Artificial Intelligence is the core of Fintech

As an expert in Fintech and big data analytics, I had to write this blog - which is essentially a transcript of my talk in Hewlett Packard to group of very talented & experience folks.

Big data is THE enabler of artificial intelligence. Financial industry generates a whole lot of data and this data forms the basis for powerful analytical tools that use Artificial Intelligence technologies which automates a whole lot of decision making.

What is AI?

At its highest level, Artificial Intelligence is an intelligent technology that leverages historical data and applies what is learned to current contexts to make predictions. AI combines various related terms: machine learning, natural language processing, deep learning, predictive analytics, etc.

Fintech & AI

In the current era of digitization and customer empowerment with mobile Internet, Banks and financial services companies are coming under intense pressure to compete with new age Fintech companies.

Fortunately, big banks and financial firms have huge amounts of customer data this can be leveraged along with newer tools to create & deliver exceptional and memorable experiences using technology.

After decades of research in AI technologies, AI is ready for prime time. According to a report by AI solutions market is estimated to reach $153 billion by 2020.

Today, AI is poised to become a key enabler of modern CRM solutions to Banks. Today many banks & firms use automated communication tools such as ChatBots to reach out to customers.

80% of executives believe artificial intelligence improves worker performance. 

Lets now take a look at these new tools and technologies.

AI:  One of the key technology trends upending the financial industry

Today's data-saturated world offers immense opportunities for financial institutions that know how to put this data to use by structuring it in the right way. As a result, an increasing number of financial institutions are adopting Artificial Intelligence (AI) to better serve their customers and increase their business growth. The explosive growth of structured and unstructured data, availability of new technologies such as cloud computing and machine learning algorithms, rising pressures brought by new competition, increased regulation and heightened consumer expectations have created a 'perfect storm' for the expanded use of artificial intelligence in financial services.

Artificial Intelligence with its advances in computing power, the ability to store and process Big Data, and instant access to advanced algorithms offers many opportunities for the financial sector. Harnessing Artificial Intelligence enables financial institutions to spot nonstandard behavior patterns when auditing financial transactions or to assess and analyze thousands of pages of tax changes. AI is destined to be the perfect tool to empower financial institutions' service efforts with genuinely intelligent tools to cut the time spent on handling lending requests, providing financial consultancies or opening bank accounts. Utilizing intelligent tools enable financial organizations to provide excellent customer experience across different channels. To remain relevant in a technology-driven world, financial pros will have to learn to combine their efforts with these intelligent tools.

Use cases of Artificial Intelligence in financial institutions

The financial sector is embracing Artificial Intelligence and machine learning to stay afloat and win over the digitally native customers. Harnessing the disruptive technology offers plenty of opportunities for financial institutions including: Customer support, transactions and helpdesk, data analysis and advanced analytics, underwriting loans and insurance, repetitive tasks and  performance, automated virtual assistants and Chatbots. Intelligent tools augment the capabilities of financial pros enabling them to easily identify customers' preferences and react with insight and emotional intelligence, which is essential for the development of meaningful customer relationships.

By leveraging intelligent tools, financial institutions and banks become technologically sophisticated and capable of meeting the financial needs of digitally savvy customers. Utilizing AI allows financial pros to analyze customers' buying patterns and red flag any irregularities and take preventive measures. Aienabled tools allow for making better risk decisions and conducting more accurate risk credit assessments.

Predictive scoring

Employing predictive scoring allows financial professionals to predict credit-related behavior, defaulting on loan payments, occurring an accident, client churn or attrition. Scoring backed by intelligence empower financial institutions to recognize creditors who will pay back a loan from those who will not pay based on the credit application's data. Financial pros can effectively apply scoring in forecasting of credit risk before granting a loan or when the loan is already granted.

Banks apply predictive scoring when forecasting the risk for a granted loan or when selecting optimal debt collection activities by assessing the credit related behavior.

Also See: 

1. Decision support with use of predictive models comparing to application of common sense rules or rules prepared by expert gives profit higher by 10-30%. 
2. 35% of executives say their decision relies mostly on internal data and analytics. 
3. By adopting an intelligent-computing program, some banks have experienced a 10% increase in sales of new products, a 20% savings in capital expenditures, a 20% increase in cash collections, and a 20% decline in churn. 

Computer intelligence with human touch

With the current virtual assistants (VC) and Chatbots revolution, along with the tremendous growth of messaging and social media apps, there is a great opportunity for organizations to optimize processes and deliver better customer experiences.

