In an exclusive CNBC interview, Jack Ma, Alibaba executive chairman, talks to CNBC's David Faber about artificial intelligence and employment.

Analytic software firm FICO recently released an interactive map of European card fraud, which shows that card fraud losses for 19 European countries hit approximately €1.8 billion, a new high. The UK saw the highest losses at £618 million, a 9% rise over 2015, topping the previous peak in card fraud, set in 2008 after the introduction of chip and PIN.

Card not present (CNP) fraud has gone from 50% of gross fraud losses in 2008 to 70% in 2016. Ten countries saw an increase in fraud losses, while eight saw a decrease. The map is based on data from Euromonitor International, with additional information from the UK Cards Association.

“The growth in online spending and CNP fraud brings new challenges for banks and retailers, as criminals thwarted by chip & PIN have moved to a less risky channel,” said Martin Warwick, senior consultant for fraud at FICO. “Hiding amongst the growth in online purchases is great from a criminal point of view, but finding and stopping fraudulent transactions just gets tougher. Spotting the ‘needle in a haystack’ requires new behavioural analytics and artificial intelligence, combined with enhanced information from outside the traditional data contained within a purchase.”

In 2015 the UK’s card fraud rise was the highest in Europe, but in 2016 two countries saw higher rises — Poland (+10%) and Sweden (+18%). The UK’s rise from 2015 to 2016 was just half of that from 2014 to 2015.

France had the highest basis points at 8.9 (ratio of fraud losses to sales) among the 19 European countries, compared to 7 basis points for the UK. However, French card spending is half that in UK, making UK losses much greater. Together, the UK and France account for 73% of the total loses among the 19 countries in 2016, followed by Germany, Spain, Russia, Italy and Sweden.

Fighting Back with AI

FICO is working with banks to advance the use of machine learning and artificial intelligence to identify fraud faster. The key, Warwick says, is to spot anomalies without putting friction into the transaction.

“It’s no longer just about identifying patterns that are unusual for the customer — we’re also looking at anomalies at the mobile device, IP address and merchant level,” said Scott Zoldi, FICO chief analytics officer. “All of these have ‘behaviors’ just as individuals do, and we’re using our 25 years of experience in artificial intelligence to identify those.”

Mobile analytics is an important area here, said Zoldi, who developed or co-developed half of the company’s 70 patents in artificial intelligence and machine learning. “FICO has developed archetype analytics that taps into the rich source of mobile context such as advanced geolocation, allowing us to use that information in FICO Falcon Fraud manager to make real-time decisions during a transaction,” Zoldi said. “These analytics draw on our patented work with customer behaviour archetypes.”

Banks and card issuers are also beginning to step up their use of real-time customer communication. “Contacting consumers early using automated two-way SMS is a key solution to making sure the transactions are valid,” Warwick said. “If this is fully automated and tied into the fraud solution — as it is with FICO Customer Communication Services and the FICO Falcon Platform — then cases can be closed without human intervention and consumers can be allowed to continue to spend when and where they want.”

(Source: FICO)

Technology is often remarked as evolutionary ammo, and the statement stands just the same for the growth of businesses. Finance Monthly below hears from Frédéric Dupont-Aldiolan, VP Professional Services at Sidetrade on the latest and upcoming innovations that have hit 2017 hard.

Artificial intelligence, robotics, machine learning and the Internet of Things: 2016 stood out as a year marked by technological development and significant advances in several fields, not least that of connected, driverless cars. Against this backdrop, a clear trend is appearing: the growing influence of robotic technology in daily life.

In 2017, we have seen more promising innovations, here is my review of the top five things we are seeing:

5. IoT, the Internet of Things

Star of the Consumer Electronic Show (CES), which took place in Las Vegas in January, and Viva Technology, which took place in Paris, the Internet of Things was thrust into the spotlight in 2016 and continues to bring increasingly intelligent connectivity to our daily lives. Smart devices, equipped with bar codes, RFID chips, beacons or sensors, are taking the lead and enabling companies to gain greater visibility over their transactions, staff and assets.

In 2016, information and technology research and advisory company Gartner estimated that there were 6.4 billion connected devices globally, an increase of 30% on 2015. By 2020, this figure is likely to have grown to 20.8 billion.

4. The explosion of Big Data

Network multiplication brings with it a proliferation of data generation, whose analysis, use and governance have become a burning issue. According to estimates by IDC, an international provider of market intelligence for information technology, by 2020, every connected person will generate 1.7MB of new data per second.

