for debit / credit card security and in voice-first transactions.

  • Although in its early days, Blockchain is already being used for digital identity management, smart contracts and international remittances.

These technologies are expected to further augment change. In the race to provide greater personalization, banks of the future will continue to harness the potential of ever-increasing data assets through advanced analytics. At the same time these organizations will need greater cybersecurity expertise to safeguard data assets and lastly, the importance of cloud technology is key, supporting the storage needs of real-time access and analysis of data. This group of technologies provides the backbone for the disruptive times to come.

Do AI technologies question the basis of humanity?

Working in Information technology in this era of technological disruption makes this a bitter sweet moment in time for me – incredibly interesting for the technology professional in me while incredibly confusing for the thinking human in me!

Wondrous AI

Nearly every day we hear of the wonders that AI technologies are bringing to the world, for example:

  • Computer vision is allowing entire industries to look at autonomous capabilities – look at how this is revolutionizing personal transportation.

  • Conversational abilities of machines using Natural Language Processing (NLP) and Generation (NLG) are redefining how humans interact with machines, in idiomatic language.

  • Robotic process automation is bringing in unparalleled efficiencies via a digital workforce (software robots) by automating repetitive and manual tasks.

  • Advanced Machine Learning (ML) is bringing exponential benefits in analyzing big data and providing actionable personalized intelligence, at scale. Variants with training, clustering, reinforced learning are all incredibly powerful things.

  • And the most interesting of all AI technologies, Deep Learning, allows machines to learn without being explicitly programmed for it – this mimics a human’s ability to learn, think and perform.

Without question, AI technologies and a digital workforce will surpass human productivity and may in time surpass human ingenuity as well.

Cogito ergo sum (I think therefore I am)

In direct contrast to the wonders of AI technologies, is its ability to question the fundamental basis of humanity – be it philosophy, morality, or other dogmas that humans spend a lifetime studying.

  • “Cogito ergo sum”, a philosophical concept coined by Rene Descartes, which means “I think, therefore I am” is considered one of the tenets of philosophical enquiry. What does this phrase mean to humanity in the age of thinking machines?

  • Another quintessentially human concept is morality, that allows humans to distinguish between right and wrong. Take the example of soldiers on a critical mission behind enemy lines, the success of which could lead to enormous gains in lowering terrorism globally – the soldiers encounter civilians while on the mission – the soldiers can choose to either kill the civilians or release them; if the civilians are not innocent this decision can lead to the death or capture of the soldiers. So the moral dilemma here is whether the greater good is served by making the difficult decision of executing civilians. In an age where many of these tasks will become automated and decisions will increasingly be taken by machines, how will one explain such moral dilemmas and the course of actions that humans face daily?

  • How will humans codify the interpretation of law, when this interpretation is subject to a nuanced understanding of humanity and an evolving view of “Justice”?

The impact on human society, of AI technologies, will undoubtedly need a rewrite of our social, financial and legal frameworks, globally.

Truly human

  • John Keats says, “Scenery is fine but human nature is finer” and Goethe says, “Our foibles are really what make us lovable.”

  • In the age of AI technologies, I believe, our ability to feel, to emote, to tell stories, to challenge, to inspire, to build relationships, to understand humor and satire, our foibles and idiosyncrasies are what will make us truly human!

Top 5 Strategies to shift budgets from maintenance to innovation

On average 65 to 70% of a bank’s IT budget is spent on keeping the lights on, in IT maintenance tasks.
IT budget for innovation led activities is often limited to 10 to 20% of a bank’s budget – perhaps even lower % if the planning horizon of the organization is short term!
Here are my top 5 strategies for shifting budgets from maintenance to innovation –

Understanding the cost base in detail

While not a strategy in the truest sense, understanding what is software maintenance and how much it costs – arming oneself with the right information on current IT maintenance spend is an important step. Typically, software maintenance spend is categorized into Bug fixes (largely reactive), Changes to improve performance or usability, Small sets of Enhancements, and On-going Preventive maintenance.
Most organizations still use the omni-present spreadsheets to tabulate their IT maintenance budgets and actual expenses. Using a technical business management solution like the one from Apptio allows CIOs to understand the cost base at a granular level and allows for a data driven approach to planning IT maintenance budgets.
Proofpoint – Look at how understanding the cost base helped XXYY

Usage of cloud based infrastructure and open source tooling

Many organisations still depend extensively on on-premise installs of software and hardware. Likewise many organisations still use prohibitively expensive, licensed software.
Moving applications from on-premise to a private cloud deployment and similarly from licensed software to open source software options will free up a significant chunk of the IT budget.
Hence this is a critical strategy to formulate and execute!