Chatbots are personal assistants that leverage messaging apps or outbound messaging and can run continual analysis of the information that is needed by the customer to ensure they get the relevant information through their preferred channel. Today's intelligent Chatbots and Virtual Customer Assistants can even take into consideration the context of the discussion and provide answers to several questions while analyzing the whole communication thread.

Natural language processing (NLP) is another intelligent tool for uncovering and analyzing the "messages" of unstructured text by using machine learning and Artificial Intelligence. It allows for providing context to language, just as human brains do. As a result, financial pros can gain a deeper understanding of customers' perception around their products, services and brand. NPL can be employed by customer service agents to more quickly route customers to the information they need.

Use Case: Nina, a customer service web-assistant developed by Swedbank, processes around 30,000 conversations focusing on 350 different queries each month. Nina had a first-contact resolution rate of 78% in the first three months of its operation.

Also See

1. The use of virtual customer assistants (VCAs) will jump by 1,000% by 2020. ()
2. The virtual digital assistant (VDA) market is estimated to reach $15.8 billion worldwide by 2021 with unique active consumer VDA users growing to 1.8 billion, and enterprise VDA users rising to 843 million.
3. Research firm MarketsandMarkets predicts the NLP market will reach $13.4 billion by 2020, a compound annual growth rate of 18.4 percent. 

Next best action for financial pros

Another benefit that AI offers for financial institutions is prescriptive analytics integrated with the next best action approach. Next best action is a customer-centric paradigm that considers various actions that can be taken for a specific contact and decides on the 'best' one. The next best action is determined by the customer's needs and interests on the one hand, and the business objectives and policies on the other.

Leveraging innovative technology can provide financial pros with recommendations about what steps they should take next to achieve a specified goal, such as the highest possible revenue or the highest level of engagement.

Utilizing the most innovative predictive decision-making technology can significantly improve the accuracy and effectiveness of financial activities. Through applying analytics, financial pros are able to better understand customer needs and drive higher customer value. Tech savvy financial pros are armed with the right tools to choose the most relevant and efficient process flow and ensure that an offered product or service meets the customer's needs.

Embracing intelligent tools allows financial institutions to focus on predicting customer behavior in order to drive value, managing multichannel interactions, and operating as an insight-driven business.

Utilizing predictive analytics to better understand your customers

Artificial Intelligence provides financial pros with the intelligent tool of predictive analytics that augments their capabilities to create exceptional and memorable customer experiences. Analyzing all internal and external customer data and converting it into actionable insights allows for not only providing real-time advice and solutions, but also anticipating future financial needs on a customer level. Banking analytics, combined with cognitive computing, provides financial pros with the ability to know each customer, provide customers with personalized offers, making one-to-one relationships a possibility. It accelerates financial institutions' ability to create individualized experiences for customers and realize tangible business benefits.

Applying predictive analytics enables financial institutions to:

• Deliver meaningful and emotionally satisfying digital experiences.
• Optimize product portfolio and enable numerous cross-sell and upsell opportunities to highly refined audiences.
• Convey personalized and relevant marketing messages to a specific audience at a specific stage of life.
• Create a complete customer view (360-degree) to personalize all customer touch points, while capturing digital data on a customer's preferences.

Also See:

1. 47% of organizations across industry now use predictive analytics to support business insight for risk purposes.
2. Artificial Intelligence Will Drive The Insights Revolution - Forrester
3. How is big data analytics transforming corporate decision-making?  

Closing Thoughts  

In the age of the customer and intelligent technology, financial institutions and banks are under increased pressure to pay attention to technological developments such as AI, and are quickly adapting to these changes. Intelligent tools along with Big Data offer financial institutions a huge opportunity to deliver exceptional and memorable experiences. Furthermore, Customer Relationship Management (CRM) solution backed with these sophisticated tools provide financial pros with the right blend of technology and human touch. An intelligent CRM solution ensures the complete view of your customers, keeping all their preferences and interests in the centralized repository accessible for financial pros, and enabling them to provide exceptional customer experience. With the AI-powered tools financial pros can exponentially enhance the customer journeys with more personalized approach. Embracing Artificial Intelligence is at the forefront of propelling the financial institutions through the digital age of the customer.

