Connect with us

Technology

EVOLUTION NOT REVOLUTION: HOW TECHNOLOGICAL DISRUPTION HAS FUELLED A ‘SHARING ECONOMY’ AND GIVEN CONSUMERS THEIR POWER BACK

Published

on

landbay

By Matt Ralph, Landbay

It seems every week we’re learning about new technologies or advances in existing technology that shaping the way we live. Take drones. Cast your mind back only a few years when the concept of Amazon’s drone parcel delivery service seemed like tech gone mad. Today we’re seeing drones at affordable prices propelling novel uses from scientific data collection to the birth of new competitive sports which see teenagers walking away with $250k prizes (yes, there is a Grand Prix of Drone Racing).

Similarly, technological disruption is already reshaping our economy and the consumption patterns of generations to come. Today’s innovations are radically altering industries such as transportation, travel and hospitality. Recently, fintech developments like peer-to-peer (P2P) lending are shaking up traditional finance sectors and it’s only a matter of time before technology driven disruption spreads to markets like housing, where demand already outstrips supply.

It’s no coincidence that technology is shaking up the finance industry. In the wake of the global financial crash of ‘07/’08 consumers began to dawn to the control unwittingly lost to the large corporations in whom they had placed significant trust for their homes, their income and their finances. As a result of this consumer awakening, digital disrupters like Airbnb and Uber launched tech based business models that hand control back to their customers,  with sharing and transparency at the core of their services; values that peer-to-peer lenders in the finance sector, such as Landbay, also share. P2P finance models harness technology to bring investors and borrowers closer together, removing layers of cost and in doing so also remove the opacity that can cover up poor lending practices.

It’s no surprise that consumers are adopting sharing economy practices with such ease, as it fits comfortably within our increasingly fragile financial states. Today, we’re faced with increasing costs of living, slow national salary growth (1) and soaring house prices that will keep many off the property ladder for years to come. A 2015 Bank of England survey revealed that just under half of all families who don’t already own their own home believe they will never get on the housing ladder (2). This represents around 4.5m households and compares with only 32% who said they were confident of buying.

Ultimately we’re left with a generation aptly dubbed ‘generation bail-out’ who are destined to depend on their parents far longer than their predecessors;  millennials with an asset-light stance on consumption who value experiences over possessions, obsessed with sharing them through social media for the world to see and critique. Generation bail-out is embracing a sharing economy with open arms and at a startling rate, something that can’t be ignored and neither can they.

So whether it’s opting for an Uber with strangers over buying a car, seeking communal housing rather than buying a homes or even checking into someone else’s home through Airbnb rather than a hotel, disruptive technology has us engaging in behaviours that would have seemed unthinkable as recently as five years ago. We have entered an era of Internet-enabled intimacy and it’s an era that is proving increasingly difficult to predict the future of. “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next 10,” a 1996 quote from Bill Gates , when Apple was still 5 years away from releasing their first iPod.

Based on the current evolution of technology enabled sharing across multiple industries, we’ve compiled our predictions for the next 10 years.

