May 2018

‘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.

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!

Solutions For Intelligent Automation Continuum

ARTIFICIAL INTELLIGENCE: THE NEXT DIGITAL FRONTIER

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.

THE AUTOMATION AND AI JOURNEY – THREE KEY STAGES AND THE EDGEVERVE SOLUTION

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, XtractEdge™.

HOW EDGEVERVE SOLUTIONS WILL HELP ENTERPRISES IN THIS JOURNEY

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 XtractEdge™, 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 XtractEdge, they utilise the existing enterprise knowledge and address specific business concerns.

For example, the data-driven, intelligent XtractEdge 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!

Source:

http://assets.teradata.com/resourceCenter/downloads/AnalystReports/Teradata_Report_AI.pdf

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 –

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 –

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 –

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 XtractEdge, 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?

REFERENCES

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:

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 –

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: https://www.rbi.org.in/Scripts/NotificationUser.aspx?Id=11218 )”, 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:

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: https://timesofindia.indiatimes.com/business/india-business/rbis-new-norms-on-bad-loans-wake-up-call-for-defaulters-government/articleshow/62902916.cms ). 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: http://indianexpress.com/article/business/banking-and-finance/rbis-new-norms-to-speed-up-resolution-of-stressed-assets-5062383/“).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: https://www.rbi.org.in/Scripts/NotificationUser.aspx?Id=11218 “) . 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.

Regulations for Non-Performing Assets – How effective is the implementation?

Lending for capital investment, infrastructure projects, and heavy industries contributes to the development of the economy and also provides long-term assured income to banks. Such corporate loans occupy a significant asset portfolio in most large banks. Project feasibility, credit risk, cash flow, balance sheet analysis, industry analysis, promoter interest and stake, creditworthiness etc. are meticulously scrutinized and reported during loan assessment. Depending on the banks’ capital and limits assigned for lending based on different industry norms, approval from the Board is obtained before clearing the loans. The bifurcation of the limit structure could be a combination of fund based (working capital and term loan) and non-fund based documentary credit/ Bank Guarantee as per the applicant’s request or credit assessment. In case the borrower is involved in exports and imports, things such as a foreign currency loan limit and pre-shipment/post shipment credit come into play. Before disbursing the loan, several officers inspect the terms and conditions and record their observations in the credit note sections.

The performance of these loans is closely monitored and in many countries, their Central Banks have formulated norms for classifying non-performing loans (where there is a default in repayment). Sometimes, there may be non-performance contrary to expected norms which could indicate that the borrower is diverting funds to associate businesses; close monitoring is required to prevent such instances. Across the globe, Basel II, Basel III and Basel IV norms are available for measuring credit risk and capital adequacy ratio based on risk weighed assets. Market risks arising in the future – exchange rate risks, industry failure, export/import issues etc. – can also lead to non-performance or repayment default by the corporate borrower.

Most banks use pre-processing or origination software for credit application analysis, credit scoring and credit decisioning in the case of large corporate borrowers; a few still use manual credit monitoring assessment tools. Post approval, limit booking and account opening are done by the lending servicing software. When the software is available at the branches but can be accessed by the bank’s Central Credit Cell, all transactions, be they disbursements or repayments, are viewed and monitored. There are software provisions available for checking non-financial conditions like non-submission of stock statements or non-submission of IT returns periodically during the lending life cycle to assess the health of the account.

Checks and computing software notwithstanding, there is both human intervention and subjectivity in NPA assessment. Banks are reluctant to book a loan as non-performing considering the huge interest amount, which is booked periodically and the associated provisioning which impacts the P&L. There is also a huge reputational impact to the borrower and the company’s business when a loan is termed an NPA. A few of the data provisions in the software need to be modified and some exceptions in the servicing or NPA software overridden manually to classify the loan as performing. To be precise, banks adopt a conservative approach in classifying corporate loans as NPA when compared to small loans. Reducing manual intervention to the extent possible and automating activities through systems and software is definitely required.

Central Banks such as the Reserve Bank of India have requested banks to periodically submit returns for all large borrowers including different parameters like amount sanctioned, amount availed, industry classifications, repayments made etc., to monitor the performance of these accounts remotely. It is possible that some of these data elements are neither captured nor reported accurately resulting in fallacious information being provided to the Reserve Bank. The Reserve Bank on its own conducts periodic inspections and reports the irregularities to banks’ top management. Any delay in conducting the inspection after the account has gone bad, preparing the report after the inspection, review by authorities and final communication for action reaching the top management may cause the account to deteriorate further or require the promoter to make some adjustments in the interim.

