Financial Tech Technological Innovations

Financial Tech Technological Innovations

Whether all Fintech should be considered disruptive innovation or not is a matter for debate, according to well-educated writers at The Harvard Business review. But it doesn’t take an Ivy League education to recognize that many of the new FinTech technological innovations in the finance sector are sending tremors across the industry. Old-fashioned practices like going to the bank are being superseded by online transactions using hand-held devices. And there’s much more.

There is nothing narrow in the definition of Fintech. The term can apply to any technology solution used to deliver financial services. According to Investopedia, Originally thought of as “FinTech technological innovations applied to the back-end of established consumer and trade financial institutions, ” now the word is used to describe any technological innovation in the financial industry.

A Broad Impact

​FinTech has been around for a long time, working in the background to support banks, trading companies, insurers, and other financial institutions. Now FinTech technological innovations include platforms that manage end-to-end processes like the interaction of users on their smartphones. The aim is to use technology creatively to make life better.

A simple example is online mobile banking. Most banks now allow customers to log in to their accounts through a secure website or app. This capability alone has changed the way people do their banking. Online banking means customers can check balances, transfer money, or pay bills without ever stepping foot into a bank lobby. And the ubiquity of credit or debit transactions in stores eliminates the need to withdraw cash at the bank or an ATM.

Past Present and Future FinTech Technological Innovations

We’ve been using Fintech for a while now. An article on “The Evolution of Fintech” in Forbes says that Fintech is “a very broad sector with a long history”. That history includes credit cards, ATM machines, bank mainframes, e-commerce, trade processing, and data analysis. We can even go back to Friday, October 15, 1954, when history’s first automated payroll checks were printed by the UNIVAC machine, as I described in “Milestones in Digital Computing”.

Today, Fintech is apparent in technologies that we use every day. In an article about the evolution of Fintech, ComputerWorld gives the examples PayPal, Apple Pay, Google Wallet, Charles Schwab, TD Ameritrade and Fidelity Investments. But that just scratches the surface. For a bigger list, take a look at “The Fintech 50: The Complete List 2016”. These financial technologies may not currently be on your radar, but maybe they should be.

Fintech expert Alex Rampell discusses “The Future of Money: Banking on Fintech” in a YouTube video. He says that millennials don’t want to talk on the phone or visit bank locations. They are used to doing everything on their smartphones. Rampell says that services such as core banking, new financial products, insurance, and investing are being addressed with Fintech solutions. The four main debt categories, credit card, mortgage, auto loans, and student loans, will also have apps that are widely adopted.

Other predictions about the future of FinTech technological innovations and banking, such as this video from Avonade and another from Newgen software, might make you wonder if the technology might become a bit too intrusive. But those things will be worked out in society and in the marketplace. A special report from the Financial Times on the future of Fintech might be a better place to start.

Another way to look at it is to say that the future is not that far off. Using the smartphones and other computing devices that we already possess, financial service providers can easily make their offerings available through a simple app. And they are already doing that. The question remains whether we are prepared to entrust our financial transactions to them.

A Fintech Primer

We said that Fintech has a broad definition. Every technology has its own language with its own special terms. We won’t cover them all here because the work has already been done for us. CNBC has done a fine job in defining Fintech terms in their article “Everything you’ve always wanted to know about Fintech”. Some of the terms covered are:

  1. Cryptocurrency – a decentralized digital currency which uses encryption
  2. Bitcoin – a peer-to-peer version of electronic cash
  3. Blockchain – a form of distributed ledger technology (DLT)
  4. Ethereum – another type of blockchain network
  5. Regulatory technology (regtech) – technology which helps firms working in the financial services industry meet financial compliance rules.

And you might be wondering about the spelling of the word we have been describing. I’ve seen FintechFinTech, or fintech. I’m not sure that there is any accepted spelling as of yet.

Conclusion of FinTech Technological Innovations

 Some people may wonder when we will start using Fintech, but the answer is that we already are. The financial technology that we have been using for years — including credit cards and ATM machines — may not have been called Fintech, but the definition applies. You can probably think of many others in your daily experience. As with any technology, there is more to it than creating the ability to do something. It all comes down to which Fintech apps are widely adopted in the market place, and how financially successful those FinTech technological innovations and solutions become via artificial intelligence and predictive analytics.

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Blockchain Technology Securing IoT Infrastructure?

Blockchain Technology Securing IoT Infrastructure?

Blockchain Technology Securing IoT Infrastructure?

The growth of the Internet-of-Things (IoT) paradigm begs the question if blockchain technology securing IoT infrastructure properly or not?  Currently propelled by an unprecedented increase in the number of internet-connected devices. Even though the Cisco’s 2011 projection about 50 billion devices in 2020 is not ending up being very accurate, more recent estimates by Gartner and IHS confirm the tremendous growth of the number of IoT devices.