  1. We’ll see further banking competition from the fintech scene with new apps and online services, along with new competition from beyond financial services sector, with the likes of Amazon moving into the banking space.
  1. Banks will remain dominant as ‘financial advisors’, building on their historical legacy of being a trustworthy place for finances. However, they will begin to incorporate the innovations from the fintech quarter to improve their functions, either from internal development or acquisition.
  1. On-demand retail with companies offering new and improved access through delivery of goods i.e. Deliveroo and UberEats, also tapping into 3D printing for instant gratification of certain goods.
  1. The race to put driverless cars on the road will intensify, seeing Tesla, Google, Apple and Uber, along with many of the traditional car makers, publicly testing and launching the technology. However, the race for global scale launches will be limited by legislation not technology. Driverless technology will also begin to dominate public transport, seeing trains and the London underground shift away from human drivers.
  1. Networked car technologies will allow cars to communicate and inform amongst each other and road signs, which then will update information on upcoming traffic issues for safer and shorter journey.
  1. Not since the Concorde launched in 1976 (and retired in 2003) have we seen supersonic air travel be available commercially. We’ll finally see supersonic planes return to the skies, drastically reducing transcontinental travel time, with London to New York reduced to 3 hours, and at a more affordable cost to consumers.
  1. Virtual reality and immersive 3D experiences will be used to bring accommodation and other holiday services to life to help people make the best choices for the taste and budgets
  1. Cloud based work environments will shift the emphasis from email communication to real time real-time project collaboration. Time will be spent more productively on the actual project than on lengthy written planning communication.
  1. The home will become a much more important place for well-being, supported by interconnected technology like smart mirrors that integrate with selected apps and other home appliances to track your daily habits, offering lifestyle recommendations each morning.
  1. By scanning the barcodes and weights of the items placed within them, fully integrated fridge freezers will be able to notify you when they’re running low on the necessities and will sync with your online shopping basket to replenish them at your command.

References

  1. http://www.theguardian.com/business/2015/dec/16/uk-jobs-data-pay-growth-slows-to-2-percent
  1. http://www.telegraph.co.uk/finance/property/house-prices/12057861/Millions-give-up-on-home-ownership-as-house-prices-soar.html

Technology

The Coming AI Revolution

Published

on

The Coming AI Revolution 1

By H.P Bunaes, CEO and founder of AI Powered Banking.

There is a revolution in AI coming and it’s going to render legacy data and model governance practices obsolete.

The revolution will manifest in three ways:

  • Automated machine learning platforms like DataRobot, H2O.ai, Dataiku, and rapidminer are making data scientists more productive. A lot more productive. One company told me that they were seeing 7x as many models from their data science group shortly after the implementation of a leading autoML platform. The increase in model output will quickly reveal bottlenecks in model validation, production implementation, and model operation and management.
  • The increasing popularity of tools aimed at “citizen data scientists”, local data literate subject matter experts in the business without formal data science training who nevertheless know a good model and a good use case when they see it, will turn a large percentage of technically savvy business people into model developers. Models developed by citizen data scientists will quickly dwarf the volume of models created by formal data science organizations adding further strain on existing procedures and revealing gaps in governance.
  • Availability of nearly unlimited capacity on demand for both data storage and computing power from cloud providers will lead to the proliferation of sophisticated predictive models that can learn from broad swaths of data; structured (your existing databases, for example), semi-structured (your documents), and even unstructured (such as images), sniffing out the data that is relevant to any one particular prediction or population. Demand for more, and different kinds of data for modeling, and the need to integrate model results into downstream dataflows and IT applications, will make data platforms and data flows significantly more complex, harder to manage, and increase points of failure.

What this all adds up to is an explosion in the volume of predictive models and of the data in motion in your organization. Where there were no models, there will suddenly be many. Where there was one model, you may find there are now hundreds. And the pipes providing data into and delivering results out of these models are going to proliferate. Operational and reputational risk from model failure will rise significantly as companies outgrow their existing data and model governance frameworks and legacy procedures.

Making this worse, many banks are starting from a weak position. The demand for more and better models (descriptive and predictive) has already led to a thicket of overlapping, partially inconsistent data flows to a multitude of models. Model outputs themselves have become part of the data flow to downstream data marts, BI, apps and even to other models as inputs. It is the rare organization that knows where all that data is coming from, where it is going, how it is being used, and can identify the potential impacts of changes to data and to the models that consume it.

Certainly there has been much improvement in recent years in data governance at most large organizations. Data quality, data standards, data integration, and data accessibility on robust platforms (increasingly cloud based) have all gotten better. And most organizations now have robust model risk management practices in place, to test and validate models before they go into production use.