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Key Learnings:

1. Artificial Intelligence can become a success enabler of modern financial institutions

2. Computer intelligence use cases financial institutions can employ to become more
Customer centric

3. AI powered tools such as predictive scoring, advanced analytics, virtual assistants and Chatbots can help financial institutions and banks win over digitally native customers

Corda - A Next Generation Blockchain Technology

Corda is a distributed ledger platform designed to record, manage and automate legal agreements between business partners. Designed by (and for) the world's largest financial institutions, it offers a unique response to the privacy and scalability challenges facing decentralised applications

Corda's development is led by R3, a Fintech company that heads a consortium of over 70 of the world's largest financial institutions in the establishment of an open, enterprise-grade, shared platform to record financial events and execute smart contract logic.

Corda is now supported by a growing open-source community of professional developers, architects and hobbyists.

What makes Corda different?

1. Engineered for business
Corda is the only distributed ledger platform designed by the world's largest financial institutions to manage legal agreements on an automatable and enforceable basis

2. Restricted data sharing
Corda only shares data with those with a need to view or validate it; there is no global broadcasting of data across the network

3. Easy integration
Corda is designed to make integration and interoperability easy: query the ledger with SQL, join to external databases, perform bulk imports, and code contracts in a range of modern, standard languages

4. Pluggable consensus
Corda is the only distributed ledger platform to support multiple consensus providers employing different algorithms on the same network, enabling compliance with local regulations

Closing Thoughts 

Corda provides the opportunity to transform the economics of financial firms by implementing a new shared platform for the recording of financial events and processing of business logic: one where a single global logical ledger is authoritative for all agreements between firms recorded on it. This architecture will define a new shared platform for the industry, upon which incumbents, new entrants and third parties can compete to deliver innovative new products and services. 

Getting your Big Data Strategy right

An expert advice on what you need from big data analytics, and how to get there.

Business and technology leaders in most organizations understand the power of big data analytics—but few are able to harness that power in the way they want. The challenges are complex, and so are the technologies. Identifying and investing in key principles will help you navigate that complexity to find the right way to tap the growing pools of information available to your organization.

There are six main factors required to get a big data analytics platform right. Lets take a look at each one of them and explain how companies can get their big data right.

1. Blazing speed

Expectations around data are higher than ever. Business users and customers demand results almost instantly, but meeting those expectations can be challenging, especially with legacy systems. Speed is not the only factor in implementing a big data analytics strategy, but it's top of the list. Customers typically need to run queries on data sets that are 10 terabyte or larger and want results in few minutes.

Typical Business warehouse solutions would take 48 hours or more, In today's high speed business world, results after 48 hours is almost useless.

Time to insight is a top priority for any new analytics platform. Companies need to invest in High Performance Computing (HPC) to get the results in few minutes. With newer in-memory analytics systems  - such as SPARK or SAP HANA, the wait times can shrink to less than a second!

New solutions are fully optimized, enabling it to provide insights on time to fuel bottom-line results.

2. Scalable capacity

Today, It's a given that any big data analytics solution must accommodate huge quantities of data, but it also needs to grow organically with data volumes. Analytics solution must be able to grow in scale as the data size increases. Today, customers can't afford a "rip and replace" options when the database sizes grow.

Business needs a system that can handle all the data growth in a  way that is transparent to the data consumer or analyst - with very little downtime, if any at all. Capacity and computer expansion must all happen in the background.

3. Intelligent integration of legacy tools

Big Data Analytics must work with legacy tools so that business management can have seemless continuity. But it is also important to know which tools must be replaced and when.

Businesses have made investments in these older tools - such as Business Warehouse, Databases, ETL tools etc. Top management is comfortable with these legacy tools. But as data size grows newer data analysis tools will be needed and these new tools will have to work along with legacy tools.

4. Must play well with Hadoop

Big data and Hadoop has almost become synonymous with big data analytics. But Hadoop alone is not enough.While Hadoop is well known, it is built on generic low cost servers, it is also slow.

Hadoop, an open source big data framework, is a batch processing system, meaning that when a job is launched to analyze data, it goes into a queue, and it finishes when it finishes - i.e., users have to wait for results.

Today, Big data analysis needs to be fast - we are talking about high-concurrency in-memory analytics. Companies will still use Hadoop - but find newer ways to run Hadoop without incurring the performance penalties. Newer implementations of Hadoop (ver 2.7.x) and Spark will allow both systems to run in parallel.

5. Invest in data scientists

Organizations must build teams of data analytics experts. Not just hire data scientists, but also invest in tools that allow them to conduct more robust analyses on larger sets of data.