The concept of ‘perishable data’ has lost validity. In 2017, companies now have the capability to use data before it becomes obsolete. Devices connected via the Internet of Things will rapidly speed up data decoding and processing for actionable insight.

3. The ramp up of artificial intelligence and automatisation

Artificial intelligence has been one of the main talking points in technology over the last year. Encompassing areas such as machine learning, robotic intelligence, neural networks and cognitive computing, it’s now in daily use in numerous forms including facial and voice recognition, endowing velocity, variety and volume.

This year, artificial intelligence has taken on an increasing number of repetitive and automatable tasks, beginning with wider use of ‘chatbots’ with the capacity to give coherent, easily formulated responses. IDC pinpoints robotics driven by artificial intelligence as one of the six innovation accelerators destined to play a major role in the digitalisation of society and the opening up of new income streams. Indeed, Amazon and DHL are already making use of warehouse handling robots.

2. Location technology, the Holy Grail of customer satisfaction

Location technology has taken great strides over the last year or so, to the marked benefit of customer satisfaction in the hotel, health and manufacturing sectors. Customers can now receive geo-targeted offers on their smartphones, for example for promotions or reductions, depending on their physical location.

In 2017, RFID chips enable yet more accurate tracking of customers and enhancement of their buying experiences.

1. Virtual reality makes way for augmented reality

One of the biggest innovations recently has been virtual reality, and with it came much media coverage too. From Facebook to Sony, Google to Microsoft, big brands grasped this new technology to offer an outstanding user experience, through the merging of virtual and real imagery.

In 2017, these virtual devices have acquired an awareness of their environment and give users a real sense of immersion of the digital environment from within their own homes. The potential of augmented reality for business will be harnessed too in the coming months. Some companies, among them BMQ and Boeing, are already employing it to increase their retention and productivity rates, or to provide training to their workforces across worldwide subsidiaries.

Over the next few months, as we gear up for another round of product launches, we should expect to see advancements in these key areas of technological innovation. Within business, this technology should help to improve customer service by streamlining production and processes, saving time and money, as well as providing new and exciting ways to reach and engage with customers, helping to retain existing clients as well as bring in many new ones.

By Bhupender Singh, CEO of Intelenet Global Services

hupender Singh, CEO of Intelenet Global Services, says AI and automation is already redefining the role of finance executives towards more strategic roles.

Digital technology is revamping business models to help organisations respond to rapidly changing market conditions. This has also impacted the finance and accounting function, where automation and AI is being used to transform business critical functions. The evolution of technology has redefined the role of finance executives from carrying out low-level tasks towards more strategic decision making. Many CFOs and financial executives are progressively turning to next generation tools to manage their workflow efficiency and encourage better governance when it comes to managing their finances.

These tools are designed to purposefully enhance business outcomes whilst meeting all operational business requirements. Bearing this in mind, a business’s accounting system is a vital component to ensuring profits are being maximised. Working from a system which encourages a more integrated and comprehensive overview of a business’s finance can help boost operational efficiency.

Businesses partner with multiple suppliers, vendors and companies to support them deliver their services to customers. Turning away from a culture of late payments and pushing towards invoices being processed on time, will ensure service is well-maintained with supporting partners. This is why, the finance function needs a robust system which bridges the gap across information from both internal and external stakeholders.

Working from a single interface with all the financial data in one place, will extend access to key information amongst finance teams to ensure a more collaborative approach when it comes to planning and forecasting. Consequently, finance executives will be able to map out a business’s strategic financial objectives to make the planning, forecasting and budget processes smoother. This will allow businesses to have a better understanding of their cash flow across different areas. Pushing for more financial transparency allows finance executives to have a more holistic overview of their expenditure.

Automation continues to displace manual work, eliminating the headache from finance executives to undertake repetitive and mundane tasks such as - contract rate disagreement, missing payments and remittance advice to improve productivity. Data-entry and coding are time intensive, and so automation can unlock value for businesses as finance executives are able to redirect their attention towards better insight and growth strategies. Rather than trawling through spreadsheets they are playing a pivotal role in steering the direction of the company.

