Blockchain – The Strong Backbone for Businesses

Blockchain – The Strong Backbone for Businesses

What is Blockchain Technology? How one blockchain can have Infinite possibilities & opportunities in hand in this red ocean market world.

Some background

Blockchain as on date is mystery story for many (Including my self). We have heard lot that it yields strong potential in global supply chain. It is become the backbone tracking architecture for an evolving and fully transparent grid. Investors can also look to diversification into this burgeoning new space via first-movers like oil and gas giant BP, and tech companies like IBM. Now some companies are busy in developing a blockchain based system that will become the data security centre in this era of Big Data.

So are we moving from centralise model to decentralise model for all our business!!

So what is blockchain

It is a protocol like any other and reminds me of SwarmIntelligence. The architecture of blockchain present a more secure way of saving and securing data. On one hand this can reduce fraud on other hand it reduces transaction processing times and fees in financial domain. Blockchain in financial domain is used for below purposes (few examples)

  • To send information
  • Track information
  • Transmit information
  • Remittances or cross border money transfer
  • Secure information, mainly in financial world

Blockchain is not bitcoin

Bitcoin is digital money. A virtual currency that was the first successful blockchain product. Blockchain is the technology that enables cryptocurrency and provides solid & secured foundation like bitcoin, ethereum, ripple etc. Lets stop here and put one thing in our minds “BlockChain and BitCoin are not synonyms”.  Blockchains can be compared to traditional BigData or distributed databases like MongoDB as well.

Smart contracts terms of programmable autonomous contracts are ensured by this exemplary technology. Some examples of for use cases outside financial domain are as below.

  • Online voting to address voter fraud.
  • Can be used as secure identity
  • Data security ; as data is the costliest a-fair of today’s time
  • It yields strong potential in global supply chain
  • Retail industry system based on blockchain that can become the data backbone

To illustrate it has many examples like above; at the same time it has many other use cases where transparency and security is lacking.


Blockhchain allows businesses to transact more smoothly and efficiently. Transform your business and digitize your transaction work flows.


Backbone for Businesses

Blockchain can work as strong backbone for businesses. This can even called as immune system of any good business of today. It is kind of distributed digital ledger technology as a powerful tool getting attention at rapid pace. Its like featuring a product that contains small blocks of brain in form of dust in contrast.

Smart Contracts – The term was first coined in 1993. SmartContracts become a buzzword in 2013 when Ethereum project about “Decentralized Platforms’” kicked in. Where it was all about an applications that run exactly as programmed without any possibility of downtime, censorship, fraud or third party interference.

Smart Contract became hero thereafter in moderne businesses across the industry. Ethereum-enabled internet-of-things platform, uses this application to allow customer to rent bicycles where they can unlock a smart lock after both parties agreed on the terms of the contract. This is example from one of the company (Slock) project.

Cloud storage – This as another application that businesses can take advantage off.  The offering is all about secure cloud storage while decreasing dependency.

Hyperledger – This is an umbrella project of open source blockchains and related tools, started in December 2015 by the Linux Foundation, to support the collaborative development of blockchain-based distributed ledgers. All over the global market there are ledgers that organizations and individuals alike must trust.

Blockchain is probably the most revolutionary innovation of the last decade. We are living in Contracts aka third generation.

An angel for your Blue Ocean Shift Strategy

Blockchain can give you the market which is your own and with competition in front. If you understand your business and where to use Blockchain in it then whole of Blue Ocean is certainly yours. The blue ocean shift strategy allows you to sail freely and therefore to grow your business smoothly without any challenge. Now Lets take all action frameworks one by one.

First action – Eliminate

Which of the factors that the industry takes for granted should be eliminated. For this blockchain suites 100% as there are many of them. We can take security issues; It is considered as most secured technology of today. If you have it right you are set.

Second action –  Reduce

Which factors should be reduces well below the industry’s standard. Easy one to guess; cost is your answer. it involves value innovation, which give organizations the ability to combine differentiation and low cost at the same time.

Blockchain allows for simulation.

In this setup transactions are recorded and visible to everyone, therefore it is not purely anonymous. But it does provide pseudonymity. For example, digital wallets are identified via the wallet’s public address or public key. The public key may or may not connected to personal information such as name or address. This allows anyone to transact privately and reputably with data remaining secure.


Conclusion– Blockchain allows you to transform your business and digitize transaction work flows. Finally as a result of this cutting edge technology importance business cant ignore blockchain any more hence its here to stay and grow. This technology has been referred to as the next revolution and although it’s only in the early stages for now.