But these worlds are about to collide. Data and analytics, once distinct and manageable separately are going to become inextricably intertwined. As brilliantly explained in a paper by several smart people at Google (“The Hidden Technical Debt in Machine Learning Systems”),​ we will rapidly reach the point where “changing anything changes everything.”

Take a simple example, what differentiates data on a client from a CRM system from data on a client created by a predictive model? The answer: nothing. Yet they are managed today by different groups. The former is typically managed by Data Governance, which is usually led by the Chief Data Officer. The latter is usually the province of Model Risk Management often found in the Corporate Risk Management organization.

But when model outputs become inputs to reports, to business processes, to critical operational or client facing systems, or to other models, they need to be governed just like any other data.

The perfect illustration of this challenge is in change management. Often you will find data change management in the chief data officer’s organization and model change management in the model risk organization. But changes in the data can, and often do, effect models in sometimes unpredictable fashion. And changes to models can change outputs and have major impacts to downstream consumers of those results if they are not prepared for the coming changes.

Managing them separately and distinctly will therefore no longer be sufficient. How to tackle this?

  • First and foremost, you must have a complete catalog of all models including metadata describing model inputs and their source and model outputs along with their destination and uses. There are a number of solutions now coming on the market for this purpose including Verta.ai, ModelOp, and Algorithmia.
  • Second, data management needs to expand to include not only source data but also all the results (predictions, descriptions) produced by models.
  • Third, model management too needs to expand its remit, not just focusing on model testing and validation prior to model implementation but also monitoring model performance and managing model changes after the fact​ ​.
  • Fourth there must be formal procedures for keeping model management and data management mutually informed and closely coordinated. Data cannot change without assessing model impact, and models cannot change without assessing data impact.

Organizationally, it may be infeasible to combine legacy organizations across traditional lines of responsibility. And it may be better to leverage existing expertise across model management, data engineering, data management, and IT. But a new partnership model, new tools, and new procedures will be needed.

The explosion in AI is upon us. To use AI safely and effectively you need to get your data and analytics house in order and make sure the right mechanisms are in place to keep it so. Regulators have taken note of the risks of poorly managed AI, and it is only a matter of time before they dictate minimum standards. Combining, or at least tightly coupling, data and model governance is where to start.

Continue Reading

Technology

How financial services organisations are using data to underpin future growth

Published

on

How financial services organisations are using data to underpin future growth 2

By John O’Keeffe, Director of Looker EMEA at Google Cloud

In addition to the turmoil caused by the COVID-19 pandemic, a significant decline in venture capital investment has left many financial services organisations feeling deflated, with others struggling to survive. According to figures from trade body Innovate Finance, investment in UK fintech organisations fell 30% in Q2 of this year, with smaller challenger firms and start-ups being the most profoundly hit by our current economic problems.

As a result, both challenger banks and more established players have had to pivot their strategies in order to maintain relevance and market share. Nonetheless, the outlook for fintech in the UK and further afield looks promising for the future. The reality of spending much of our time at home, and out of reach of brick and mortar services, means that many of us are becoming even more accustomed to digital banking for example. Recent analysis of finance application usage from Adjust, found that the average sessions in investment apps surged 88% globally, while payment and banking app sessions increased by 49% and 26%, respectively, during the COVID-19 pandemic.

However, the fact remains that investment in the sector is currently hard to come by. To help regain momentum, a review into the UK’s fintech industry was launched to identify opportunities to support growth across the industry. Data has – and will continue to – play a key role in this push for innovation, helping organisations spot gaps in the market, predict customer behaviours and ensure that the decisions they make are based on real insights. At such a critical time, enabling a data-led approach will help organisations ascertain exactly what is required to accelerate change and ensure the sustainability of the industry.

The financial services industry is a data-rich environment, giving organisations a potential goldmine of customer interactions, product performance and market trends. However, the difficulty often lies in bringing this into a coherent whole, and extracting the business insights required for long-term success. This is as much about strategy and accessibility as it is about technology. Fostering a true “data culture” where employees across the business, whether data experts or not, can access real-time intelligence that informs their day-to-day decision making in a positive way, is crucial. This may mean tweaking your onboarding and training programmes, identifying data evangelists that can catalyse others, or simply making data engaging and relatable for those who are new to the practice.