Sensor based assistance to the human workforce

Analysing processes, typically unearths various repetitive and effort intensive manual tasks within the IT organization.
Using sensors to provide automated inputs, deploying software robots to automate these processes and using human skills to deal with only exceptions in these processes are ways to improve productivity of the human workforce – it also frees up the workforce to focus on more complex tasks.
And so this is another critical strategy to formulate and execute!

Automating IT Operations

Monitoring of IT Operations is another manual effort intensive area in many organisations.
Automating the monitoring of IT operations as well as diagnostics and prognostics using AI based technologies will enhance productivity of existing FTEs as well as free up some FTEs over a period of time to undertake other higher order tasks.
Business processes, typically consist of various repetitive and effort intensive manual tasks within the IT organization. Deploying bots to deal with this is an excellent way to free up parts of the workforce and improve productivity of existing FTEs.
Ergo this too is a critical strategy to formulate and execute!

At the absolute top of the list is shifting the staffing mix in IT organisations

If one looks at the staffing of an IT organization, on average 80% of the staff would be for helpdesk and application centric roles and only about 20% would be focused on strategy, architecture, and other specialized IT roles.
These ratios need to be inverted such that the organization internally is optimized and lean. And ensuring key specialized roles are well staffed, also increases the focus on longer range planning horizon instead of fighting fires all year long!

Solutions For Intelligent Automation Continuum


At Google I/O, Google’s annual developer conference, Sundar Pichai showcased Google Assistant making a call to book a salon appointment. With a distinctly human-sounding voice and smooth conversation style, Google Assistant conquered a key digital outpost at the event!
While this is just one instance, we all come across many more technological disruptions while simply browsing through our Twitter or LinkedIn feeds. This fast pace of change along with emerging technologies like artificial intelligence (AI), machine learning (ML) and deep learning (DL), is impacting each one of us. In a good way of course! It’s also driving businesses to be fast, agile and scalable in an exceptional way.
Organizations today, have no option but to adapt to this by embracing new technologies. Technologies such as AI and automation is what will help businesses improve customer experience, staff productivity, and cut costs. However, all my discussions with clients have revealed that the biggest challenge they grapple with is determining where to begin and what steps to take to implement AI/ML and automation.
The study from renowned research firm Vanson Bourne1 on The State of Artificial Intelligence for Enterprises concurs that while 80% percent of organizations have invested in some form of AI technology, 91% see barriers in implementing it. The study highlights that the most significant reasons are lack of IT infrastructure (40%) and lack of talent (34%).
To help our clients overcome these obstacles effectively, we at EdgeVerve have set a lucid path by viewing this adoption as a continuum or journey with distinct and clear phases.


When a business is transforming digitally, we segment the automation journey into three phases – deterministic, predictive and cognitive.

Caption: The automation and AI journey broadly divided into three phases

The first phase, deterministic automation, focusses on saving time and effort by utilizing robots to automate repetitive and rule-based business processes. As the starting point in the automation continuum, Robotic Process Automation (RPA) capabilities identify inefficiencies and automate a part or the whole process with bots. For example, RPA can be used to automate stock analysis, invoice despatch, credit card reconciliation or refund process in the banking sector.
Going beyond RPA deployment, when a business starts analysing data to make future predictions, it’s considered to be in the predictive phase. Here, AI and automation can be implemented with analytics-driven operations to predict failures and create a framework that suggests actions and recommendations. For example, actionable insights can help procurement officers identify the right vendors at the right time to make a purchase.
Building and managing this repository of knowledge over time to derive evolved patterns and aid business decisions, propels an enterprise towards the cognitive phase of automation. The use of ML and natural language processing (NLP) and pattern analysis decrease dependencies on external factors and assist in driving faster growth. For example, a procurement officer can benefit by knowing the effect of a socio-economic problem in an area as well as suitable actions to mitigate it.
EdgeVerve enables this journey by facilitating a smooth transition for enterprises with its technology solutions. The deterministic automation capabilities are provided by AssistEdge, our efficient RPA platform. The predictive and cognitive capabilities are achieved with our next-gen AI platform, Infosys Nia™.