Blockchain technologies record promises, trades, transactions or simply items we never want to disappear, allowing everyone in an ecosystem to keep a copy of the common system of record. Blockchain allows businesses to transact more smoothly and efficiently. To discover the power of business blockchains and distributed ledger technologies; you might have to wait for next blog post.

Disclaimer – All credits if any remains on the original  contributor only.

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Blockchain – The Strong Backbone for Businesses

Risk is for Real if not Artificial Intelligence

Risk is for Real if not Artificial Intelligence

Artificial Intelligence is the future of growth. There is sure to be at least one article in the newspaper/internet/blogs daily on the revolutionary advancements made in the field of Artificial Intelligence or its subfield disrupting standard industries like Fintech, Banking, Law, or any other. In banking domain digital banking teams of all modern banks planning to transform the customer experience with their AI based chat-driven intelligent virtual assistant i.e. bots.


Amalgamating the latest technology of artificial intelligence, predictive analytics and cognitive messaging to serve millions of customers is now a new winning strategy? AI and regulation are paving the way for Fintech. Artificial intelligence is a new factor of production and has the conceivable potential to introduce new sources of growth, reinventing the business in place, changing how work is done and reinforcing the role of people to drive growth in business.

Artificial Intelligence has become a very popular term today. Banks behind-the-scenes glimpse into the technology that’s designed to grow and develop with customers over time. The lexicographical work of banking & artificial intelligence related buzzwords are on rise every now and then. But it amazed me to think that if how many people still thinks that all their queries and questions especially in 24X7 online chat support are answered by human but fact is human did not respond to these people, its a bot working which does not need of any break or food or rest. It’s astonishing what we don’t know and what we learn by interacting.


Is Artificial Intelligence permanently Impenetrable? 60 years ago when the term ‘AI’ was coined. The whole idea and the ambition was to build smart and emotional machines that can learn, interact, work and put forward reasoning like humans. After several decades of trying, today the original vision is almost getting faint or got abandoned. Today’s machines largely work on model to predict the future, as long as the future doesn’t look too different from the past. But goal is to move towards artificial consciousness & machine consciousness where no computer program will be needed. What is the real risk here?

In today’s time i.e. talking era of artificial intelligence (Not actual, its just talking show) its impossible to predict what is going to come. AI and its sub fields like machine learning, neural networks and deep learning. But its 100% up to us how this technology will behave in future. As said by some one; how technology or robot (end product) will behave depends upon who create/craft/design them with what mind set. Risk is for real if not artificial intelligence. A new study in the journal Intelligence reports that highly intelligent people have a significantly increased risk of suffering from a variety of psychological and physiological disorders.

In the industry usually data mean “numbers and stats”. However, text is also data. AI offers huge space to innovate, re-innovate, and create creative mess for improving customer experiences. Applications of AI and its related techniques like machine learning and deep learning are improving day by day though. Right vision and determined mindset will create best intelligent customer interface that can thrust businesses on an accelerated maturity path to help digital transformation of the organisation with purpose and customer centricity. As on date AI has got some break through at base level and able to perform below activities.

  • FinTech: Predicting customer rating, defaulting in repayment etc. Credit scoring or direct lenders are using AI for credit scoring and lending applications.
  • Insurance: Predicting rate of death or likely a date of death for funeral insurance business.
  • Finance: This category rely on AI chatbots, mobile app assistant applications in order to monitor personal finances and predicting fraudulent activity on a credit card
  • E-commerce: Predicting customer churn
  • Healthcare: Predicting patient diagnostics
  • Social Network: Predicting certain match preferences on a dating website
  • Biology: Finding patterns in gene mutations that could represent cancer


Major reasons of this paradigm shift and increasing adoption of artificial intelligence chatbots (AI driven bots as best example) for use as virtual agents or assistants are changing customer expectations, falling customer satisfaction, and the promise of lower operating costs. Banking, financial services, FinTech services and insurance services are major gainers of AI and clearly taking the lead here. All these players are using AI to monitor regulatory framework adherence, compliance, & Fraud Detection.

HDFC Bank also has an AI chatbot called “Eva”, built for it by India based AI research company, which has “successfully addressed over 2.7 million customer queries in the six months since its inception”. AI can detect fraudulent and abnormal financial behavior, and/or to improve general regulatory compliance matters and workflows. AI uses large volume of data stored and can be utilized for automation, intelligent actions, cloud robotics and machine learning. While this technology may sound revolutionary, it’s actually been around for years in industries like trading.