For many organisations, data is often stored within traditional business intelligence tools, third-party SQL clients or even just a simple spreadsheet, meaning that valuable data insights are siloed and often hindered by a bottleneck between a stretched analytics team and the rest of the business. There is also the all-important General Data Protection Regulation (GDPR) to consider, so data governance and having a clear view of where data is being housed, and for what purpose, is particularly pivotal.

With this in mind, it is crucial to have a “single source of truth” to bring various data streams together and enable real-time, self-serve insights to your whole employee base. As an example of this in practice, data is a great way to understand your existing clients more intimately and nip any problems in the bud early. By building a custom data dashboard incorporating, for example, number of support tickets issued, change in ticket sentiment and number of days to renewal, you can build up an accurate picture of account health and how this has changed over time. In combination with real-time metrics on which products and features are being used and how, sales teams can have more meaningful and accurate conversations with their customers, converting at-risk accounts into potential growth opportunities.

Given the dip in VC investment mentioned earlier, it is more important than ever for startups and scale-ups to do more with less and set a strategic roadmap that supports rapid growth. By using data to measure and action customer feedback, these organisations can be more agile in taking new products to market and making sure these are useful and address specific pain points.

Whether a fintech scale-up or an established name, it has never been more important to shift your operations to a more data-led strategy. With an uncertain outlook ahead for business across all sectors, making data the “single source of truth” can help to navigate market trends, identify new growth opportunities and simply make an organisation’s decision-making smarter and more efficient. Through data-driven innovation and growth, one of Britain’s most valuable industries can continue to thrive in the future.

Continue Reading

Technology

The Bank of England partners with Appvia to assist in the design, construction and assurance of a new cloud environment

Published

on

The Bank of England partners with Appvia to assist in the design, construction and assurance of a new cloud environment 3

The Bank of England has appointed self-service cloud-native delivery platform Appvia to support the creation of a new cloud environment.

The announcement follows a public procurement process which commenced in January 2020. The Bank of England will work with Appvia on design, construction and assurance of a modern, fit for purpose cloud environment.

During the two-year partnership, Appvia will be supporting development and project teams within the Bank in testing and deploying code in cloud environments, working with security teams to integrate the cloud into existing operational and security processes; and implementing information governance compliance so staff are able to collaborate safely and securely.

Oliver Tweedie, Head of Digital Platforms at the Bank of England, said, “We have selected Appvia as our Cloud Delivery Partner to help us realise the Bank’s cloud ambitions and unlock the potential of the Cloud. Appvia come with a great pedigree and a wealth of experience delivering Cloud services within government.  Working in collaboration with Bank Technology teams, Appvia will help us shape and build the future of Cloud services across our organisation – a key part of our Technology strategy.”

Jon Shanks, CEO and Co-Founder of Appvia, said, “This is an exciting opportunity to work with the Bank as it undergoes a step-change in its approach to the cloud. Harnessing innovative cloud solutions, such as containers and Kubernetes is a real business enabler for the Bank to streamline the software development lifecycle, ways of working and cloud operating model. We look forward to working with all stakeholders at the Bank of England to support its digital transformation journey.”

Appvia, which counts the Home Office among its major clients, is a self-service platform that enables organisations to scale their infrastructure quickly, securely and easily using services such as Kubernetes. In September, Appvia launched the world’s first developer-centric tool to enable teams to predict and control cloud costs.