EdgeVerve solutions offered at each stage ensure a gradual move from deterministic to predictive, and finally the cognitive stage. In the deterministic stage, AssistEdge covers the full spectrum of automation. Leading in the RPA space by revenue and number of bots, AssistEdge is a complete service for building and implementing RPA. It has successfully helped save more than $2 billion for our clients.
The move to the next phases – predictive and cognitive – is enabled by Infosys Nia™, our AI platform where big data analytics, machine learning, knowledge management, and cognitive automation capabilities converge to offer end-to-end solution. It drives and maximizes digital transformation for an enterprise by enabling a wide set of industry- and function-specific solutions.
The recent introduction of AI-powered Business Applications by EdgeVerve, has ensured that the shift to a digital future is faster. These are a set of plug-and-play industry solutions that integrate seamlessly into the existing infrastructure. Built on the foundation of Infosys Nia, they utilise the existing enterprise knowledge and address specific business concerns.
For example, the data-driven, intelligent Nia Loans Loss Mitigation Business Application can assist lenders and bankers to predict (using existing lending systems and data) and provide early warnings about customers likely to default the next month or even the next quarter. Well, it doesn’t just stop there. The Application offers actionable insights and creates negotiable strategies, giving recovery agents an upper hand in negotiations.
These technological solutions by EdgeVerve aim to lead the way and guide enterprises in their Automation Continuum journey. With end-to-end solutions that improve operational efficiencies in the deterministic phase, avoid business challenges in the predictive phase and manage knowledge to reduce dependency in the cognitive phase, the digital future for an enterprise appears optimistic. Our clients are in safe hands!

Machine Learning In Visual Search For Emerging Markets

The unorganized retail sector is a massive space for technological advancement for optimizing various business processes. The diversity of complexities offer several opportunities for sanitization of prevalent practices. One such area is planogram compliance. Top Industry analysts estimate that internal process failures cause up to 50% of inventory distortion, and prevention of such inefficiencies can result in around 7-8% increase in sales.
While modern trade is picking up in markets such as India and China with supermarkets sprouting in big and medium-sized cities, a substantial section of the demand chain still remains “distributed” in nature. CPG manufacturers still rely heavily on distributors to cover the last mile with numerous small-sized retail outlets, such as mom-and-pop stores.
It is a known fact that CPG brands even after implementing multiple monitoring controls, do not have sufficient control over retail execution at mom and pop stores. With India itself having over 7 million known ‘kirana’ stores, it gets tedious to apply controls at scale. Particularly for food products that need special storage conditions, this becomes an overwhelming challenge for brands. CPG companies often provide such outlets with branded refrigerators for maintaining soft drinks at the appropriate temperature, or with smaller coolers for items such as chocolates. However, several challenges exist with respect to the retailer’s actual compliance with the steps necessary to keep such products in a desirable ambience. Such challenges include but are not limited to the following –

  • The retailer might not keep the refrigerator powered on (or at the correct temperature), in an attempt to save electricity bill

  • The shop owner might remove the refrigerator from the shop and take it home

  • The retailer might be keeping products of competitors in a refrigerator provided by a brand.

Each of these situations result in non-compliance to Planograms, often referred to as “Brand Contamination”. All of these challenges can be tackled in a different manner. For instance, smart coolers empowered with Wi-Fi or other IoT technologies such as temperature sensors and accelerometers can track whether the cooler is being maintained at the right temperature, or whether it has been moved from its designated location in the store.
CPG companies often hire third-party data-syndication providers for conducting in-store audits, which involves an auditor visiting a certain number of stores in an area to visually verify whether the outlet is adhering to the planogram. However, there are inherent shortcomings to this approach –

  • Such third-party auditors are expensive and are billed on a time/ effort basis.

  • Due to the above, the CPG company can only afford to have them cover a small sample-space of stores, and extrapolate the data to represent an entire region or market – which is not accurate.

Therefore, a more suitable solution in emerging markets is to utilize the distributor sales force for conducting such audits. Sales representatives frequently visit retail outlets for taking orders, delivering shipments, collecting payments – on an average, one store visit per week. Their in-store responsibilities can be extended to conduct retail execution related surveys. But a factor causing minor deterrence in this approach is that sales representatives may not be fully relied upon due to their lack of ability in conducting such audits at the same level of competitiveness as a professional auditor.

This leads to a technology-based solution enabling a sales representative to carry out such audits effectively and accurately – Visual Search (Image processing). The sales representative can carry a smart phone issued to him by the distributor, and simply click a picture of the shelf or the refrigerator containing products of the brand that he is auditing for. This image can then be sent to the brand in two ways –

  • Instantaneous: In this case, the sales representative can immediately get feedback on the processed image as to whether the planogram is acceptable or whether there is brand contamination, and what action needs to be taken to correct it.