Robots can fail; as reason is very simple and known which is machine learning models are not sufficiently accurate or can’t be accurate without lots of data and lots of training. Artificial Intelligence with cloud computing add advancements using new use cases to improvise the systems developed so far. Robots or AI enabled AI taking our job is still far not in next 50 years or so. It has to pass basic 4 tests. Turning test i.e needs to acquire college degree, needs to work as an employee for atleast 20 years and perform well to get promotions and attain ASI status.

Artificial intelligence is the only technology, which is going directly from disappointment to deadly with being beneficial in between. Most cognitive architectures developed in the past are highly modular, utilizing, for example, distinct modules for short-term memory (STM), long-term memory (LTM), parsing, inference, planning, etc. Nowadays almost all AI work relates to narrow, domain-specific, human-designed capabilities. Lemmatization of artificial intelligence has many names i.e. narrow, strong/general or super also its subsets i.e. machine learning, machine intelligence, machine consciousness, deep learning etc.

Robots are well suited for work where human are at significant risk, the economics or menial nature of the application result in inefficient use of human workers, and for humanitarian uses where there is great risk. The current AI cloud landscape is categorized into two groups-AI cloud services and cloud machine learning platforms, which provide future Market Insights, and almost accurate research services provide valuable intelligence necessary to gain edge over competition.


Can we really stop this storm of artificial intelligence answer is definitely no as innovations has no stop button. Can we regulate it; answer is yes for sure it can be regulated. Regulation can define boundaries and limits the usage level of AI in each domain of todays work. Word of caution regulatory framework should not be taken as benefit or money producing machine for some or few people or to kill it.

Staying acquainted with the latest and emerging market trends renders companies the competitive advantage they need. HDFC Bank India has claimed that in Sept-2017 its chatbot OnChat able to register a 160% month-on-month growth in transactions. This encouraged us to build a complete ecosystem around Natural Language Understanding (NLU), so that our solutions can talk to clients in their native natural languages.

Please note: I am the big fan; follower and lover of everything about AI, which can bring only best things as just good enough. AI will help and do betterment for humanity. This kind of revolution tells us where we are heading.  Analytics reveals in collected data; how AI-driven apps are influencing consumer behavior on smartphones. An expanding universe of bots – impacting our daily digital behaviors, like shopping, browsing the Internet, and consuming content?

If you’re waiting for A.I, look around you — you will find some form of artificial intelligence at work almost every where/ Though its pinnacle will take around 50 or more years; machine consciousness is indomitable will happen sooner of later. The other side of it for people involved here specifically, those with a high intellectual capacity (hyper brain) possess over excitabilities in various domains that may predispose them to certain psychological disorders as well as physiological conditions involving elevated sensory, and altered immune and inflammatory responses (hyper body).

History don’t lie any ways robots were used in the military and industry invested in robotics in order to build nuclear weapons and power plants, this is were things would go tribally wrong. High intelligence is touted as being predictive of positive outcomes including educational success and income level. However, little is known about the difficulties experienced among this population.

Conclusion – We should see in next couple of years a vast improvement in current state-of-the-art machine learning in cyber-security, payment intelligence and info-security intelligence. Instead, business silently gravitates toward the subtasks that have implicitly performed. Classic example for this in AI is a critical tool to improve customer experience i.e. facial recognition technology, which is 10 to 15 times more accurate than human beings at identifying people. Innovation, which is fueled by advances in computing power and connectivity, the fields of the robotics and artificial intelligence have grown rapidly. With technology advancing at breakneck speeds and demystifying robotics and artificial intelligence with new applications, machinery, and ultra fast process in business, factories and homes in short from teleportation to autonomy.

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Risk is for Real if not Artificial Intelligence

Harnessing Machine Learning in Payments

Harnessing Machine Learning in Payments

Abstract – As I mentioned in my earlier blog post “Machine Learning in FinTech – Demystified”  you will realise today in payments machine learning is one of many advanced, most talked and becoming critically important tools for analytics got its place business toolbox with lot of pride and respect. Main objective is depict how Machine learning can and has already extended into so many aspects of daily life. ML gets the problem-solving call in conjunction with deep learning artificial neural networks. As these jargons i.e AI, ML, DL or ANN etc may be getting their day in the sun, but they’ve been around for a while. It’s just in the past 5-10 years that they have gained traction, technology that was once niche is now becoming more mainstream and cost-effective reaching to common man. Until recent machine learning was known as historical phenomena in the worlds of academia and supercomputing. This is the part 3 in this series first part was Artificial Intelligence for Digital Payments Security and second part was Data Science of Payments.