Accounting errors that affect the balance sheet can also result in hefty fines and retail losses, making it vital to avoid financial and reputational risk. As a business grows and expands, working with multiple partners, it depends on their processers keeping pace with the financial management of ongoing operations. Automation can reduce retail losses by 40% and have in one case recovered £1.7 million as a result of missing/ incorrect documents.

Data Analytics has always been aligned with better understanding of customer needs, shopping habits, and preferences. However, there is also a lot to be said on how this hard data can be used to help executives gain valuable insight to improve performance, identify growth trends, and manage errors.  An automated, real-time supply of financial data across different processers, such as invoicing & billing administration, tax filing and payroll processing endorse better governance and compliance across these areas.

There is a lot of emphasis on innovation in the area. However, in order to optimize the full potential of these technologies, requires a high level of digital expertise and skill to process the financial data and streamline the financial management process to enhance the service delivery.

Website: https://www.intelenetglobal.com/

Artificial intelligence is shaping the future of retail. Smart algorithms and data analyses are creating sustainable performance benefits across all levels of the retail supply chain.

With its Omnichannel ePOS Suite, Wirecard AG is the first payment provider to offer a fully integrated solution for self-learning analyses based on payment data in combination with other data sources. The evaluations substantially support e-commerce and high-street retail in implementing the following central growth concepts: increasing customer conversion, reducing customer attrition rates, predicting future consumer behaviour and linking points of sale with e-commerce.

Jörn Leogrande, Executive Vice President Mobile Services at Wirecard: "Using our data evaluations and analyses, merchants can increase their metrics in important performance areas. Our previous experience has shown that sales increases in the double-digit percent range are realistic."

Wirecard's turnkey solution generates insights into customer segmentation and cohort analyses, for instance, to optimise marketing efficiency. This revolves around the concept of a data-supported, real-time view of a retailer's customer behaviour in its entirety and increasing the customer lifetime value - optimal customer retention.

Insights into customer attrition (otherwise known as customer churn) behaviour are another unique selling point of the Omnichannel ePOS Suite. Complex evaluations enable merchants to identify customers who may potentially shop elsewhere. By introducing appropriate marketing measures, the churn rate can be significantly reduced.

Analyses on anomalies, trends and sentiment, peak detection and time series based on country-specific data as well as cohort analyses to assess the efficacy of marketing measures are additional beneficial tools. The Omnichannel ePOS Suite can be used in pre-existing systems without incurring large expenses.

Markus Braun, CEO of Wirecard: "The Omnichannel ePOS Suite is the first step towards large-scale digital transformation in the retail sector. Over the next few years, data analyses using artificial intelligence and machine learning will play an increasingly important role in their business area. Based on our analyses, we are able to reduce risks and increase the chances of success for our partners. This means that all parties involved can gain a significant competitive advantage, which is why the omnichannel ePOS suite marks a decisive step for the future of payments."

(Source: Wirecard)

The robotic revolution is set to cause the biggest transformation in the world’s workforce since the industrial revolution. In fact, research suggests that over 30% of jobs in Britain are under threat from breakthroughs in artificial intelligence. Thanks to advances in technology, many jobs that weren’t considered ripe for automation suddenly are. Is your job next? Find out how many jobs per sector, are at high risk of being taken by robots by 2030.

(Source: RS Components)

Utter the words ‘disruption’ and ‘financial services’ and your thoughts will be drawn to the bevy of technologies that were supposed to transform the sector. Artificial Intelligence (AI) and Blockchain are the most recent additions to the list, but this time around, they probably have the potential to drive real structural change. To explain their potential and differences, Grant Thomas, Head of Practices at BJSS talks to Finance Monthly about the impact of these technology disruptions.

Blockchain, which was originally developed to support Bitcoin and other cryptocurrencies, is being heralded by the Financial Services industry as the next big thing because it supports peer-to-peer mass collaboration which could make many of the traditional organisational forms redundant.

In theory, Blockchain will reduce transaction costs – Santander expects to achieve savings of around $20 billion a year – so while the industry is still largely unclear on how it should be applied, there is a race to productionise it. Heavy Research and Development investments are being made.

The problem with Blockchain in the Financial Services industry is that it is largely pie in the sky. Its development landscape is being driven by a handful of large multinational organisations, mostly working as consortia, because they’re the only players able to handle its scale and apply the multi-jurisdictional experience the project needs. Open Source projects such as Openchain and Hyperledger are not sufficiently developed to offer a credible alternative. There is also a shortage of skilled talent available to build applications, or subject matter experts available to develop and validate business use cases.