Though the NPA guidelines/regulations/policies framed by the Reserve Bank are largely in line with global standards, the measures taken by banks are only partially successful in controlling the mounting NPAs of public sector banks in India. Some banks and borrowers have relationships outside of accepted banking norms and principles, which could prevent NPAs being given the proper treatment. Usually companies’ top managements know key bank officials and government authorities at the highest level, which could work to their advantage. Banks are even able to circumvent the software to dilute the conditions for borrowing.

To conclude, the measures or principles for classifying a loan as a bad asset are not followed as per regulations; human intervention prevents the software from automatically classifying the account. There are also numerous provisions for NPAs which are added or modified every year by the Central Bank of each country and bank officials use this to their advantage, pretending to be unaware of the latest guidelines, deliberately misinterpreting clauses, not responding to communication issued by the Head Office or Central Bank, or not explicitly mentioning the clauses about provisions applicable to different industries, and so on. Though software vendors want to update their NPA software with the new additions, different interpretations of the same guideline by different banks make it difficult to adopt a common approach for building the software. An effective early NPA warning and implementation measures by banks are seldom useful in curbing NPAs

The Central Bank of each country needs to examine the practicality of building software for only NPA management, which can be changed easily and made to work on top of any core banking or lending software for better control and monitoring. Banks can share the data periodically or as desired by their Central Bank online and for the Central Bank to run the data through this NPA software. The Central Bank (and only the Central Bank) can thus approve or reject any exceptions / deviations. It does not augur well to have post facto rules or modifications to existing norms after an incident has occurred. There could be policies framed for centrally controlling the large corporate accounts through a separate institution with an external rolling committee of analysts and consultants (who can be appointed by the RBI involving non-RBI and non-bank officials) who will periodically scrutinize such accounts and provide their recommendations. They can independently monitor and review these accounts whereas accounting, loan booking, interest applications, and loan transactions can continue to remain in the original bank. The names of corporate entities having exposure above a certain threshold amount can be made public (in the banks’ balance sheets or on publicly accessible portals or through reputed credit agencies) making them accountable for repaying their loans. Irrespective of the future changes envisaged by the regulator, there should be stricter policies to control the mounting NPAs in public sector banks in India which could potentially erode a large part of their capital and derail the economy. The Reserve Bank of India cannot silently watch this deterioration and evade responsibility. It should proactively frame guidelines and policies by involving bank leaders, government officials, industry experts etc. and ensure they are implemented.

Evolution of an “Assistant” at a workplace

Personal assistant

– a secretary or administrative assistant working exclusively for one particular person.

Personal digital assistant

– a handheld device that combines computing, telephone/fax, Internet and networking features

Virtual assistant

– a software agent that can perform tasks or services for an individual.

Dictionaries define the above terms in the way they do. What is noteworthy is how humans have evolved in developing advanced technologies that can take on tasks in our daily lives, and with such ease.

From a human – exclusively working for a particular person, and available during particular working hours of the day; to a device – owned by an individual and available at all times; and now to software – available to everyone across the organization at all times. We wake up to a confluence of technologies every day.

Software used by business users to perform specific business functions such as payroll, procurement, inventory management, etc. have been heavily invested in by software providers over the years. The paradigm shifts in the way business users expect these business applications to work, rather support them, has created the need to innovate.

Business applications have been taken to the next level of delivering a B2C experience in every sense of the word. Adding artificial intelligence, natural language processing and automation to the existing applications enhances the user experience and reduces the turn-around-time of an activity manifold.

The ease of interaction with a software just as one would interact with a human, has become the key to success – in most ways, the defining factor that drives users to adopt a business application. That then makes us delve into what organizations are doing to “catch up” with the fast changing needs of users at the workplace.

Rather than setting off on a massive digital transformation journey, most leading organizations are opting to use simpler and smaller technology solutions that can be deployed in a very short span of time, and would provide the required impetus.

Increasing need for such solutions, has led to large software providers developing solutions on AI based platforms, suitable to support horizontal functions such as application maintenance, IT ticket management, etc. across business functions.

Taking it a notch higher, and providing solutions addressing the needs of specific business functions like procurement is a reality now.

A technology that is gaining popularity is conversational software. Procurement Assistant is one such solution developed on an AI based platform using Chatbot as an interface. Interaction using text and speech makes it easy for users, since it is their natural way to communicate. This Chatbot is integrated with the organization’s existing procurement applications to help business users’ by guiding them to place an order, provide information about suppliers and products, or simply by completing tasks on their behalf – an assistant in every possible way, as it is pre-designed with procurement intelligence, and also actively learns on a daily basis. The procurement assistant is made available through multiple channels such as, mobile, web and messaging platforms.

Such a solution makes the business function more efficient, compliant and agile.

The pace of evolution of such assistants is pretty fast – most of them do take voice commands and respond with expected outcome. However, there is still some way to go before a user can give a voice command to invoke a series of processes within a business application.

Not a far-fetched reality though!