Introduction – Artificial Intelligence domain is still very new and at just initial stage, trust me no one in todays time (at-least I wont believe any one who says he/she know everything in AI) knows the full domain/scope/boundaries of AI. Technologies like this which don’t have limits can be dangerous also. Machine learning techniques are a great fit to improve the info-security for financial security and getting expanded beyond fraud detection. Due to the nature and process of its working model and to be effective machine learning is very effective involving large dynamic data sets to train it self,  test and do the predictive & prescriptive analysis. I can train and learn the consumer behavior by tracking certain patterns and behavioural biometrics will give it flying wings. When behavior changes it raise alarm and when behavior’s change, it can detect subtle shifts in the underlying data, and then revise algorithms accordingly.

Main Story – Machine learning is not new in payments industry its well known and familiar tool primarily used in credit card transaction monitoring game at basic level. In card industry rule based learning algorithms play important roles in near real-time authorization of transactions. I am sure these application companies will admit that it’s very early days for this proposed future. As such, all of these assistants are far from polished. That said, I would agree that most AI applications nowadays are indeed using or will use ML soon. ML can be handy for us to do some tasks much better then human impact in certain industries. For instance, we might think of fraud detection as the canonical example of machine learning in the financial sector. Currently, majority of machine learning approaches in info-security is used merely as basic “alarm” or as simple “warning” system. Which often needs human intervention to make action and make decision which may not happen after few years down the line.

As a result, the humans has the final decision due to their lower false rates any critical or important decision on payment intelligence matter. May be few years down the line from now our immediate reaction to the same question’s (As mentioned above) answer would be “Please don’t hurt us”. Just to describe on simple and high level; the three widely used terms i.e. Artificial Intelligence, Machine Learning and DeepLearning can be arrange as below. Deep learning, which is itself a kind of machine learning is becoming more an more popular and successful at different use case. BaaS (Banking as a Service) came as friend which a package of best deals i.e best analytics blended with artificial intelligence, data intelligence,  payment intelligence, Big Data and really deep technology with help of deeplearning i.e selfie based payments. Platform to perform BaaS software service BaaP (Banking as a Platform) got emerged, for all such companies to break bank’s attitude as they were long seen as a highly technical, highly complex with rocket science technology.


AI_ML_DL.png 1400w, 100w, 300w, 768w, 1024w” sizes=”(max-width: 700px) 100vw, 700px” />

Behavioral biometrics is the undisputed example of machine learning for information security. You may merely have to look at a variety of ubiquitous technological experiences you undergo each day, and find a myriad of machine learning applications at your core of the day. Companies have started managing their data like they manage their money. Recommendation engines at Netflix and Amazon are best examples of machine learning in retail. In matter of next ten years or less, AI will be ingrained in everything we do today. Today we ask what AI can do for us other then harming as described by some big names of this industry. It’s a basic but profound question that merits some thoughts based on my 19 years experience managing / running / developing / designing both information and financial technology functions in technology and data businesses. Long and short of it AI can do and have done lot of good things.

payment[1] 302w, 100w” sizes=”(max-width: 151px) 100vw, 151px” />Imagine imagination as a tool because it helps us move beyond mental blocks. To understand what “imagination” is, we could look at how it works. Threat Intelligence will play a bigger role coupled with an evaluation of the driving factors and key capabilities required by convergent systems and requirements. With advancement in technology, organisations outside the banking industry diversified into financial services targeting margins in the space. These were organisations servicing millions of customers through broad distribution channels, be they mobile operators, retailers or on-line merchants.

The most dramatic advances in AI are coming from a data rich or data greedy techniques i.e machine learning & deep learning. Machine learning requires lots of data to create, test and “train” the AI. Which is the best direction? The answer lies in the analysis of future technologies development within the 3GPP framework (For Telecom), FinTech, AI and AGI, Machine learning & Deep Learning. There’s no single answer to this without end-to-end architectural analysis. AI and blockchain  combination is explosive! Blockchain technologies . It  can help realize some long-standing dreams of AI and data analysis work, and open up several opportunities.

cropped-ds.jpg 1400w, 100w, 300w, 768w, 1024w” sizes=”(max-width: 700px) 100vw, 700px” />ANN, ML & DL are type of or subdomain of artificial intelligence (AI) where computers can essentially learn concepts on their own without being programmed. The scenario is already quite imaginable. For example, AI might program your decision based on stock market data where to invest and how for how long in what. The implications are vast and not always completely understood. For example, it’s commonly believed that AI will ease traffic congestion and pollution; now some experts wonder if it will actually lead to less public transit use and make people fatter and out of shape because they don’t walk to the bus any more.