AI on the other hand is far more mainstream. Companies such as Facebook, Google, Viv, and Nuance already provide frameworks and turnkey solutions, and AI technology is already being used by many Financial Services providers to handle everything from detecting fraud, to market regulation and customer interaction. The Royal Bank of Scotland, for example, has recently completed a trial of a ‘Luvo’ AI customer service representative to support internal customer-facing staff.

AI is capable of processing data to make decisions far more efficiently and accurately than humans can. It does this through self-learning to solve cognitive tasks. The technology crunches historical data and teaches itself to act based on the decisions that have been previously taken by humans. It also learns from its mistakes - so every time AI completes a transaction, it becomes more accurate.

The ‘disruption’ from AI comes from the efficiency savings that Financial Services providers will achieve by automating the highly-transactional jobs that are usually handled by humans. This will improve customer service quality and consistency and will improve both regulatory compliance and risk management. When they deploy AI tools such as IBM Watson, Financial Services organisations have both cost-cutting and customer satisfaction in mind.

The barrier to entry for AI is far lower than it is for Blockchain. There is ready access to experience, talent, and a burgeoning ecosystem to sustain innovation. That said, Financial Services providers should consider these five steps to ensure that their AI deployments succeed:

1. It’s mostly about the data

Banks have large IT estates which generate a great deal of data – everything from customer demographics, to product adoption and market trends. There isn’t necessarily a requirement to collate data into a centralised data lake, but integration is important. Access to a self-service data model will allow, with minimum viable process, easy access to this data. Bear this in mind because providing as much data as possible is integral to the success of an AI deployment.

2. Begin at the end

Look at the ideal scenario. Consider the outcomes that are to be achieved and reflect on the experience the user should have. Develop personas to keep users in mind, build models to ensure that business outcomes are being achieved. And only then, start to build the AI.

3. It’s an elephant. Eat it slowly.

AI is huge. With an array of use cases as diverse as risk and fraud detection, customer relationship management, business development and cost reduction, AI is becoming increasingly important for financial services firms to remain competitive.

Don’t be tempted to tackle everything at the same time. When deciding which use case to start with, choose the lowest hanging fruit, build the AI, deploy and learn from it, and then finally, tweak it. Once this cycle is complete, move on to the next use case, applying the lessons learnt.

4. Experiment in the Lab before moving to the outside world

Some organisations embrace the concept of Innovation Labs to generate new ideas for products and services. Others routinely use Labs as part of their project delivery objectives. Whichever way innovation is achieved, it is important to have a process, the right behaviours and lean thinking.

For AI, a lab provides a safe space for expose data, to apply simulations, to learn and to experiment with configuration tweaks.

5. It’s not a project, it’s a journey

The project doesn’t end when the AI is commissioned – it continues.

A key part of disruption is the feedback loop. With the technology evolving quickly, this feedback mechanism should result in minor corrections being deployed quickly, while improvements are continuously implemented.

Written by Gavriel Merkado, REalyse

In innumerable ways, technology has shifted the power paradigm from the elite to the many and has empowered investors to make better investment decisions. Big data and smart algorithms mean more information, quicker, which ultimately leads to less risk for investors.

Illiquid vs liquid assets

The enabling power of technology is particularly true for liquid assets such as stocks and bonds. Advancements in financial markets technology and data systems have led to transactions become safer, cheaper and faster. Technology has provided a more systematic approach to investment compared to previous methods based more on human instinct and intuition.

Utilising technology to make better investment decisions around illiquid assets is more complex. It’s harder to track the value of illiquid assets like collectibles, art and property due to a lack of transparency around pricings. While these assets have previously had to substitute speed for clarity, there’s now a growing trend of using technology to combine the two to help produce better returns.

Property investment

Prop tech is one area that is enabling better investment decisions. At REalyse we empower property developers to efficiently make informed decisions about where, when and what to invest in. Using big data analysis REalyse tackles the unpredictable element of the market, allowing users to anticipate potential risks, while also cutting the time spent on weeks of personal market research.

Peer-to-Peer (P2P) platforms such as LendInvest have established a marketplace for investors to find and invest in new and existing property loans. By removing the need for a mortgage, these platforms help investors remain in control of how much they invest in conjunction with other investors. Similarly, companies, such as Yielders, have created opportunities for people to invest in property with shares as little as £100, permitting amateur investors to become equity owners.