The key to move forward with best possible data analysis solution is to enable data scientists work with actual data sets and not a sample subset. The data analytics development environment must have the scale and size needed to work on actual data sizes, else the answers can go wrong and also leads to longer and more iterative development process.

6. Advanced analytics capabilities

Data Analytics tools and capabilities are rapidly evolving. Newer analytical tools use Artificial Intelligence (AI) tools as businesses move toward predictive analytics.

Big data has moved beyond reporting. Big data analytics is being used to answer very complex questions based on the data in your database. Analytics are now being more predictive, geospatial, and sentiment focused.

The shift toward predictive analysis and other advanced analysis has started. Organizations now—with the way data science has become more and more a corporate asset—there's definitely greater interest in becoming more predictive and more data-science savvy in nature.

Closing Thoughts  

Globally, data is growing at a very rapid rate: 40-50 percent per year. In this environment, every business is going to struggle against an overwhelming volume of data. New technologies are there that can help manage data at that speed and scale.

But having a right big data strategy is vital for success. As new tools, technologies emerge, it becomes critical to have the right strategy to incorporate them into the existing eco-system in a seemless non-disruptive way.

In this blog, I have highlighted 6 main aspects of a big data strategy that helps organizations to get its big data strategy right. 

Developing an Innovation Zone

Every business today wants to be innovative. Yet most companies fail to innovate. Having worked as R&D engineer in Silicon Valley - where I started my career and filed for my first patent, and a innovation coach, here is one the key point for building an innovative teams.

Innovative teams need market knowledge to be successful. Having just technical skills is not enough. Here are four key inputs innovative teams need.













1. Customer Insight 

Great innovative companies such as Intel, Hewlett Packard, EMC etc have developed multiple channels to get information about and from customers, and have built extensive knowledge sharing systems to distribute this information widely within the company. This enables teams to understand customer needs and that helps develop new products that delights customers.

Having deeper customer interactions also provides opportunities for customers to participate directly in the innovation process.

2. Global Network

Companies that leverage information from the large business ecosystem are always at an advantage. Organizations that build a global network have greater advantage. When we look at top 10 innovative companies - they have a global foot print with teams operating in all continents and many countries, they integrate these global sites with information sharing channels helping teams all over to innovate.

The global network extends to global partners in the business ecosystem. For example Intel works with Hewlett Packard, Dell, IBM, Lenevo, Microsoft and several hundreds of partners to collect information & knowledge from the ecosystem.

3. Future Foresight

Innovation does not happen without a need from the market. Understanding what customers will need in future. But getting to know what customers will need in future is not easy. Developing a future insight in extremely difficult. This is where global companies have an advantage.  Identifying tomorrow's market needs, opportunities and risks requires working with senior management to assess the strategic and tactical implications of trends, and sharing information throughout the organization. Company leadership then shares this vision of the future with the entire organization. Global teams then work on this insights to develop products for the future.

4. Innovation Organization

There is tremendous innovation potential residing in all employees. Companies that go to great lengths to hire intelligent employees and then they have to empower innovators. Innovative companies provide dedicated R&D budgets, invest in labs, and focuses on innovation.

Company leadership backs up an idea with money, resources and time to develop innovative products.

Closing thoughts

All successful companies have reached the top because of their innovation. There has been no shortcuts in innovation.

Innovation is essential for success, but not all innovative products will be successful. For example, Nokia invested in two different smart phone technologies - Symbian and MeeGo, and yet failed. While history will say Nokia as a leader who failed, history will always remember Nokia as an innovative company.

Why Fintech should embrace DevOps

Making your IT operation DevOps friendly can improve Bank's ability to respond to rapid changes in FinTech deployments.

For many years now, the biggest challenges banks face have been "soft" and cultural as much as technical. CIOs and IT directors need to align with business values, nurture security awareness, and cultivate a forward-facing workforce with considerably more urgency than they apply when they look at, say, a migration to IPv6, certifying SOC2 compliance, or calculating the ROI of a shift from spinning disks to flash memory.

The latest wrinkle for IT is how to befriend DevOps talent, or more precisely, how to leverage DevOps capabilities and resources. This introduction describes DevOps, distinguishes DevOps and IT cultures, and concludes with a handful of specific ideas IT should consider in response to DevOps fashions.

What is DevOps?

Make no mistake: A significant fraction of DevOps practice is fashion, in the sense that belief in the benefits of those practices is far more widespread than objective documentation of the same benefits. That doesn't mean that all of DevOps is bunk. It just means that DevOps deserves the same critical scrutiny from IT as functions like data management, legal, or marketing. All of them ultimately have a responsibility to communicate how they promote the organization's mission.