Conclusion –  In short all I can say with full confidence is machine learning is going be diva and  major opportunities finder/locator in payments. AI can be very dangerous if it gets into our life too deep (actually its almost there). AI take us to work with shorted and fastest possible route, and when we get there we use tools system based on big data analytics to make our business decisions. Another intelligence engine normally decides, based on what kind of day we had yesterday(data based); what we will be doing today as appoints to achieve todays goal we might get options or  perhaps our bot will already schedule/reschedule our appointments. But what will happen when AI will develop a mental disorder?, if mind cant be insane its actually not mind. AI over taking almost every felid of todays’s industries, Elon musk is repeatedly telling us that AI will lead us to some disaster a major disaster and after that nothing will be left behind. ANI (artificial narrow intelligence) based applications like Cortana, Siri, Alexa and Google Assistant in market as AI babies but still technology has come a long way.

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Harnessing Machine Learning in Payments

Artificial Intelligence for Digital Payments Security

Artificial Intelligence for Digital Payments Security

Abstract – Digital wallets are becoming the new way to pay. Most interesting factor here is to know companies who are most successful in this field actually came out of payment industry with no prior knowledge or payment intelligence but rather came with Artificial Intelligence. How to pay and where to pay, when to pay etc. Disrupted the most unknown and unsecured payment methods like contactless payment systems. Does any one ask any question from these ventures weather their suggested methods are safer, secured or how much vulnerable than chip-enabled plastic cards, and what it might take for contactless systems to be used as widely as cash. How the data or sensitive data of a customer been treated and used in their systems. The AI sub-field called “Artificial General Intelligence” (AGI) is most relevant. AGI is about autonomous agents interacting in an environment.

Introduction – Apple is in talks to launch its money transfer service may be as Apple Cash. JPMorgan quits R3 block chain consortium though. In the Q1 of 2017; $3.2 Billion United sates dollars went into FinTech Deals. The world is becoming increasingly interconnected through mobile phones and the Internet. The digital wallet revolution has not have lived up to its expectations yet. According to Gallup, just 13 percent of smartphone owners have a digital wallet app, while the majority of those who do have an app (76 percent), rarely use it. That may be disheartening for those of us involved in the industry, but there’s always a silver lining. Today, you can see how everyone else lives, in his or her own countries and abroad. But while the aspirations of people around the world are converging, opportunities are not.

Main Story – Machine learning gets the problem-solving call. Deep learning algorithms, AI and neural networks may be getting their day in the sun, but they’ve been around for a while. It’s just in the past 15 years that they have gained traction, technology that was once niche is now becoming more mainstream and cost-effective. Additionally, today’s marketplace is increasingly technology-dependent. I spoke at the NGT Africa Summit last week in Lusaka-Zambia to demonstrate issues around Privacy, Innovations, and Security in Digital Payments. One point agenda was to highlight the need to accelerate development of privacy & security as basic requirement in any innovation. With this advancement people can lift themselves out of single mindset (being just inventors or disruptors).  

To use limited funds in the most effective way possible, we must fundamentally rethink our approach to development finance.


In 2017, why we dont have some very basic measure in place for every payment system for example when someone type his/her own password or PIN.

We humans are same and lazy enough and love not the accept or like changes but wants to shout slogans “change is the only constant thing”. Our habits of about using computers, mobile devices or almost everything  in a ways that are unique and consistent with our own biology and physiology. System should be able to learn the behaviour and style and when the same person types 3rd person password or PIN system should be able to detect and raise alarm. The way some one click and type, the way any one use mouse and other input devices are pretty consistent with that person’s own behavior, habits, education level, and familiarity with a service or system. Basic security of typing wrong password is no longer good or doing anything good. Complexity and cost to be redistributed to implement the considered security measures in innovation. Aspirations, linked to opportunity, can breed dynamism and inclusive, sustainable economic growth. That’s something we all want to see. Meanwhile, tech giants like Apple, Google and Intel are investing billions of dollars in using machine learning AI’s to build out their next big projects.