  • Batched: In this case, the sales representative can synchronize multiple such images with the server once he has returned to the office, but won’t be able to take any immediate action as the processing will be delayed.

Certain food products such as soft drinks may be very difficult to visually differentiate, e.g., Cola-flavored soft drinks will have similar color, and can’t be determined especially if the bottles are placed in a way that their logos are not visible. In such circumstances, standard image-processing algorithms such as SIFT or SURF may not work reliably, since they rely on techniques such as feature-point detection. Alternately, Machine Learning based algorithms can work more effectively in correct identification of a brand’s product versus a competitor’s product in such scenarios, to give precise information on brand contamination. Deep-learning techniques have been found to be more accurate than traditional algorithms (which have ~20% error-rate), thereby also reducing the cost of conducting manual audits.
The analytics generated via such automated visual search can produce powerful insights on promotion effectiveness and retail execution for brands. It brings a new dimension by adding qualitative parameters to existing sales data, for better insights. For instance, a newly launched promotion may not have been effective in certain regions due to unfavorable in-store ambience. This critical piece of information results in actionable decision-making, leading to higher on-shelf brand visibility. Eventually this leads to an uplift in sales for a brand. EdgeVerve offers these capabilities to retail businesses via its Artificial Intelligence platform Infosys Nia™, which when bundled with its sales-execution product TradeEdge can provide powerful insights through machine-learning. In today’s competitive landscape, it is becoming increasingly difficult to drive operational efficiency at reduced costs. The businesses which employ the right technology at the right time can be ready for a successful future. So are you prepared?


  • Gartner: “Image Recognition Can Help Consumer Goods Manufacturers Win at the Retail Shelf”, Ed Porter, Tuong Huy Nguyen, 21 Feb 2017.

  • Stanford: “Deep Learning Approach to Planogram Compliance in Retail Stores”

Articial Intelligence’s Role in Digital Banking

Artificial Intelligence and Digital Banking: what is the correlation?

The first question which may arise is what has Artificial Intelligence (AI) got to do with Digital Banking? But does it surprise you to know that according to a survey conducted by Narrative Science in conjunction with the National Business Research Institute, 32% of financial services executives surveyed confirmed using AI technologies such as predictive analytics, recommendation engines, voice recognition and response. Thus the answer to me is simple – AI is the blood stream of digital banking i.e. without AI there is no digital banking.. Let’s try and understand this context a little better. One of the pillars of digital banking is to put the customer in the center of any banking interaction. Let’s try and imagine how this happens in a digital interaction say in Amazon. As soon as you log into Amazon, you get a summary with the following information:

  • What are the incomplete shopping items which is lying in your cart

  • Whether you would be interested in similar items or new models of the same (similar items which is there in your cart)

  • What are other customers like you are shopping for

  • And thus what can be the new recommended items from their side

So what is Amazon trying to do?They are trying to understand your buying behavior vis-à-vis similar patterns by customers like you and thus are proactively trying to suggest the best thing for you to do based on this understanding built on AI. There are a couple of elements of AI at work: predictive intelligence, deep learning and cognitive intelligence. In layman’s terminology, all this is trying to mimic the way we (humans) think –

  • We collect basic data to analyze the situation – here it is simple information of what is lying in the cart

  • Then we try and correlate with items in the cart, my earlier buying behavior and try and predict what is the next set of items which I may be interested in – predictive analytics comes into play

  • When we try to find out a pattern between me and customer’s like me and their shopping/ buying behavior we are using learning algorithms. The more complex models used in learning from ‘similar’ transactions it tends to be called deep learning which basically works on certain patterns which designate customer behavior

  • Based on the above patterns we use Prescriptive analytics derived out artificial intelligence to recommend what would be the next best action for the customer

  • The data inputs from the same can come from machines (IoT) which could be in these scenario from their mobiles which pass on location information