The "DevOps" neologism aspires to capture collaboration between software development (Dev) and IT operations (Ops) skills and experiences. One sign of DevOps' mind-share is that it has inspired even more contrived targets, such as DevSecOps and Lean DevOps.

DevOps is often explained as a contrast to outdated silos of responsibility. In the latter model, programmers code up functionality, then toss their source code "over the wall" to a distinct engineering or operations crew responsible for delivering that functionality at a production level and scale. Ignorance of operations realities often results in developers choosing architectures or coding styles that scale poorly and fail clumsily, and are hard to monitor.

Similarly, operations staff who work without deep programming insight have only blunt tools to attack such real-world requirements as a sudden need to multiply capacity by a factor of 10 or harden an application against hostile actors.

DevOps practitioners should have sufficiently broad and deep perspective to step around such pitfalls. The current hot market for DevOps hires reflects the expectation that DevOps practitioners should be solidly grounded in both development and operations.

Another strong theme in DevOps culture is that practitioners automate and generally treat as software as much of operations as possible. A traditional operations staff might install and test operating system patches during downtime hours, while dedicated DevOps workers are more likely to have hosts under automatic control so they can launch new resources and migrate workloads during business hours. Similarly, a new version of an OS should be just another configuration element to update.

When things work well, DevOps takes credit for reliable, highly scalable results delivered to customers, with rapid turnaround of feature requests.

Hybrid vigor

Those certainly sound like desirable qualities. Can traditional IT hire a few DevOps practitioners and acquire the goodness for itself?

Yes and no, as with any important question. Yes, there's plenty DevOps can bring to traditional IT. There are also numerous hurdles to overcome to avoid wasting IT resources and annoying DevOps adepts.

Part of DevOps reality in 2017 is intimacy with cloud service providers, from Amazon Web Services (AWS), Google's G Suite, and so on. Most DevOps professionals take for granted the rich ecosystems of management and orchestration that have grown around these services. A traditional IT on-premises environment—where a new host or storage unit might take weeks to approve and physically requisition rather than seconds to select and deploy—can upset DevOps hires profoundly.

How can central, traditional IT best welcome DevOps talent? These following four ideas—right target, tech clarity, broad perspective, and API opportunity—will take you a long way toward making the right DevOps impression.

Right target

First, keep the goal clearly in mind: While the title of this piece targets "friendly" relations, that's a metaphor. The real goal is to promote the organization's business mission; "friendliness" or "comfort" are means to that end. A traditional IT department probably should never aspire to the "leading edge" technology turnover that some DevOps relish. At the same time, a traditional IT department can speak frankly about the specific help it needs from DevOps and the opportunities to apply DevOps principles outside the usual DevOps domains. The right candidates prize that sort of honesty and challenge.

Technical clarity

Clarity about the organization's infrastructure plans is also important. Is the department adopting private cloud on-premises? DevOps can help define its configuration. Has the company decided on a hybrid cloud architected to allow loads to migrate from on-site to off-site and back? Hybrid remains a specialized, narrowly understood technology likely to excite many DevOps professionals. Is the company committed to legacy architectures in its own data center?

A smart company can remain with legacy at that level and simultaneously work to virtualize and streamline management of those legacy resources. Good DevOps professionals can recognize that, although on-premises legacy architecture won't help them keep up with the latest AWS releases, integration and modernization projects offer abundant opportunity to apply DevOps principles and tools. Part of recruitment should be to sort out the DevOps candidates you encounter: Does a particular individual hunger to be at the bleeding edge of technology? Is the candidate's reward more in using existing tools to fulfill the organization's needs? While both prospects can equally claim "DevOps" status, the latter is likely to be a better fit for integration in centralized IT.

It's more than just a GitHub account

The DevOps focus is on automation and lifecycle management. While this applies immediately to provisioning, that focus can help traditional IT in other areas, including capacity planning, availability, and business continuity. Certainly the past decade has seen a great improvement in tooling around performance, but much of that tooling is still poorly understood by traditional IT. DevOps will assume Logstash, Kibana, Redis, Elasticsearch, Nagios, Puppet and/or Chef, a messaging bus, and other such tools are available. Even the most traditional IT departments probably need to open up to these basic building blocks. IT probably also needs to support Git or a comparably modern source code management system, along with an integrated issue tracker and source review manager. The department doesn't have to hand everything over to GitHub—but it better offer something almost as good or DevOps careerists will think they're just wasting their time.

