Trends Identified
The Rise of Roboadvisors & App-based Investments
Roboadvisors are viewed as one of the most disruptive of AI-influenced fintech services. Robo-advisors rely on technology, rather than a financial planner, to deliver financial advice at any time of the day.
2018
Top Tech trends 2018
Juniper Research
Facial Recognition Applications Surge
Facial Recognition solutions can be utilised across a host of arenas, from security to personalised marketing. Technology has the potential to deliver benefits ranging from frictionless authentication and payment to improved brand interaction.
2018
Top Tech trends 2018
Juniper Research
Edge Computing to Fast Track the IoT
‘Investment into Edge Computing or ‘Fog Computing’ will fast track the IoT (Internet of Things)’. Faster speeds and lower latency enabled by the placing ‘micro’ data centres at the edge of the mobile network. Will play a vital role in the delivery of IoT services, which require low latency or are data intensive. New architecture will necessitate new network security features at both network and device level.
2018
Top Tech trends 2018
Juniper Research
AI and Blockchain to Power Numerous Fintech & Insurance Solutions
2018 is the year the combination of blockchain and AI will impact upon areas outside of banking. Blockchain offers the possibility of carrying out transactions in transparent way, offering a simple platform for solutions outside of banking. These solutions will include: Money transfer and remittance. Insurance powered by smart contracts.
2018
Top Tech trends 2018
Juniper Research
Apple, Facebook, Google Bring Social Payments to the Masses
Increased competition from digital-only service providers offering no to lower transaction fees. 2018 is going to be the year for social payments: Key launches from Apple, Facebook, Google to drive P2P market.
2018
Top Tech trends 2018
Juniper Research
Amazon and Facebook Lead OTT Bids for Major Sporting Rights
Delivering attractive premium content is critical to the success of any video service. Acquiring exclusive rights to popular sporting events is a well- trodden path to increased viewing figures.
2018
Top Tech trends 2018
Juniper Research
Adversarial Machine Learning Becomes Key for Security & Fraud Prevention
Machine learning has had its advantage in effectively delivering rapid prediction of trends and establishing robust risk management and inference. Much investment, time and focus by organisations has been dedicated to programming and training machine learning algorithms to fulfil these functions. However, if these algorithms become compromised, they will be prone to attacks from threats and viruses. The chaotic damage that permeating cyberattacks have inflicted in algorithms can dangerously result in the misclassification/alteration of information within them; in effect an organisation’s entire system’s security can be at stake. Cybercriminals constantly seek to successfully exploit weaknesses of learning algorithms of highly valued organisations. Fraudsters are responding to the enhanced detection capabilities for transaction fraud and account fraud offered by fraud detection and prevention service providers. In some instances, they are also using machine learning algorithms to uncover weaknesses in fraud detection systems, in a type of machine learning chess match. It is here that the choice of FDP vendor becomes important, in terms of how its machine learning solution is implemented. Is a static model used, or does the vendor employ an adversarial model that adapts to changing conditions? Fraudsters will have very little knowledge of the precise algorithms being used to detect fraud. As a result, time, effort and funding must be sourced to identify weaknesses, which may then be applied or replicated across other merchants assumed to be using similar algorithms. Sectors (healthcare, industrial, advertising) where protection of huge amounts and types of sensitive data (e.g. consumer/public data) is a high priority – will be the drivers here. Spend on Fraud Detection & Prevention software in the financial sector, ie for eCommerce transactions including ticketing, money transfer and payments, will reach $10 billion by 2022. These sectors recognise the value of determining and containing susceptibilities (e.g. to detect unauthorised access points, weak security infrastructures, etc.) in machine learning approaches within adversarial circumstances. They will prioritise increasing their understanding of these vulnerabilities in machine learning algorithms. They’ll also engage with machine learning specialists to design and implement effective action steps to address these vulnerabilities. Juniper Research believes, moving forward into 2019 and beyond, that adversarial machine learning will be required by numerous industries to: · Identify weaknesses of machine learning algorithms during the learning and identification process · Enforce the protection and integrity and validity of data in these systems · Action steps in response to specific threats · Assess the potential damage of these threats · Programme algorithms to enhance their resistance to viruses · Eliminate the presence of opaque ‘black boxes’ · Grant adequate time to algorithm developers to invest into ‘breaking’ the efforts of cybercriminals to infect data Related Research: Online Payment Fraud: Emerging Threats, Segment Analysis & Market Forecasts 2018-2023.