We usually encounter in our organization where CMO says my marketing is good, CTO says my network is good and CIO says my IT is good and every claims the are best in their work area but still customer says I am not happy as my experience and satisfaction level are not same. In order to change economics and political science of innovation; ISO 12812: released new standards for mobile banking. Apple is looking for new ways to boost usage of Apple Pay, and the debit card could be one way to do that. On the other hand this makes lots sense as Apple used invention that was on table from long time but still its Pay usage has been lighter than expected since it launched two and a half years ago.

Chatbots harness software that uses artificial intelligence (AI) to process language from interaction with humans in chat programs and virtual assistants. For humans our habits and behaviors are very difficult to change and if we can identify legitimate users by their typical behavior patterns – we can detect anomalies on a totally new level. Same goes with fraudsters – ability to identify and quantify behavior patterns of cyber criminal will allow us to uncover and neutralize threats that may be undetectable by other means. The concept of chat bots are coming up very fast but its limited to smart devices holders. As chatbots make their way into our daily life, financial institutions are laying the groundwork to use the software to automate customer service, move transactions and lower costs. Just to reiterate chatbots are not new or very different then  audio-based bots such as Apple’s Siri, Samsung Play, Amazon’s Alexa and Google Home are all now household names. AGI can be modeled as a feedback control system. This is  an excellent idea of AI great because control systems have many great qualities.The bot uses cloud-based AI developed by LivePerson alongside self-service software to intelligently answer or “surface” answers to frequently asked questions.

AI may just be starting to see the era of its own in digital payments but if industry trends are to be believed, it’s fast becoming the soul of the global fraud-fighting mechanic.Applying Robotic Process Automation in Banking, fintech or any financial services business. Robotics is quickly gaining traction in banks to automate their everyday finance and risk processes. The best to protect data from stealing by hackers; may be by not converting and putting the data in system, In the old days people says keep smiling it does not cost you anything hang on this is not true any more. By not creating digital data can we survive answer is NO, smile has cost in todays time as you pay for your bills for shopping with smile or selfie pic. Threat modeling in context of security architecture using machine learning to fight frauds, supporting compliances and regulations and taking care of preventive measures of any leakage of sensitive information and data.

BI does not take any action it just tells something about data but if we add AI on top of BI so that AI can trigger and kicks actions on BI findings how it would look like. Bank of England issues statement recently about its plan for next generations settlement system that will be DLT (distributed ledger technology) Compatible. While my regular reading I found on Google search a very interesting fact about Apple, if its free cash meets expectations, will have roughly doubled its holding in a little over 4.5 years in the last quarter of last year Apple was reportedly bringing in about $3.6 million per hour in revenue. In perspective, that is more cash on hand than the market cap of Walmart and the foreign-currency reserves held by the U.K. and Canada combined.  Applying Robotic Process Automation in Banking, fintech or any financial services business can bring significant savings and reduction in negative impacts. Robotics is quickly gaining traction in banks to automate their everyday finance and risk processes. The Royal Bank of Scotland is rolling out a customer service “hybrid bot” from vendor LivePerson that hands over to a human colleague if questions flummox its artificial intelligence. With this this tool customers can message for their day to day with queries.

Arguably, the most destructive cyberattack is distributed denial of service attack (DDoS). Other attacks cause great harm—steal computing power, exfiltrate sensitive information, hold files and devices for ransom. But DDoS attacks are brute destruction of critical services. As the Dyn attack demonstrated, they can extend far beyond single organizations. Use of AI servers with AI logics which been learnt over time DNS are essential, so they are a primary DDoS target—but they are preventable or defensible. A variety of techniques exist for fortifying them in todays time but AI would be much more effective compare to the tools at hand. 

  • Distributing query processing by combining hosted and on-premises DNS services, deploying recursive servers to the network edge, and creating redundant DNS architectures
  • Using response policy zones to cut off botnets and create whitelists for legitimate traffic
  • Rate-limiting noncompliant devices
  • Sharing threat intelligence to stay ahead of attackers and create a unified front

In Digital Banking industry this year, the leaders who are creating state-of-the-art apps, chatbots, authentication and internet-of-things applications will throw on what they do but why and how answers may not come out. The issues will be debated, like how and with whom to share account data and whether or not to try to compete with top-rated mobile wallet apps. And the brightest minds in the industry should lend hands to share ideas, network and collaborate. Business Insider is forecasting that in the U.S. alone mobile payments volume will increase to $503 billion by 2020 and will be used by 56 percent of the consumer population during that year. Meanwhile, Sweden, Singapore, the Netherlands, France, Canada, Belgium and the U.K. are already on their way to becoming cashless societies – with Australia, Brazil, India and much of Africa following suit.