Embrace APIs

Join the API gold rush. APIs represent another (at least?) double-edged sword, of course. Plenty of past IT attempts to provide programmability have resulted in ill-conceived uses that made more work for IT, however much the intent was to encourage departments to construct their own robust applications. APIs play a different role in 2017, one DevOps will insist be supported well. Cooperate with DevOps and they will show how APIs can be published and consumed more inexpensively than in the past.

Blended strengths = enormous opportunity

A traditional IT department in a non-technology company does no one any favors by pretending it is DevOps paradise. If it's clear about its goals and plans, though, and ready to move on from break-fix and physical craftwork approaches, it can present its IT plans as great opportunities for the automation and management DevOps does best.

There's no need to view cloud providers and convergence technologies as enemies to traditional IT. Rather, they are simply new challenges that deserve thoughtful response in support of traditional IT's longtime strengths in business continuity management, security routines, identity management, cost accounting, and so on. The opportunity to blend the strengths of DevOps and traditional IT is enormous, at least for those IT decision-makers clear about their plans and resources to support them.

Can Apple Revive iPad and make it great again?

I have had an iPad 2 for nearly 5 years now and it still works great. I have not felt a need to upgrade it either. The fact that the old iPad works so well is great, but it also indicates a BIG short coming in product planning by Apple. (Samsung has not done great either with its Samsung Tab)

Eons ago, Apple released a groundbreaking product called iPad which sold hundreds of millions of units and customers loved it. After few years of increasing sales, gravity seems to have taken over iPad sales and now the sales of iPad is dropping every quarter.

The product is so good that it does not breakdown and customers like me are happy using it even after 5 years.

The fact that my iPad has outlasted my laptop made me think over it.

When I bought my first iPad, I thought the next version of iPad will surely replace my laptop. Few months later, I got another one for my daughter and my wife got a Samsung Tab.

Both iPads and Samsung Tab still do a great job & I see no reason to replace it with newer models. I read books, browse web, read gmail etc., But the usage is limited. For other creative work, I still need a laptop. For example, I am typing this article on a laptop!

In my opinion, iPad missed several great opportunities to be a perfect replacement for a laptop and a Television. I think, Apple is struck with an unresolved dilemma. Is iPad a computing device? Or is iPad an entertainment device? Should iPad evolve to replace laptops? Or should iPad evolve to replace Televisions?

To me, this is similar to the dilemma faced by Yahoo!  Is Yahoo a media company or is Yahoo a technology company! Yahoo was unable to resolve this dilemma and died. Hopefully Apple can do better.

Long ago, I saw iPad as a computing device which would replace my laptop as a primary computing device. But my daughter uses it as a game console and entertainment device. In both cases, iPad still has not been able to progress much.

Path Ahead for Apple

Putting my product management hat, the best I can recommend is to split iPad into two distinct product lines.

1. Expand the compute capability & make it a preferred compute platform.

2. Expand screen size and make iPad a perfect personal TV Platform.

iPad as a Compute platform

With ever increasing CPU power and memory sizes, iPad can be expanded with a better keyboard, and mouse to replace the laptop. Increasing CPU power.

Apple always supported Bluetooth keyboards and it works, but the real issue was with the pointing device. The Apple pencil and Styli is awkward and has never taken off as a pointing device, this needs to be fixed in the future release. Maybe a bluetooth mouse perhaps?

In addition, Apple needs to beef up its app offerings to support better content creation tools - either via a VDI platform or native content creation apps. Today, modern ARM based mobile CPUs have enough compute power to support content creation.

These two can be easily addressed by Apple.

The real big problem is the screen size.

Increasing the screen size to a 11" or a 12" will make iPad bigger and cumbersome to handle. iPad has long supported external displays - via AirPlay protocol, but it was always cumbersome and one had to carry a big bag full of accessories for a small iPad. Instead, the solution is to allow iPad to operate multiple screens - like Windows & OS-X.  Ideally having apps adapt to any screen wirelessly without a docking station will make it ideal device as a workplace productivity tool. Users can use both the iPad screen and an external screen to do their job.

iPad as an Entertainment platform

In the entertainment version of iPad, iPad can leverage wireless connectivity to a larger screen and also support multiple screens - like a VR headset - will help iPad become more relevant to the future of entertainment world.

When it comes to gaming, I would like to see iPad support different wireless gaming controllers - via Bluetooth or wireless USB. This would allow users to use a bigger 48"-96" TV screens for gaming via iPad.