Art/collectibles investments

Another example of tech enabling better illiquid investments is Arthena. This platform enables investors to make better decisions around the value of a piece of art. By taking into account the artist’s career, the year the piece was created and an analysis of art auction results, it helps to predict the risk and return on investment.

Financial investments

In the financial world, the rise of robo-advisors has given investors the opportunity to consult detailed data and information before making decisions at the touch of a button. Industry leaders, such as Nutmeg and UBS SmartWealth have provided their customers with around the clock access to their investments, giving them full clarity and transparency without having to consult a wealth advisor during working hours.

Blockchain has made dramatic steps in transforming the investment industry. Forbes reported last year that it could be Wall Street’s most game changing technology advance since the internet. In a highly regulated industry such as investment, Blockchain provides a transparent way to digitally track the ownership of assets before, during and after transactions, and it has the potential to transform everything from how stock exchanges operate to how proxies are voted.

Allocator is another tool transforming the investment management industry by streamlining the process to access the information investors need, in real time and in a format that’s useful. Fund managers control who they share information with and what exactly each investor can access.

Hedge funds are also now incorporating artificial intelligence to give investors quick, accurate and transparent data. An AI fund, such as Emma AI, is designed to operate autonomously in context of wealth management, financial analysis and research.

Conclusion

More than ever before, technology is empowering the investor to take control of their assets. From property to finance across liquid and illiquid assets it is transforming investment decisions.  As we enter the dawn of AI and machine learning, the technology will only get smarter, more intuitive and more effective. Being a technology innovator in the investment sector is a very exciting place to be right now.

Website: https://realyse.com/

The latest market report from technology M&A advisory firm, Hampleton Partners, reveals a reduction in fintech transaction volume in 2016 whilst overall transaction value remained stable as early hype has been replaced with cautious investment in proven and more established technologies and businesses.

The Fintech M&A report, which covers mergers and acquisitions in the period between July 2014 and December 2016, shows deal values for the first half of 2016 were down 32% from the previous half year. With investors increasingly prioritising profitability and resilient business models, EBITDA multiples fell to 15.0x compared with 15.4x in the previous half-year, while revenue multiples through 2H 2016 also dipped to a four-year low of 2.2x.

Top acquirers
Enterprise financial software companies accounted for 46% of the deal count on the trailing 30-month period, with a total of 689 deals completed.

Broadridge was the top acquirer, buying eight 8 businesses, its most recent acquisitions being investment advisor compensation firm M&O Systems, brokerage and shareholder communications business INVeSHARE and outsourced customer communications company, DST Systems.

SS&C, ICE and IHS Markit came in second place, acquiring six entities each. Other active acquirers included IRESS, Accenture, Envestnet and Digital Asset Holdings.

Search for scale and global consolidation
Deals were driven by acquirers looking to build scale, as well as the opportunity to enhance or replace in-house legacy systems. Hampleton also believes that CBOE Holdings’ $3.2 billion offer for Bats is the latest sign of a push towards global consolidation in the exchanges sector.

Enterprise resource planning and front-to-back office management solutions were particularly sought after. Meanwhile, the growing adoption of cloud and mobile services prompted established players such as SSC&C and Fiserv to buy digital solutions that either complement their existing portfolios or replace them entirely.

Miro Parizek, Hampleton managing partner, says: “Going forward, Hampleton believes that the Fintech M&A marketplace will remain consistent, continuing to deliver attractive multiples for sellers. Despite wider concerns surrounding Brexit and other geopolitical issues, London will remain an investment hotspot for fintech assets with investment activity driven by the three forces of consolidation, compliance and disruption.”

Blockchain and AI
Jonathan Simnett, Hampleton sector principal, adds: “Despite the market focus on mature technologies during 2016, Hampleton expects to see strong demand for blockchain companies and increased interest in artificial intelligence (AI) applications in the coming months as the technologies move to being a key area of focus in financial services. Disruptive alternative payment and lending services will also continue to thrive, attracting more interest from technology majors such as Apple and Google.”

(Source: Hampleton)

Deloitte explores the rapid evolution of business technology in its eighth annual technology report, "Tech Trends 2017: The Kinetic Enterprise." Released last week, the report describes how companies presently must sift through the promotional noise and hyperbole surrounding emerging technologies to find those solutions offering real potential. To realize that potential, they should become "kinetic" organizations — companies with the dexterity and vision required to thrive amid ongoing technology-fueled disruption.