Now the Amazon experience could be bettered by a bank which has much more information on the customer. Bank has a lot of structured and unstructured information internal to the bank about a customer’s buying and usage behavior. All the customer does as part of his entire lifestyle in many ways gets captured in the transactions / touch points the customer interacts with every day in his life. He/ She uses Amazon to buy electronics finally ends with the customer realizing the same with a banking transaction using a debit/credit card to complete the transaction. Now the bank has the structured data that the transaction happened in Amazon, and has the unstructured data (in the transaction description) which is the provider for the article purchased (which can be tagged to what that provider essentially deals with). Here the bank has the edge of knowing what he bought from Amazon, Walmart, Best Buy etc. and then use artificial intelligence to look at the specific customer’s buying behavior and match it with similar patterns of similar customers and then suggest the next best product to buy, from a merchant who will give him a better deal and also the bank that will give discount if a specific instrument is used for making the purchase.
According to Analysts ‘Smart Advisors’ driven by AI would be a huge area of impact in banks – there would be advisors both for customers as well as the bank executive supporting the customer. This would change the entire face of product selling and servicing in a financial institution. Smart advisor would be a robot which driven by Natural Language Processing capabilities (NLP) and the intelligence would be driven by machine learning AI models which would drive its intelligence. It would provide the intelligent output through chatbots or voicebots as the case maybe. Thus these AI driven capabilities would be basically the ‘Aladdin’s Genie’ for the customer or the executive of the bank. An Asian giant has launched a digital-only bank which uses chatbots to provide each customer with a very differentiated intelligent advisory on product and service usage.
AI is becoming a very common tool in fraud management and anti-money laundering (AML) areas as well. Here also using data from diverse sources, using both structured and unstructured data, the endeavor is to find out if the transaction happening follows the pattern undertaken by the customer. The pattern arises from the type of transaction, region, time, the purpose of the transaction, the transaction initiators machine identifiers etc. all to understand whether it fits into a pattern which is usual with this customer (or customer’s like him) or if there is anything out of place. As soon something is detected to be out of place, it raises an internal alarm, takes action to gather more information on the transaction perpetrator and tries to match with the identity of the customer associated with the account. If this does not match, the transactions are barred from proceeding. All this is done in milliseconds with the AI engines working vigorously in the background. The tool or intelligence is considered better when there are less of false positives and this is tied to the maturity of the algorithms being used.
AI can transform boring tasks which are people intensive and take place in the back office of banks into routines of algorithms that help in improving productivity phenomenally. But this is a sensitive area as it needs a cultural change in banks. But many repetitive processes can be put into the domain of AI robots who can easily learn the process, for e.g. reconciliation process for ATM transaction takes data from 4 different subsystems in a bank and runs matching rules to tick off the transactions which exactly match. When there is a mismatch, human beings apply patterns (which is based on their intelligence) to figure out whether it matches with the patterns applied. With AI robots can be trained on these patterns, and the more they learn, the better they become at matching based on those patterns. With Advanced AI they can go beyond well set patterns and can predict a particular reconciliation need which may have happened due to some peculiar circumstances on that particular day. Advanced AI can predict what would be the impact of a hurricane on a particular set of companies and thus can help equity traders with buy or sell decision for those bourses.
Digital Marketing (you can look at banks becoming Amazon) is another area where we think there would be a huge impact in terms of AI in banking. As the mantra of life style banking (embed banking into everyday life actions of consumers) spreads, digital marketing driven by AI would be extensively used by banks to sell not only bank wares but extended bank wares as well. For e.g. concierge service for booking a restaurant for your wedding anniversary and making sure you have a limousine booked to take you there. The restaurant is the highest rated one of the cuisine which you most frequently dine/ order outside and where the bank has the tie up to provide you a significant anniversary discount. Such offers would be identified for you specifically using AI, and delivered to you using digital content marketing tools in the channel of your choice (which again is AI driven).
But there are significant challenges in converting the AI vision into a reality. The first of the challenges is the data challenges which is internal to the bank. Many banks (especially the larger ones) have BI and DWH practices. But the existing data quality within the bank is itself a challenge, and thus to use that data and provide correct insights is a bigger challenge. The second significant challenge is that data from each system is so varied, that a lot of massaging is required before a meaningful whole can be made of a single customer’s data from myriad systems. This means it takes time and significant amount of processing before an insight is generated for the customer. The passage of time means that the insight loses its significance as the context may have changed. Secondly, in banks there is a tremendous amount of human resistance to AI and automation. The reason is very human. Bank employees feel very threatened in terms of their job when thinking of implementing such technologies which may eventually replace them. This is a very real fear, and thus it leads to many of the projects falling through because of the severe resistance and non-cooperation in implementation of such projects. But challenges of this nature are nothing very different from what we had encountered while implementing core banking solution for the first time. So the response to the challenge is also very similar. Show the advantages of implementing such a technology, positive impact on bottom lines, hence salary etc. Not only that, it makes them more relevant in the market because they have implemented something that would be adopted by all in the future.
Every industry, every RFP is asking what we can do as a solution vendor to help in achieving their drive to achieve AI and seamless automation. This means this is a future direction which is very important for the banks for the future. As more and more banks implement it and learn to benefit from the same, we will have more and more proven case studies about their success. It would become a norm for all banks to follow suit (though the degree of sophistication for each of them would be different). Statistics predict almost a 70-80% increase in AI and automation projects in next 3 years.
Thus the banks that want to be ahead of the curve should start the process now. The process steps are very simple. Identify on one hand your readiness with the data, and how you can move in the journey of providing clean data and then next in priority would be to provide clean data online, real time. Once this is identified the next step is to try and identify the use cases or business scenarios which could be used to serve as a pilot for the bank. A pilot is important because not only it proves that the idea works, but it helps in bringing the nay-sayers onto your side. This also helps in breaking down resistance because the real value is felt. While the pilot is running it is important to identify those additional areas which would be required as a minimal mass, to put together for putting the first set onto production. Thus once the pilot is a success, it is time to jump with all the use cases to be made ready for production. This is the minimal viable set which should be in production to start bringing in the benefit of the digital strategy to the end customer as well as the bank in terms of selling/ servicing more for less.