Support for multiple screens will also aid in entertainment usage. One person can watch a video being casted on TV while other watches another video on iPad screen.

Closing Thoughts 

Once iPad makes these changes, app developers will enhance their apps to support such flexible options with large-screens, VR headsets etc., and it usher in a new era in home entertainment, while keeping the base iPad's portability and all day battery usage.

iPad is a great device and can reach greater heights. Apple just needs to evolve this into two distinct platforms to make fit better into more people's divergent needs.

HPE Reference Architecture for VMware vRealize Suite on HPE ProLiant DL380 with HPE Service Manager

Executive summary 

To remain competitive, organizations are looking for unprecedented agility and efficiency. Budgets for IT are now funded by line of business, which means IT needs to be agile to provision workloads faster. For some, public cloud is the answer; for others who demand higher levels of security, regulatory compliance, specific SLAs, advanced automation, and more efficient ways to track their IT resource consumptions, private cloud is increasingly the de-facto answer. However, architecting and optimizing a private cloud can be complex and requires cross-domain expertise that might not be easily available. Hewlett Packard Enterprise Reference Architectures can help.

This Reference Architecture document provides a step by step guide to building a private cloud with automation built upon the VMware® vRealize Suite running on HPE ProLiant rack-mount servers using VMware vSAN for cost optimized storage backend. In addition, it includes integration with HPE Service Manager and HPE Universal Configuration Management Database (UCMDB) to speed up incident management of cloud services.

VMware vRealize Suite is a leading cloud management platform for creating and managing hybrid clouds. It consists of a set of products to help speed up deployment of a private cloud, quickly set up the private cloud environment to enable cloud service deployment, facilitate Day 2 operations with the ability to create Anything as a Service (XaaS) services, as well as monitor and automate management of provisioned cloud services.

VMware vSAN is a scalable distributed storage solution that is simple to deploy and manage. Because it is built into vSphere, vSAN can be enabled quickly with a few simple steps and managed using vCenter. Storage capacity can be easily added to existing hosts in the vSAN cluster without disruption to ongoing operations.

The HPE ProLiant DL380 Gen9 server is designed to adapt to the needs of any environment, from large enterprise to remote office/branch office, offering enhanced reliability, serviceability, and continuous availability.

HPE Service Manager and HPE UCMDB enables IT to collaborate and quickly identify and resolve service outages.

The combination of VMware vRealize Suite with HPE ProLiant rack-mount servers, VMware vSAN, HPE Service Manager and HPE UCMDB creates a private cloud solution with flexibility, efficiency and agility that responds to business needs.

Target audience:

This document is intended for IT architects, system integrators, and partners that are planning to deploy an enterprise grade private cloud using VMware vRealize Suite and HPE Service Manager software on HPE infrastructure. Document purpose: The purpose of this document is to demonstrate the value of combining VMware vRealize Suite for private cloud deployment and HPE Service Manager with HPE UCMDB for incident management using Hewlett Packard Enterprise servers and storage to create a highly manageable and highly available solution that meets the needs of the business, IT personnel, and the user community.

This Reference Architecture describes testing performed in January 2017.

See: http://h20195.www2.hpe.com/V2/GetDocument.aspx?docname=a00003395enw 

How Automation will change the face of Indian Banks

Today, I had to visit a SBI branch near my house. I had three banking tasks: Deposit a cheque - which was a payment received from a Postal Savings account to the bank account; Transfer funds from the bank account to Public Provident Fund Account and update the passbook to know the bank balance.

This task in a public sector bank branch took nearly 60 minutes of my time, and I had to interact with 3 clerks and one service manager!

This experience made me think on how the upcoming digital transformation will change the face of Indian Banks. As an example, the same task that took me an hour today could be done in few minutes on a digital platform and without any human intervention!

Indian banks operating in conventional systems use tedious human oriented process. I need to fill out a form - where all information is filled out twice - one copy for the bank, one for me! The form is then verified by a clerk and then re-verified by a service manager, and then it takes 3-5 working days for money to move from one account to another!

With automation, 60% of jobs in bank branches can be eliminated. Traditional jobs like passbook updating, cash deposit, verification of know-your-customer details, salary uploads are also going digital increasing job redundancies. Most of the work done at branches will be done by IT systems, and Indian banks are at the inflection point where technology will rapidly improve efficiency and replace humans from performing mundane clerical jobs.