2019
Top Tech trends 2019
Juniper Research
AI to Enhance EDGE Computing Power in IoT Systems
Over the past 12 to 18 months, the concept of EDGE computing to power the Internet of Things (IoT) applications, devices and systems surface has become a significant trend in the digital industry. The following factors have led to the a combination of EDGE Computing Power and IoT: · IT and technology providers, given their limitations, have had to decide how much processing power and computing resources to allocate from the cloud layer to the EDGE layer, particularly for applications that are highly Central Processing Unit (CPU) concentrated. · These providers aim to work alongside many sectors, industries, organisations to attain insightful analysis of specific, filtered data and nformation, via the use of machine learning and EDGE computing within IoT. · The collation of these significant amounts and types of intelligent data via smart sensors, actuators, servers, etc, for further analysis by EDGE and Cloud Analytics engines – instills confidence in decision makers to make the right moves. The aviation industry, for example, realises the power of the EDGE element and IoT together. By measuring and monitoring an aircraft’s performance, significant reductions in fuel and operational expenses and customer churn can be achieved. In 2017, SAS and Cisco announced the birth of their IoT Analytics platform, developed to enable analytics on devices at the edge of the network. Many industries see value in capturing and analysing data on the go, or in motion, rather than analyse data that was stored. Meanwhile, Huawei and Google have made efforts, in the last couple of years, to establish specific products to strengthen and enhance their IoT computing EDGE capabilities. In summary, the combination of AI/machine learning and EDGE within IoT systems allows IT and technology providers to: · Increase the running efficiency of their IoT operations via the gathering of intelligence from local data, particularly in locations where cloud connectivity is inconsistent. · As highlighted earlier, deliver real-time, fast predictions for critical IoT applications through machine learning processes that gather and process this data from devices and sensors. · Increase security of all types of sensor collected data via EDGE, with all privacy, security and compliance risks of these data fully eliminated. Juniper forecasts that the total number of connected IoT sensors and devices will exceed 50 billion by 2022, up from an estimated 21 billion in 2018. This growth, equivalent to 140% over the next 4 years, will be driven by EDGE computing services; increasing both deployment scalability and security. Incorporating powerful AI at the EDGE will enable faster processing, and analysis of IoT applications, and deliver improved data filtering, automation and workload distribution. We expect cloud corporates to potentially lead this space strongly, given their experience and background in AI. Related Research: The Internet of Things: Consumer, Industrial & Public Services 2018-2023
2019
Top Tech trends 2019
Juniper Research
Banking-as-a-Service Economy Emerges
Juniper expects the emergence of banking-as-a-service to be a key driving force in the world of finance in 2019. With PSD2 applicable as of January 2018, we expect 2019 to be the year that products based on the regulation’s open API requirements to have a significant effect on the market. Most particularly, this will enable new types of banking services to be offered by new players, who can handle the cross-platform requirements for banks unfamiliar with the technological requirements. The main opportunity here is for new technology-focused players to offer services across a wide range of new platforms. With compliance established in 2018, 2019 will see banks and technology players look beyond mere compliance and build on the opportunities the directive presents. The newer fintech start-ups stand to be the biggest beneficiaries of this development, as they can provide white-label services to a wide variety of financial institutions wanting to offer more services to customers. In time, this will extend to PaaS and SaaS providers more generally, as financial transactions becomes another form of secure data to transact through the cloud. As a result, we have seen Oracle among one of the early movers in this space; extending their cloud expertise into a new sector. This will also benefit banks, as it gives them more platforms through which to engage their customers. More types of product will give them a larger presence, and thus more ways to make their products more sticky to customers. However, the Open data requirements of PSD2 also means that consumers should benefit from increased competition as dedicated BaaS players emerge to serve customers, in the way that Monzo and other fintech companies have become prominent. Juniper expects that, while this movement has begun in Europe, later in 2019, other banking players will bring out BaaS services to customers. This will not be mandated, but a drive to increase competitive differentiation, and with the PSD2 as a de facto framework, a global BaaS market will form, although banks themselves will remain largely national. In tandem with this, we also expect IoT-based services to make an appearance in 2019. With several companies explicitly targeting this space already, it will become a key competitive benefit for several BaaS players. Related Research: Retail Banking: Digital Transformation & Disruptor Opportunities 2018-2022
2019
Top Tech trends 2019
Juniper Research
RCS Messaging to Contest Chatbots & OTT Business Platforms
RCS has undergone a significant transformation from previous iterations of the messaging technology. Support of RCS will continue to grow over 2019 – amongst both operators and handset vendors. At present, there are over 60 operators supporting the service globally – with more operators expected to announce their support in 2019. 2019 will shape up to be a crucial time for operators wishing to curb the migration of mobile messaging traffic to OTT messaging apps and chatbots. Accessibility and management of the service for brands will be provided by CPaaS vendors, leaving little to no investment needed into infrastructure from operators - just a flat fee to the CPaaS provider. Using RCS for business messaging, or application to person messaging, will provide new revenue streams if the technology is implemented correctly. As a result, this places increased pressure on these OTT messaging apps, their business platforms and in-app chatbots as brands gravitate to the superior capabilities and reach of RCS. As the reach of RCS increases so does the value proposition for its usage in contacting customers, thus increasing adoption of the service further. This will create a virtuous circle, in which the value of RCS only continues to increase. Thus, the timing of implementation is crucial, or operators risk losing A2P messaging traffic to other messaging services in addition to P2P traffic. We anticipate advertising and retail to be the first industries to adopt RCS messaging on a large scale; further encouraging a number of other industries to look into the service. Additionally, we can expect a large amount of focus of MWC 2019 to be on RCS, notably on the revenue potential that the technology will bring operators – and what role the technology will play in the wider CPaaS ecosystem. Related Research: Mobile Messaging: Operator Strategies & Vendor Opportunities 2018-2022
2019
Top Tech trends 2019
Juniper Research