Tech Trends 2017 examines seven key trends that will likely revolutionize enterprise technology in the next 18 to 24 months. Among the trends discussed are machine intelligence, dark analytics and mixed reality, which is a blend of augmented reality, Internet-of-Things and virtual reality. The report also covers innovations in analytics, digital and cloud that are transforming the way organizations engage with customers and citizens; and reimagine products, services and business models.

"Kinetic enterprises are fluid and their leaders understand that to remain relevant, they will need to develop a deliberate innovation response to these disruptive forces," said Bill Briggs, chief technology officer and managing director, Deloitte Consulting LLP. "It's not about chasing every shiny new object; it's about translating the raw potential of emerging technology into a focused set of priorities with measurable, tangible business impact."

According to the report, some of the key trends that will transform the business landscape in 2017 and beyond include:

• Dark Analytics: Advances in computer vision and pattern recognition allow companies to plumb the recesses of unstructured data, which may include images, audio, video and information residing in the "deep web." These tools can unlock powerful strategic and operational insights for businesses in the next level of technology-driven enlightenment.
• Everything-as-a-Service: Services-based ecosystems are becoming increasingly common in business. This model requires open and agile systems, which could provide a business rationale for modernizing legacy core systems. From next-generation ERP to "replatforming" custom back-office applications, everything-as-a-service (XaaS) can help information technology achieve greater efficiencies and lay a foundation for business innovation and growth.
• Machine Intelligence: Artificial intelligence and machine learning are doing more than providing insights and recommendations. Increasingly they are augmenting and automating more complex, mission-critical tasks. This continuum covers cognitive and predictive analytics, bots and robotics process automation — related but distinct disciplines delivering on the broader promise of machine intelligence.

"This goes beyond the CIOs and IT department. There are factors changing every element of business," said Briggs. "Machine intelligence, blockchain and other technologies will have huge implications for talent, operations, and for the enterprise as a whole. Developing a strategy for prioritizing investments and harnessing these emerging technologies has become a boardroom directive."

The report's "Exponentials" chapter identifies four key areas blending science and applied technologies — nanotechnology, advanced energy storage, synthetic biology and quantum computing. Business leaders across industries should be aware of the looming potential these technological advances hold and begin exploring ways to harness exponentials within their organizations.

In addition, each chapter features a "Cyber Implications" section, which helps CIOs balance potential with responsibility around security, privacy and compliance. For the kinetic enterprise, striking this balance is necessity, and reflects a shift toward viewing risk strategically as a core discipline. The report also includes case studies, perspectives from industry luminaries, and experience from Deloitte practitioners that crosses government, business and society.

(Source: Deloitte)

Authored by Grant Thomas, Head of Practices at BJSS, the below provides Finance Monthly with particular insight into the top trends and movements UK financial services organizations will encounter in 2017, and increasingly in the future.

Financial services have always been at the forefront of technology. The industry was amongst the first to invest in mainframe computing, while it pioneered complex integration points to global payment switches, and in 1967 Barclays introduced the concept of self-service with the world's first ATM.

Fintech takes this innovative spirit a step further, and in spite of operational challenges, is driving the development of pioneering ideas to improve customer experience, efficiency and security in the Financial Service sector.

1. Brexit has injured Fintech. But not fatally.

One of the biggest questions to be answered this year is the extent to which Brexit will stifle Fintech innovation and if there will be an exodus towards competing financial centres such as Paris and Berlin.

At face value, things look challenging. Proposed restrictions to the free movement of talent may make it more complex and expensive to hire experienced staff. The process of securing VC funding is likely to become more rigorous as financiers look towards investing in less politically risky climates, but many opportunities still exist.

The key opportunities are that the lower value of the pound has made UK providers more commercially attractive, allowing local firms to compete against their offshore rivals. Added to this, changes to the regulatory environment, and continued R&D in complementary technologies will mean that London will continue to play a leading role in Fintech.

2. Product roadmaps will adapt to support the Bank of England’s new regulatory environment.

The UK will be keen to remain an attractive financial destination, so the Bank of England will take a critical look at its regulatory environment, deciding on which financial regulations require tweaking, diluting or eradicating. The regulator will also look at introducing new financial products, as demonstrated by a recent announcement of its ambitions to launch a Bitcoin-rival cryptocurrency. As a result, Blockchain, which automates and adds transaction security, will continue to attract investment.