Continue Reading
Editorial & Advertiser disclosureOur website provides you with information, news, press releases, Opinion and advertorials on various financial products and services. This is not to be considered as financial advice and should be considered only for information purposes. We cannot guarantee the accuracy or applicability of any information provided with respect to your individual or personal circumstances. Please seek Professional advice from a qualified professional before making any financial decisions. We link to various third party websites, affiliate sales networks, and may link to our advertising partners websites. Though we are tied up with various advertising and affiliate networks, this does not affect our analysis or opinion. When you view or click on certain links available on our articles, our partners may compensate us for displaying the content to you, or make a purchase or fill a form. This will not incur any additional charges to you. To make things simpler for you to identity or distinguish sponsored articles or links, you may consider all articles or links hosted on our site as a partner endorsed link.

Call For Entries

Global Banking and Finance Review Awards Nominations 2020
2020 Global Banking & Finance Awards now open. Click Here

Latest Articles

The Coming AI Revolution 4 The Coming AI Revolution 5
Technology11 hours ago

The Coming AI Revolution

By H.P Bunaes, CEO and founder of AI Powered Banking. There is a revolution in AI coming and it’s going...

Q&A with Joe Steele, Head of Workplace Technology at Starling Bank 6 Q&A with Joe Steele, Head of Workplace Technology at Starling Bank 7
Interviews16 hours ago

Q&A with Joe Steele, Head of Workplace Technology at Starling Bank

In just under a year, many businesses had no choice but to go online and with digital transformation on the rise...

How financial services organisations are using data to underpin future growth 8 How financial services organisations are using data to underpin future growth 9
Technology17 hours ago

How financial services organisations are using data to underpin future growth

By John O’Keeffe, Director of Looker EMEA at Google Cloud In addition to the turmoil caused by the COVID-19 pandemic, a...

Three questions the financial services industry must answer in 2021 10 Three questions the financial services industry must answer in 2021 11
Top Stories22 hours ago

Three questions the financial services industry must answer in 2021

Xformative, a Mastercard Start Path recipient, shares what these questions mean for fintech partners and their innovations This year, fintechs...

A quarter of banking customers noted an improvement in customer service over lockdown, research shows 12 A quarter of banking customers noted an improvement in customer service over lockdown, research shows 13
Banking22 hours ago

A quarter of banking customers noted an improvement in customer service over lockdown, research shows

SAS research reveals that banks offered an improved customer experience during lockdown A quarter (27%) of banking customers noted an...

Is Digital Transformation the Key to Business Survival in the New World? 14 Is Digital Transformation the Key to Business Survival in the New World? 15
Business22 hours ago

Is Digital Transformation the Key to Business Survival in the New World?

After a turbulent year, enterprises are returning to the prospect of a new world following an unprecedented pandemic. Around the...

Virtual communications: How to handle difficult workplace conversations online 16 Virtual communications: How to handle difficult workplace conversations online 17
Business22 hours ago

Virtual communications: How to handle difficult workplace conversations online

Have potentially difficult conversation at work, like discussing a pay rise, explaining deadline delays or going through performance reviews are...

Black Friday payment data reveals rapid growth of ‘pay later’ methods like Klarna 18 Black Friday payment data reveals rapid growth of ‘pay later’ methods like Klarna 19
Finance22 hours ago

Black Friday payment data reveals rapid growth of ‘pay later’ methods like Klarna

Payment processor Mollie reveals the most popular payment methods for Black Friday Mollie, one of the fastest-growing payment service providers,...

Brand guidelines: the antidote to your business’ identity crisis 20 Brand guidelines: the antidote to your business’ identity crisis 21
Business24 hours ago

Brand guidelines: the antidote to your business’ identity crisis

By Andrew Johnson, Creative Director and Co-Founder. How well do you really know your business? Do you know which derivative of your...

COVID-19 creates long and winding road for startups seeking investment 22 COVID-19 creates long and winding road for startups seeking investment 23
Investing1 day ago

COVID-19 creates long and winding road for startups seeking investment

By Jayne Chan, Head of StartmeupHK, Invest Hong Kong Countless technology and other companies describe themselves as innovators, disruptors or...

Newsletters with Secrets & Analysis. Subscribe Now