Understanding Blockchain Technology Securing IoT infrastructure

The need to support billions of devices in the years to come is inevitably pushing IoT technologies to their limits. Despite significant progress in blockchain technology, the specification and implementation of IoT technologies for identification, discovery, data exchange, analytics and security, the future scale of IoT infrastructure and services is creating new challenges and ask for new paradigms.As a prominent example, IoT security is usually based on centralized models, which are centered round dedicated clusters or clouds that undertake to provide authentication, authorization and encryption services for IoT transactions. Such centralized models are nowadays providing satisfactory protection against adversaries and security threats.

Nevertheless, their scalability towards handling millions of IoT nodes and billions of transactions between them can be questioned, given also recent IoT-related security attacks which have manifested the vulnerabilities of existing infrastructures and illustrated the scale of the potential damage.

blockchain technology securing IoT data and devices

In particular, back in October 2016, a large scale Distributed Denial of Service (DDoS) attack took place, which affected prominent Internet sites such as Twitter, Amazon, Spotify, Netflix and Reddit. The attack exploited vulnerabilities in IoT devices in order to target the infrastructures of, a global infrastructure and operations provider, which serves major Internet Sites.

The incident is indicative of the need for new IoT security paradigms, which are less susceptible to attacks by distributed devices and more resilient in terms of the authentication and authorization of devices. In quest for novel, decentralized security paradigms, the IoT community is increasingly paying attention to blockchain technology, which provides an infinitely scalable distributed ledger for logging peer to peer transactions between distrusted computing nodes and devices.

Most of the people that are aware of the paradigm to blockchain technology securing IoT perceive it as the main building block underpinning cryptocurrencies such as the well-known BitCoin. Indeed, the main characteristic of Bitcoin transactions is that they are not authenticated by a Trusted Third Party (TTP), as is the case with conventional banking transactions. In the case of the BitCoin, there is no central entity keeping track of the ledger of interactions between the different parties as a means of ensuring the validity of the transactions between them. Instead, any transaction occurring between two parties (e.g., A paying 1 Bitcoin to B) is kept in a distributed ledger, which is maintained by all participants of the BitCoin network and which is empowered by blockchain technology. Among the merits of this distributed ledger approach is that it is very scalable and more robust when compared to traditional centralized infrastructure.

This is due to the fact that the validation of transactions is computationally distributed across multiple nodes, as well as due to the fact that the validation requires the consensus (“majority vote”) of the whole network of communicating parties, instead of relying on a centralized entity. In this way, it is practically impossible for an adversary to attack the network, since this would require attacking the majority of nodes instead of one or a few parties.

The scalability and resilience properties of the blockchain approach have given rise to its applications in other areas such as electronic voting or IoT transactions. The principle remains the same: Transactions are logged in the distributed ledger and validated based on the majority of nodes, even though in the case of voting and other transactions Bitcoin units are replaced by votes or credits. This results in a trustful and resilient infrastructure, which does not have a single point of failure.

Based on the above principle, blockchain is deployed as an element of IoT infrastructures and services, which signifies a shift from a centralized brokerage model, to a fully distributed mesh network that ensures security, reliability and trustworthiness. Blockchain technology securing IoT infrastructure facilitates devices to authenticate themselves as part of their peer-to-peer interactions, while at the same time increasing the resilience of their interactions against malicious adversaries. Moreover, this can be done in a scalable way, which scales up to the billions of devices and trillions of interactions that will be happening in the coming years.

Cases based on IoT Blockchain Technology Securing IoT

blockchain technology securing IoT systems

The development of secure mesh IoT networks based on blockchain technology is no longer a theoretical concept. During the last couple of years several companies (including high-tech startups) have been using blockchain technology in order to offer novel IoT products and services. The most prominent implementations concern the area of supply chain management. For example, is applying blockchain in the pharmaceuticals supply chain, as means of ensuring drug safety.

The company’s service uses the blockchain technology in order to log all transactions of a drug’s lifecycle, starting from its manufacturing to its actual use by a health professional or patient. Recently, the retail giant Wal-Mart Stores Inc. has announced a food products track and trace pilot based on blockchain technology. The pilot will document all the steps associated with tracking and tracing of pork, from the farm where the food is grown, to the supermarket floor where it is shipped. This pilot is a first of a kind effort to validate the merits of the blockchain outside the scope of the financial services industry.

Beyond supply chain implementations, novel products are expected to emerge in the areas of connected vehicles, white appliances and more. Several of the applications are expected to benefit from blockchain’s ability to facilitate the implementation of monetization schemes for the interaction between devices. In particular, as part of blockchain implementations, sensors and other IoT devices can be granted micropayments in exchange of their data.