Also, evolving regulatory directives such as Open Banking and PSD2, will create an even more difficult operating environment for established players – there will be great demand for Fintech providers to help plug this gap.

3. Mobile devices will become Fintech’s primary channel.

According to Ofcom’s 2016 Communications Market Report, Smartphones are now our preferred channel for accessing online content. Now they are set to become the main way of managing personal finances. Already three out of every ten mobile internet users use their devices to access their bank accounts, while two out of every ten use their devices to complete electronic payment or transfer transactions.

While most consumers are already familiar with services such as Apple Pay, Android Pay and Samsung Pay, Fintech providers will exploit online as well as built-in NFR and biometric technologies to introduce peer-to-peer payments, digital-only banking, forex, and mobile wallet products.

But mobile is just one part of the future of Fintech, and the ability to crunch diverse and deep datasets will drive greater innovation.

4. Customer take-up will be driven by Big Data, Data Science and Analytics.

Fintech providers will look at exploiting tools such as Hadoop, Python, NoSQL and Spark onto Private and Public Cloud services in order rapidly to deliver outcomes and to identify and understand customer behaviour and target markets.

Big Data will be combined with sophisticated machine-learning algorithms to upsell products and services based on key life milestones. This use of data science will proactively push financial products based on customer behaviours, instead of simply waiting for clients to submit product applications. Modern computing and advanced mathematical techniques enable personalisation, at any scale, and without human intervention.

5. Artificial Intelligence and machine learning will use this data to put a human face in computing decisions.

AI technology presents a huge opportunity for Fintech providers because it combines the rules-based reality of computing, with a human interface. It enables providers to take quick, business-safe decisions while reducing the processing time of routine customer enquiries. The model can be adjusted to accommodate customer preferences, their demographics, and interests. Thanks to language interpretation, a customer will be able to ask a question, which will be processed and answered by a Bot either by through text to speech or instant messaging services.

AI has development commitment from the big players. Apple, Amazon, Google, Facebook, IBM, and Microsoft have partnered on a non-profit joint venture which aims to “conduct research, recommend best practices, and publish research under an open license". AI is becoming mainstream.

By adding machine learning to the mix, the accuracy of chatbot responses is improved. When combined with AI and superior user-driven service design, Fintech providers are able to provide compelling and personalised customer interaction products to improve reliability and customer satisfaction. Those Fintech providers who focus on using AI and machine learning will pioneer a customer experience revolution: CX2.0.

This will lead to the death of ‘off the shelf’ and proprietary one size fits all.

Wide-ranging standards such as Blockchain, mobile, Big Data, AI and machine learning preclude a single one size fits all “off the shelf” solution. Fintech providers with ambitious roadmaps will embrace low-latency products based on enterprise-grade Open Source which are proven and secure.

Also, given the speed at which this new technology is evolving, Fintech providers will adopt an Agile approach to building their products. The benefit of Agile is simple. It accelerates delivery processes, and through on-going planning and feedback, ensures that value is maximised. Crucially, Agile also supports continuous delivery, ensuring that quality is maintained and that any risk of failure is reduced. With Agile and continuous delivery, Fintech providers will be able to rapidly develop and refine their products to support an ambitious roadmap. They enable Fintech providers to ensure that the engineering of their products, integration, functional and non-functional tests, deployments and provisioning are catered for throughout.

Britain’s role in the Fintech space is secure and, thanks to a range of next generation technologies, coupled with an improving operating environment and Agile development processes, will provide compelling products and innovation that will boost service provision and reduce costs.

You may have heard the words ‘data management’ flying around left, right and centre with no clear understanding on what it is and how paying attention to said meaning could be useful to you, so this month Finance Monthly heard from Maysam Rizvi, a 15-year banking innovator, who provides particular insight into exactly why the data revolution is worth paying attention to. Maysam is the Founder and MD of Aelm, and is responsible for managing change initiatives at international institutions including J.P. Morgan and National Bank of Dubai.

In 2006, UK mathematician Clive Humby coined a phrase that was utterly obvious, hugely prophetic and unerringly timeless. Pointing at the raw material with which we'll build life's next phase, he said: “Data is the new oil.”

In 2017, some 2.5 quintillion bytes of data are created each day. At this rate, it'll take just three months to double the world's entire existing data stock. So Humby's statement is truer now than it was then: data is every industry's imperative. And that's quintuply true for banking.