Analytics and AI – what is their usage and where are we heading

In every article related to digital banking transformation we hear about how analytics, machine learning and artificial intelligence would change the digital game in banks. But where we are today on this subject, and how this script would play out for the future is the topic of my blog today.
Yes, analytics, machine learning and artificial intelligence are game changers to any industry today. There is no denying this fact. But it is also true that this is a journey. The journey starts in having/ collecting data, then curing and taking out the right data, employing statistical models in understanding the data patterns, and then trying to make sense whether there are patterns involved within the various aspects of the data, and whether we can understand the patterns to make sense of what the pattern suggests as specific insights that can be actionized. Let’s take a simple everyday case of shopping on Amazon. When you go in for the first time they have no data about you. They look at what you are buying and provide recommendations for similar offers based on other people’s buying behavior. Let’s assume you buy product A. The next time you login, based on your browsing and buying history, it would suggest the other products you may be interested in, looking at the pattern of other people who had browsed around or bought product A. As they collect more and more data, they get better at trying to guess what products you may be interested in and try to suggest the same. This is nothing but the next best offer model used across industries in trying to cross sell better and contextually to the end customer. But before I move out from Amazon, I would ask you a question – how many times have you bought into something which has being suggested by Amazon? I have bought only 2 times out of the 100 or more times I have shopped in amazon in the last 6-7 years. I am not saying that their offers are not intelligent or out of context, but most of the time, I have logged into Amazon I knew what I was looking for, and then tried to match my buying parameters around that item, rather than looking at Amazon is offering. The take away for me is that most of the time, when we are in a buying mode, human beings would know what they are looking for, and possibly would go by their intelligence rather than what the artificial intelligence bot is suggesting. The reason for this is that artificial intelligence is yet to evolve to that level where it can be anything close to the intelligence level of human beings. If average human intelligence is 100, most of the AI bots would be less than 5.
So how does AI work for banking. For AI to work there must be data in banks. Yes, banks have tons of data, but mostly this is transactional data. It gives data on his withdrawals and deposits and most of the transactions do not say ‘why’ the transaction was done. So pattern analysis can yield expected balances, and based on that can be used for predicting whether the customer would be short of funds gauging his/ her transactional behavior. What banks are now trying to do is that try to get to the ‘why’ of the transactions, so as to better understand where money comes from and where money goes for a customer, so that they can provide proactive advisory to the customer of how to save better, how to invest better and get a better bang for the buck. This is indeed a journey, because banks needs to clean up their systems, so that they automatically provide the ‘why’ part of their transactions. This means cleaning all their channels (which include ATM, POS, Payment systems: which may not be in their control) solutions to give them the right details of the transactions so that they can make sense of the ‘why’ part of the transaction. With this knowledge, the bank would be in a better position to use machine learning to get insights in the customer’s spending behavior and advise him/her better. Better transactional data also helps the bank understand fraud patterns better. Once we know through AI the typical transactional pattern of the customer, any pattern which does not match that pattern can be a possible fraud pattern. This helps in protecting the customer from all sorts of fraud including cyber fraud. In a modern world we can use the devices of the customer to identify the particular device the customer uses, the location which is used by the customer to do transactions, the typical types of transaction and spending patterns etc. Anything not matching can be caught by AI to indicate that something is not so right. Thus fraud engines are very fast moving to AI from their earlier rule based avatars. The more AI learns on transactional patterns and fraud patterns the better it would be in recommending and advising on action.
Automation is another area where AI has a big role to play in improving operational efficiencies and thus reducing the cost to income ratio. But today bank processes are far away from automation even where rules can be used to achieve it, keeping aside AI for automation. Most of the decision making, including credit decisions, audit decisions, charge waiver decisions etc. all are manual because it is expected that a human being is supposed to decide. But what is the basis for the decision? He/she decides based on data, and in complex cases the decision is taken based on data from multiple sources along with complex rules. But in the world of AI which works on data, and also on capability to learn on rules and patterns – do we need human beings to take routine decisions? Rather human beings should be used for very complex decision making, where a normal AI engine fails. Definitely automation use cases would be quite popular in the AI world, as they would be easier to execute and get results on, which brings in tangible benefits to the banks.
Cross sell is an area where AI can have a significant play. But in today’s bank’s the reality is that existing customers with 2 credit card from the bank keeps on getting offers on taking the same or lower category of credit cards from the same bank. This happens because the data available in the banks is not clean. Same customer has multiple identities across different systems which the bank owns. Campaigns are run on data which is not clean, and thus different campaigns yield same results which irritate the customer instead of wowing the customer. But banks are working hard to become the Amazons of the world. Their data journey has started but it would take some time till they get all the data cleaned up, organized and then get the models right to get proper insight to thus send them the right actionable. This is a journey and it would take the biggest of the banks some time to get the pieces of the puzzle right end to the end.
The digital bank of tomorrow is being looked at as being a distributor where they would be distributing both banking and non-banking products and services which will include life style needs of end consumers. In such a scenario the data sources would multiply, and thus banks would indeed need to apply ML technologies to make sense of these varied and vast data sources, and come up with the right gleaned insight which would lead to a contextual actionable for the end customer. Thus a bank of the future would mandatorily require AI/ ML systems so as to engage the customer meaningfully.
To summarize, even the most advanced banks are talking data rather than being able to use data effectively to deliver better value to business. This is a journey, and banks have embarked onto it with varying degree of seriousness. The ones who are serious, and are investing significantly in these technology and domain would have much to gain in the future because these would be creating the differentiators and would be winning customers from the other banks with the right tools of engagement which they develop.