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Artificial Intelligence for Digital Payments Security

Big Data for FinTech & InsureTech

Big Data for FinTech & InsureTech

Abstract – Last Sunday I was at big retail store in Harare and it was a very busy day due to the fact it was month end and people got paid. Grocery shopping was in full swing, I also bought some groceries for my self. When I was in the queue for payment and collection, I saw almost every one making payment either by swiping the magic plastic card or struggling on their mobile handset by punching few numbers etc. The electronic payment queue was moving fast compared to the cash payment queue where I saw only a handful of people with just one/two small item/items. The thought came to my mind out of this whole picture was “Whats happening here besides the payments through mobile and plastic”? Data, More Data, Lots of Data so called BIG DATA was getting generated.

Introduction – Without the right security and encryption solution in place; big data is a very big problem. A smart Big Data factory should take smart approach to this costly, sensitive and critical asset maintenance and management. Before we go further let me explain un short what is Big Data, I am sure most of us knows the answer already; Big data is term that means a huge amount of Digital Data. This data is unorganized and unstructured because it is capture from different sources. So it is difficulty to analysis. For instance cardholder data should be managed in highly secured data vault, using multiple encryption keys with split knowledge and dual/triple control. In todays time with the help of Artificial intelligence, data security took another angle where now actual data is mapped to dummy data and actual data never gets into internet black hole as this data store cannot be connected to/via the internet but remains at back seat. A data thief would not be able to make use of information stolen from a database without also having multiple level of keys.

Main Story – Big data presents a tremendous opportunity for enterprises across multiple industries especially in the tsunami like data flow industry of “Payments”. FinTech, InsureTech, MedTech are major data generating industries i.e massive group of factories. According to some data from Google it shows technology based innovative insurance companies pays $0.60-$0.65 of each dollar in claims, with the rest covering costs of admin, marketing and reinsurance. Next questions were “Who owns this data?”, “What is the use of this data?” and “How secure is this data?”. My payment data with all my sensitive information is it secured and in safe hands? What about privacy of my sensitive information?. Thousands of questions started spinning my head. There is a massive scope of big data security. This presents a significant opportunity for disruption. With improvements in technology which anyways happening every day without demand and this will bring reduction in each of these cost items.

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Big Data for FinTech & InsureTech

Artificial Intelligence to Amplify FinTech

Artificial Intelligence to Amplify FinTech

AI has taken some steps into banking, but it also poised to transform how banks learn from and interact with customers. Financial services will lead the charge in the implementation of AI. Africa’s mobile phone market has expanded to become larger than either the EU or the United States with some 650 million subscribers (2016 data). At the same time, Internet bandwidth has grown 20-fold as hundreds of thousands of kilometres of new cables have been laid across the continent to serve an increasing number of its 1.2 billion Africans. Augmented experience on how to recommend how much to spend and on what. AI is already driving the reinvention of existing products and interactions. Endowing the modern workforce with AI, machine learning, payment intelligence and advanced analytics fintech will thrive, amplify and fly. FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track.

The most striking AI solutions to FinTech, banks, insurance companies (now called InsureTech) and any other financial services company will probably be those that have the robust & smart financial systems with data security, machine learning (machine conciseness is very far for now) and strong analytics features in place. On usage and acceptance part of AI i.e. AI for intelligence or AI for automation we will probably see both types of services getting emphasis heavily by Fintechs to gain a competitive lead. AI will sit on top of every industry of today and will dictate what to do, when to do, how to do and what not to do. Usage of statistics analogy for real life problems to make succinct sense of the information around us support intelligence for better prediction and strategy. Predictions are fintech could be bigger than ATMs, PayPal, and Bitcoin combined in few years time. FinTech brings efficiency as well as the ability to deliver new services and a much improved customer experience throughout the global financial services industry.

Deep dive into how artificial techniques runs on a banking is out of scope for this article but will touch at high level. For instance bill payment for your shopping i.e. ecommerce payments through fintech products (BaaS and BaaP) of course, is not the only industry to leverage recent advances in machine learning. FinTech is a best candidate for cloud computing as it can fit in the environment rapidly and quite intelligently with swift response. New services can be quickly developed, deployed, and scaled on public, private, and hybrid clouds under FinTech umbrella. Harnessing FinTech with AI and cloud computing will be the financial super intelligent services. Blockchains for Artificial Intelligence A planetary-scale blockchain database (IPDB) unlocks opportunities. In addition, these current technologies are being improved daily, with these improvements being fueled by greater data analytics, reduction in the cost of computation, and advancements in the state of the art of machine learning research.