If financial institutions want to edge ahead, and stay there, it's time to fully embrace data and its possibilities for the long term.

Financial institutions have been longsighted enough to harvest data, but our putting it to work has been sporadic and disorganised. We've been slow to deploy data in areas like regulation and compliance, and we've probably been over keen, and under-effective, in areas like credit and risk.

To digress slightly, I grew up watching movies like Terminator 2: classic struggles depicting robots (bad) versus humans (good). As a young man, I learned – as many of us did – not to trust a world that's in the hands of Artificial Intelligence (AI).

Whenever machines edge out a human workforce, or Hollywood spawns a new cyber villain, robots' reputations nosedive. But it's important to remember that AI is simply a manifestation of data: sets of numbers, trends and analytics built and programmed to perform tasks.

It's daunting, but today's data is the foundation of tomorrow's AI. And the effectiveness of banks' AI will, as the future of finance draws nearer, separate the wheat from the chaff.

The proposition is this: banking will soon rely incalculably on AI. The bedrock of AI is data. We are in a position to mine and manage rich data now.

If the story of the industrial revolution is one of optimising processes and stripping out costs, the tech revolution has utterly multiplied that paradigm.

Twenty years ago, cars started to, basically, build themselves along production lines. Today, quantum data and real-time machine learning means cars can now drive themselves. That's data in action.

And so is this: a 2013 study by Oxford University’s Carl Frey and Michael Osborne estimates that 47 percent of US jobs may be replaced by robots and automated technology within 20 years. Owing to all the brains required, banking is the kind of high cost industry where an AI coup is inevitable.

Since the ATM, we've given pieces of banking over to machines. From internet banking to intricate trading algorithms, anything that can be handed over to machines has been – and will be.

So, that's the proposition. And we can probably make peace with it. Then comes the practical.

How can banks adapt and ensure a steady transition?

On that, there's no quick answer. Whether it's retail or investment banking, preparing for mass AI means dramatically improving technology infrastructures, and sorting a lot of data.

Aside from what already sits in banks' data vaults – and what data is being crunched this very moment – 2017 will bring more machines, software and apps that'll further swell the data highways. We will probably never hit a data ceiling so I can't overstate the importance of a sound and forward-looking data management strategy now.

Central to that strategy are things like business intelligence: drilling quickly to the truth in your data. Storage: expensive server farms versus the Cloud. And security: Tesco got hacked, TalkTalk got hacked – the threat is very real.

Unfortunately, fix-all, pan-department, off-the-shelf AI systems aren't available. So, automated platforms, AI, robots – call them what you will – need to be mapped, developed, integrated and trained. And this data management strategy can't exist in isolation: banks need to roll it up as part of a wider digital strategy, and as part of an overall business strategy.

For starters, new talent is required to develop, design, deploy, analyse and work with new technologies, while current employees will need to be reskilled for a new reality.

Then there's clients and customers. Institutions that are able to construct and manage efficient, intertwining data flows must find ways to push benefits down the chain.

Like it or not, banking is not a trusted industry. Putting more automation between customers and their money or goals may be a bitter pill to swallow. In addition, the AI push will see certain people nudged out of jobs, so banks must think about payoff.

Customers aren't daft. Facebook, Google, Uber - we wearily trade our data in exchange for what, in the end, are personal, hyper-relevant services. Banks need to, basically, come up with their own 'crystal ball' technology.

Uber knows where you are, before, during and, now, even after your ride. It knows where the driver is; how much you'll pay; what service you require.

Uber has a crystal ball. But all that goes to show is that we're not staring down an impossible task. Banks have power, reach and resource at their disposal so my last point, which might sound laughable after all that, is to try and keep things simple.

A comprehensive data strategy for your bank may include only a dozen key end goals, so start there and work back: there are some great brains out there to help you with the detail.

Banks need to believe in and invest in a future made of data. If you don't, the others will.

In fact, the others are.

If you’re a bank looking at AI solutions, I advise you to consider

Where can you apply AI and how to set it up?

How quickly can you adopt an AI solution?

How to manage your team's transition through this technology upscale;

What do you need to do to your existing infrastructure to make this successful?

Tying business strategy closely with technology strategy;

Taking baby steps, solving one problem at a time;

Building the right partnerships to facilitate the transition.