The concept has already been implemented by company tilepay, which enables trading of data produced by IoT devices in a secure on-line marketplace. At the same time, cloud-based infrastructures enabling developers to create novel blockchain applications are emerging. As prominent example Microsoft is providing a Blockchain-as-a-Service (BaaS) infrastructure as part of its Azure suite.

Overall, blockchain technology is a promising paradigm for securing the future IoT infrastructures. Early implementations are only scratching the surface of blockchain’s potential. We expect to see more and more innovative products in the next few years.

In this direction, several challenges need also to be addressed, such as the customization of consensus (i.e. “majority-voting”) models for IoT transactions, as well as efficient ways for carrying out the computationally intensive process of transaction verification. Solutions to these challenges will certainly boost the rapid uptake of this technology in the IoT technology landscape.


Smart Objects: Blending Artificial Intelligence into the Internet of Things

Smart Objects: Blending Artificial Intelligence into the Internet of Things


Smart Objects: Blending AI into the Internet of Things

smart objects artificial intelligence

It’s been more than a decade since the time when the number of internet-connected devices exceeded the number of people on the planet. This milestone signaled the emergence and rise of the Internet of Things (IoT) paradigm, smart objects, which empowered a whole new range of applications that leverage data and services from the billions of connected devices.  Nowadays IoT applications are disrupting entire sectors in both consumer and industrial settings, including manufacturing, energy, healthcare, transport, public infrastructures and smart cities.

Evolution of IoT Deployments

During this past decade IoT applications have evolved in terms of size, scale and sophistication. Early IoT deployments involved the deployment of tens or hundreds of sensors, wireless sensor networks and RFID (Radio Frequency Identification) systems in small to medium scale deployments within an organization. Moreover, they were mostly focused on data collection and processing with quite limited intelligence. Typical examples include early building management systems that used sensors to optimize resource usage, as well as traceability applications in RFID-enabled supply chains.

Over the years, these deployments have given their place to scalable and more dynamic IoT systems involving many thousands of IoT devices of different types known as smart objects.  One of the main characteristic of state-of-the-art systems is their integration with cloud computing infrastructures, which allows IoT applications to take advantage of the capacity and quality of service of the cloud. Furthermore, state of the art systems tends to be more intelligent, as they can automatically identify and learn the status of their surrounding environment to adapt their behavior accordingly. For example, modern smart building applications are able to automatically learn and anticipate resource usage patterns, which makes them more efficient than conventional building management systems.

Overall, we can distinguish the following two phases of IoT development:

  • Phase 1 (2005-2010) – Monolithic IoT systems: This phase entailed the development and deployment of systems with limited scalability, which used some sort of IoT middleware (e.g., TinyOS, MQTT) to coordinate some tens or hundreds of sensors and IoT devices.
  • Phase 2 (2011-2016) – Cloud-based IoT systems: This period is characterized by the integration and convergence between IoT and cloud computing, which enabled the delivery of IoT applications based on utility-based models such as Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). During this phase major IT vendors such as Amazon, Microsoft and IBM have established their own IoT platforms and ecosystems based on their legacy cloud computing infrastructures. The latter have alleviated the scalability limitations of earlier IoT deployments, which provided opportunities for cost-effective deployments. At the same time the wave of BigData technologies have opened new horizons in the ability of IoT applications to implement data-driven intelligence functionalities.

AI: The Dawn of a new era for Smart Objects using IoT applications

Despite their scalability and intelligence, most IoT deployments tend to be passive with only limited interactions with the physical world. This is a serious set-back to realizing the multi-trillion value potential of IoT in the next decade, as a great deal of IoT’s business value is expected to stem from real-time actuation and control functionalities that will intelligently change the status of the physical world.

In order to enable these functionalities we are recently witnessing the rise and proliferation of IoT applications that take advantage of Artificial Intelligence and Smart Objects.  Smart objects are characterized by their ability to execute application logic in a semi-autonomous fashion that is decoupled from the centralized cloud.  In this way, they are able to reason over their surrounding environments and take optimal decisions that are not necessarily subject to central control.

Therefore, smart objects can act without the need of being always connected to the cloud. However, they can conveniently connect to the cloud when needed, in order to exchange information with other passive objects, including information about their state and the status of the surrounding environment. Prominent examples of smart objects follow:

  • Socially assistive robots, which provide coaching or assistance to special user groups such as elderly with motor problems and children with disabilities.
  • Industrial robots, which complete laborious tasks (e.g., picking and packing) in warehouses, manufacturing shop floors and energy plants.
  • Smart machines, which predict and anticipate their own failure modes, while at the same time scheduling autonomously relevant maintenance and repair actions (e.g., ordering of spare parts, scheduling technicians visits).
  • Connected vehicles, which collect and exchange information about their driving context with other vehicles, pedestrians and the road infrastructure, as a means of optimizing routes and increasing safety.
  • Self-driving cars, which will drive autonomously with superior efficiency and safety, without any human intervention.
  • Smart pumps, which operate autonomously in order to identify and prevent leakages in the water management infrastructure.