The list of companies benefiting from AI and industries is growing by the day in addition to the various applications of machine learning. Common applications of machine learning in today’s technology include voice recognition, fraud detection, email spam filtering, text processing, search recommendations, video analysis, etc.  Data sharing leads to  better models and qualitatively new models. Audit trails on data & models for more trustworthy predictions. Shared global registry of training data & models. Data & models as IP assets gives data & model exchange for betterment and scaling up business in least possible time and least cost.


Micro-service philosophy favors AI for better machine learning environment to decentralise all aspects of softwares and design becomes super simple but difficult and too much for humans to handle in manual manner. This is where fintech companies are successfully leveraging AI. FinTech companies with help of AI are finding cheap, easy and swift methods to apply the technology to an existing business problem at the same time many banks are failing to do so. AI technology such as specialized hardware, AI based operating systems, strong and large data analytics tools for big data, machine learning algorithms for machine intelligence, payment intelligence, data intelligence and info-security intelligence are being used in fintech to augment tasks that people already perform. This focus doesn’t just guide of businesses for training its machines but also help through supervised learning process how data is persisted.

The down side is also extremely ugly here like in monolithic approach for software design create huge problems when data changes over time. Data science techniques designed for this environment with strict programs wont help or do anything. Like we say Big Data is nothing or life less with strong, intelligent and powerful algorithm which can change it self over time. Weak algorithm can cause billions of dollars in fraction of seconds for example any  high-frequency trading algorithms picked if pickup some dangerous keywords like quake, terror attack or tsunami etc from a trusted source can get into overdrive and react to what they perceived as a confirmed bad news that never actually happened. The cost to the market? can runs in billions and this has happened in past any ways. This kind of orchestrate attack with this magnitude can never be caused in micr-service and traditional environments. Because of catastrophic financial implications a human in this case can detect, defuse and reject this false alarm.


Data alone life-less and inherently dumb. It doesn’t do anything alone without support and knowledge & tools on  how to use it, how to act on it. Algorithms is where the real value lies. Algorithms define & drive action. Whole point of the algorithms involved in high-frequency in business is to detect, analyse and make decisions faster than a human heartbeat.  FinTech known through the help of AI that, in one way or another how to make money. Because of this, new wild and flashy AI systems that are making FinTech ‘s smart systems smarter  and can help to them to fly. Not surprisingly, these companies each have a clear market application and reduce friction in the business problems they address. Fintech’s Artificial Intelligence revolution is perfect example and era of pervasive AI financial technology services.

Business success should be AI goal. AI is a service enabler tool in defining Fintech goals and guide to achieve them. Data Science understands the significance of data and machine learning is not new in payments industry anyways; its well known and familiar tool. Descriptive Analytics is all about using cutting edge tools meant for data science to understand what has happened in the past and how this will predict the future. This is for learning and to know how to manage the present & future by understanding the past. The biometric authentication feature associated with mobile wallets is a great example with promising feature but still very far from a basic security that can catch the fraudster with behavior biometrics though. With AI power to enable security features of mobile payments mean the technology could gain traction in other areas of B2B payments and escalate blockchain to generalize, any previous application of AI, but now the AI “owns itself”.

Conclusion – Artificial intelligence is set to transform the financial services industry. How AI will be transforming the future of finTech to elaborate items from the above list in African markets and opportunities are even more dramatic – In just the past five years. Modern mobile payment infrastructure availability – Africa has payment instruments such as mobile wallets for merchant payments, bill payments, prepaid airtime top-up etc. Smart machines producing smart payments with inbuilt payment intelligence. High-powered algorithms are not a new phenomenon in finance though, and for this industry, the name of the game is efficiency and precision which suites more for FinTech due to their fast adopting nature and risk appetite. Artificial intelligence may be all the craze in Silicon Valley, but on Wall Street, well, there’s a lot of skepticism. Unfortunately so far only intelligence got artificial but risk still remains for real and natural. Natural language generation can create, write and tell your business stories but still raise hands when risk over takes. I am getting tempted to say — this time is really different. AI DAOs – AI that can accumulate wealth, that you can’t turn off.

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Artificial Intelligence to Amplify FinTech