Corporate Banking: Stressed Corporate assets management in India

In an ideal world, a bank’s profitability would be a function of its net interest margin, fee income, and operational efficiency. But a well-known fact in the banking industry is that, a bank’s bottom line depends to a larger extent on the amount of non-performing assets (NPA) that are recognized and provisioned for, in the Profit and Loss account. It is therefore a common practice in banking to “extend and pretend”, i.e. provide borrowers more time beyond the contractual date, to repay their obligations thereby avoiding the NPA tag for the asset. “The Asset Quality review that was commissioned by the erstwhile Governor of the Reserve Bank of India (reference: )”, unearthed many hidden skeletons from the aftermath of the lending boom in the previous decade to companies in the steel, power and infrastructure sectors.
The stressed assets of Indian banks at 12.2 % as of September 2017, can neither be resolved overnight nor be provided for in the books of the affected banks at one shot. The former lacked a robust legal framework and the latter would result in wiping off capital for many large banks.
There is also an aspect of government’s prerogatives to support and encourage certain industries based on the budgetary and NITI aayog kind of bodies recommendations which most of the public sector banks are bound to support. With change of guard at central government level every 4 to 5 years, banks need to align themselves in terms of lending to such sectors.
The Insolvency and Bankruptcy Code was enacted into law in 2016 with the objective of consolidating and amending the laws relating to reorganisation and insolvency resolution in a time bound manner for maximisation of value of assets. The code stipulates that the evaluation and viability determination must be completed within 180 days, extendable up to 270 days. If not resolved in this time frame, liquidation proceedings will be initiated. Here are the other highlights of the code:

  • Any financial/operational creditor can apply to the National Company Law Tribunal(NCLT) for insolvency on default by a borrower

  • On acceptance, a Resolution Professional (RP) is appointed

  • The RP will take over the running of the Company.

  • A moratorium period will be declared during which no action can be taken against the company or the assets of the company.

  • A resolution plan would have to be prepared and approved by the Committee of creditors. If the resolution plan is rejected by NCLT or no plan is worked out within the moratorium period, liquidation will be initiated.