Banking sector was among the big job creators in recent years, but in next 3 years, Banking sector in India will see a decline in number of jobs. Just like ATMs that eliminated the need for bank tellers, new banking apps will eliminate most of the clerical jobs in any bank.

Not all Branch Jobs will be Lost

While I was at the bank, I two elderly gentlemen who wanted my help in getting a "service token!" These long time users of bank accounts are still not comfortable with using digital technologies. Even simple 'self service token generation' is a tough task for them.

India as a society is still not ready for a 100% digital banking. Lots of Indians are digitally illiterate, and the complex banking rules and forms intimidate them. Therefore these users still prefer to sit across the table with a bank employee and get their banking tasks accomplished.

Size of this customer segment is still substantial, but will decline rapidly. This implies that Indian banks will operate branches for a long time to come, but for banks to be relevant and profitable, the total value of transactions per employee will have to increase. This implies that a lot of back end jobs will have to be automated, and moving customers to digital platforms.

The nature of Indian customers has slowed down the transition from people-driven to IT driven processes. However, technological development has not slowed down and constant innovation in technology has made online banking easier. This has also led to a slowdown in the hiring of  branch staff at banks, though banks are hiring people with IT skills to drive automation.

HDFC bank for example, saw staff strength fall from 90,421 in December 2016 to 84,325 in the quarter ended March 2017. At the same time, it has expanded its network to 4,715 branches, from 4,520 a year earlier, ATMs to 12,260 from 12,000.

Hire Younger Talent

As banks brace for the digital revolution, Banks will have to infuse their workforce with a lot of younger talent - who are more comfortable in embracing new FinTech solutions. Large public sector banks will be forced to re-balance their workforce by offering voluntary retirement scheme for older staff and usher in a younger, digitally savvy talent pool.

Major Savings is in Backend Processing

Banks can save lot of costs by automating high cost operations such as loan processing. Today with high speed data analytics & AI tools almost 95% of loan requests can be processed automatically, thus eliminating expensive human labor - which also aids in speeding up loan approvals - which inturn helps improve productivity.

A team of 400-500 programmers can automate house loan approval process & that will eliminate 10000's of jobs at banks. Artificial intelligence & data analytics can replace loan underwriters, the IT systems can underwrite loans on the spot. This increases employee productivity in a big way, while reducing operational costs.

Similarly, routine tasks like salary processing will get automated. In fact all low-end back office jobs in banking sector will be automated.

This transformation means all the low skilled workers do not have a bright future! They will have to re-skill or perish!

Impact on Real Estate Costs

After labor costs, rentals on bank branches form the next highest costs for Indian banks. With Automation, the need for a large number of branches will reduce substantially. Some banks do not need as many branches as they have today.

Global banks like Citi, HSBC, etc have already consolidated the number of branches. Looking at the trend in US & Europe, where number of bank branches have shrunk by 20% in last 5 years, I expect a similar trend in India - but with a difference. The reduction of bank branches will be limited to urban areas only, while Indian banks will still add new branches in non-urban areas and the size of branches will reduce rapidly. Overall, I expect the total square footage of branch space will reduce 20-25% in next 5 years, while the total number of bank branches will increase.

Even in rural areas, Branches will need smaller footprints - and the focus in these branches will be to train customers to use digital online platforms. The branch staff will help customers learn and use digital systems - i.e., marry digital technology with human touch.

Changing face of Indian Banks

New banks like small finance banks like Au Financiers, Equitas or Ujjivan will use an army of  people to expand in rural areas, but this army of people will not sit & operate in a typical branch. Instead, they will be like foot soldiers, traveling to customer locations and providing banking services via mobile platforms.

Just like micro-finance companies & cell-phone companies that changed how people borrow, these newer small finance banks will change how people will use banking services. These banks can leverage a large army of small business owners in rural areas to offer human touch to rural customers - with digital platforms.  Customers can walk to any member of this rural service army and get their banking services.

Customers will now recognize the bank by the mobile apps, rather than the physical branches or the employees.

Closing Thoughts 

Banking sector in India will thrive in next 10-20 years and will employ millions. But it will not be the same way as today. Banks will expand branches and increase the reach of its distribution network, but it will be aided in a big way by IT.

New, exciting, & high paying jobs in Banks will be in IT as Banks transform to be a IT driven, software based banking services company.

The new face of the bank will be the mobile app - from which the entire banking transactions can be completed.

Also see: 

Disruptive influence of FinTech on Indian Banks