The integration of smart objects within conventional IoT/cloud systems signals a new era for IoT applications, which will be endowed with a host of functionalities that are hardly possible nowadays. AI is one of the main drivers of this new IoT deployment paradigm, as it provides the means for understanding and reasoning over the context of smart objects. While AI functionalities have been around for decades with various forms (e.g., expert systems and fuzzy logic systems), AI systems have not been suitable for supporting smart objects that could act autonomously in open and dynamic environments such as industrial plants and transportation infrastructures.

This is bound to change because of recent advances in AI based on the use of deep learning that employs advanced neural networks and provides human-like reasoning functionalities. During the last couple of years we have witnessed the first tangible demonstrations of such AI capabilities applied in real-life problems. For example, last year, Google’s Alpha AI engine managed to win a Chinese grand-master in the Go game. This signaled a major milestone in AI, as human-like reasoning was used instead of an exhaustive analysis of all possible moves, as was the norm in earlier AI systems in similar settings (e.g., IBM’s Deep Blue computer that beat chess world champion Garry Kasparov back in 1997).

Implications of AI and IoT Convergence for Smart Objects

This convergence of IoT and AI signals a paradigm shift in the way IoT applications are developed, deployed and operated. The main implications of this convergence are:

  • Changes in IoT architectures: Smart objects operate autonomously and are not subject to the control of a centralized cloud. This requires revisions to the conventional cloud architectures, which should become able to connect to smart objects in an ad hoc fashion towards exchanging state and knowledge about their status and the status of the physical environment.
  • Expanded use of Edge Computing: Edge computing is already deployed as a means of enabling operations very close to the field, such as fast data processing and real-time control. Smart objects are also likely to connect to the very edge of an IoT deployment, which will lead to an expanded use of the edge computing paradigm.
  • Killer Applications: AI will enable a whole range of new IoT applications, including some “killer” applications like autonomous driving and predictive maintenance of machines. It will also revolutionize and disrupt existing IoT applications. As a prominent example, the introduction of smart appliances (e.g., washing machines that maintain themselves and order their detergent) in residential environments holds the promise to disrupt the smart home market.
  • Security and Privacy Challenges: Smart objects increase the volatility, dynamism and complexity of IoT environments, which will lead to new cyber-security challenges. Furthermore, they will enable new ways for compromising citizens’ privacy. Therefore, new ideas for safeguarding security and privacy in this emerging landscape will be needed.
  • New Standards and Regulations: A new regulatory environment will be needed, given that smart objects might be able to change the status of the physical environment leading to potential damage, losses and liabilities that do not exist nowadays. Likewise, new standards in areas such as safety, security and interoperability will be required.
  • Market Opportunities: AI and smart objects will offer unprecedented opportunities for new innovative applications and revenue streams. These will not be limited to giant vendors and service providers, but will extend to innovators and SMBs (Small Medium Businesses).

Future Outlook

AI is the cornerstone of next generation IoT applications, which will exhibit autonomous behavior and will be subject to decentralized control. These applications will be driven by advances in deep learning and neural networks, which will endow IoT systems with capabilities far beyond conventional data mining and IoT analytics. These trends will be propelled by several other technological advances, including Cyber-Physical Systems (CPS) and blockchain technologies. CPS systems represent a major class of smart objects, which will be increasingly used in industrial environments.

They are the foundation of the fourth industrial revolution through bridging physical processes with digital systems that control and manage industrial processes. Currently CPS systems feature limited intelligence, which is to be enhanced based on the advent and evolution of deep learning. On the other hand, blockchain technology (inspired by the popular Bitcoin cryptocurrency) can provide the means for managing interactions between smart objects, IoT platforms and other IT systems at scale. Blockchains can enable the establishment, auditing and execution of smart contracts between objects and IoT platforms, as a means of controlling the semi-autonomous behavior of the smart object.

This will be a preferred approach to managing smart objects, given that the latter belong to different administrative entities and should be able to interact directly in a scalable fashion, without a need to authenticating themselves against a trusted entity such as a centralized cloud platform.

In terms of possible applications the sky is the limit. AI will enable innovative IoT applications that will boost automation and productivity, while eliminating error prone processes.  Are you getting ready for the era of AI in IoT?

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