RBI had a large number of schemes for resolution of stressed assets including Corporate Debt Restructuring Scheme (CDR), Flexible Structuring of Existing Long Term Project Loans, Strategic Debt Restructuring Scheme (SDR). To align with the Insolvency and the Bankruptcy Code, all the resolution schemes have now been withdrawn through a notification in February 2018 (reference: ). A revised framework for Resolution of Stressed Assets has now been introduced.
“As soon as there is a default in the borrower entity’s account, lenders need to initiate steps to cure the default (reference:“).The resolution plan may involve reorganization including regularisation of the account by payment of all over dues by the borrower entity, sale of the exposure to other investors, change in ownership. Minimum credit rating by rating agencies has been stipulated for the residual debt after implementation of the plan. As per the new RBI norms, if a Resolution Plan in respect of large accounts is not implemented within 180 days of default, lenders have to file an insolvency application under the Insolvency and Bankruptcy Code.
The new framework can have a significant impact on a bank’s bottom line. “In case of restructuring, the accounts classified as ‘standard’ shall be immediately downgraded as non-performing assets (NPAs) (reference: “) . Borrowers who have committed frauds or are a wilful defaulter will not be eligible for restructuring.
There is also provision to impound passport of defaulters and even guarantors if banks move swiftly and avoid getting into the usual trap of unable to extradite these defaulters once they move out of the country.
Technological advancements – Blockchain, certainly will have major role to play in cases of syndication of loans. Though it may further increase the complexity of operation, but I believe it will definitely reduce the stress in the system and reduce NPA ratio. It will definitely act as multi factor safety net for banks and helps in building a national repository of black list of such corporates, which as of now is not very structured.
To conclude: My belief is that the framework is in line with the Insolvency and Bankruptcy Code and would strengthen the industry in the long term by providing a sound legal, technological and regulatory framework.

De-frictioning Customer Experience with Digital Tech

As buzzwords go, this one is top of charts currently – A ‘frictionless’ customer experience encompassing the erstwhile used terms such as seamless, digital, effective customer experience.

What is frictionless customer experience? As a concept, it is simple – it refers to removing anything in the customer journey, it could be about products or services irrespective of the industry, or anything that creates friction and grit which settles in the journey of the customer leading to dissatisfaction and eventually churn. In today’s hyper-connected world, any small delay in the customer journey, is enough to make the customer look away at other options. And there is always another option.

In 2017, Mckinsey mentions “70% of buying experiences are based on how the customer feels they are being treated.”, Forrester states that 72% of businesses claim improving customer experience to be their top priority and Bain and Co seals the discussion by saying that companies that excel at customer experience grow revenues at 4-8% above the market.”

The struggle however is to peg the exact moment a single customer’s experience is expected to start and end, as the customer has myriad channels to engage with, and several ways to complete the purchase or avail a service. Complexity therefore arises as every aspect of this journey has to be closely monitored and maintained, keeping the number of handovers to a minimum and making the entire experience smooth.

As a customer, how many of us have faced the scenario of calling a call centre, holding on for fifteen minutes, then having all the details of our problem explained to us and after what seems like eternity, we are passed on to yet another customer service agent where we have to repeat the entire story all over again with extra details, simply because the service provider hasn’t invested in a technology that can make handovers easy and reduce angst?

Handshake between various parts of the channels providing customer experience is just one aspect of the frictionless customer journey. While researching for an upcoming trip to Orlando, I read about the MagicBand provided by Disney in its theme parks, a wrist band which doubles up as the room key, the ticket to the parks, fastpass access and even can be used to receive the souvenir photos on the mobile app. No more having to queue all wet and flapping at the end of a water ride and waiting for your pictures to show up on a screen! Excellent use of IoT to make everything available to the customer with a single interface.

A friend of mine visited the UK from India and one of the tasks on his list was to close an old bank account in Barclays. He had put aside two days for this job. He went in to a small branch of Barclays and was out within twenty minutes, in disbelief. His account had been closed and the remaining money transferred to another account. He was surprised by the lack of paperwork, lack of questions, as the same process in India would have been much longer and a very different experience with several security hurdles.

Which is all very well, but that does bring up the question about security. We can make things easy for the customer with features such as one touch clicks on amazon, contactless transactions, etc. but what about security? If we are making it easy for the customer, then isn’t it becoming easy for a fraudster too? If we remove the checks and other security hoops which are mandatory, who is watching and doing the policing?

The answer lies in imbibing the right technology to offer a better and safer customer experience.

Automation of back end processes can reduce errors and also enable a quicker time to complete
tasks, leaving the service provider able to concentrate on the softer aspects of customer experience which are often forgotten. Being able to handle customer information residing in disparate applications and providing the relevant information quickly to a customer is also critical whilst trying to engage with the customer, and not losing them to another channel or another provider altogether. Automation whether attended, or unattended, is abundantly available and it is becoming increasingly easy to adopt technologies. Once on the journey of automation, cognitive and AI based chatbots and other interactions can be leveraged so that frictionless customer experience gets closer to reality.