One of the hottest and most contentious issues facing banks today is how and when to utilise Artificial Intelligence (AI) within a business. AI has transformed many industries and consumers everywhere are becoming increasingly used to the idea of driverless cars, conversational chatbots and suggestive recommendation services.

While AI is relatively new in the financial industry, there are significant concerns and limitations that banks must get their heads around. For example, there is much fear surrounding the integration of AI in workplaces as people believe it will result in job losses and ‘robots’ ruling the world. Even the Bank of England has expressed concern, with their Chief Economist predicting a disruptive fallout from the rise of AI that could make many jobs obsolete.

But when applied in the right way, AI can bring endless opportunities, taking away tedious tasks and amplifying what we do as humans. Tanmaya Varma tells us more.

 Where does AI fit?

Discerning how best to use AI, without alienating customers or employees, is a complex issue. Within the finance sector, AI is already being implemented to support with tasks such as fraud detection and management, and credit card and loan risk assessments. JPMorgan Chase, for example, uses image recognition software to analyse legal banking documents. It is efficient and accurate, extracting information and clauses in seconds compared to the 360,000 hours it takes to manually review 12,000 annual commercial credit agreements. This sort of capability could transform the lives of many banking employees as they will no longer be consumed by administrative tasks but can focus on value-added roles instead.

AI is perfectly suited to many straight-forward roles within customer experience. As much as 98% of all customer interactions are simple queries and bots can be used to monitor and streamline these engagements. For example, RBS’s chatbot ‘Luvo’ has the ability to respond to basic customer queries; and can therefore reduce the need for as many customer service employees.

Over the last couple of years, Goldman Sachs, JP Morgan Chase and Charles Schwab have introduced robo-advisers that are able to manage investments, collect financial data and use predictive analytics to anticipate changes in the stock market. While some employees are concerned about competing with this technology, we’re already seeing the use of bionic advisers in the finance sector. These combine machine calculations and human insight to provide a more efficient and comprehensive analysis, whilst also still maintaining the superior customer service clients have come to expect from their financial adviser.

The robots’ limitations

AI has such great potential but there is still one key thing missing – emotional intelligence (or EI) and when customers are involved, this really matters. Where a bank might pay less for a fully automated interaction, the justification for paying more for the human touchpoint is the real value of emotional intelligence, something that computers can’t really provide… yet.

Responding to the emotional cues that your customer displays is an extremely important part of a business relationship, and the ability to read and comprehend these signals plays a huge part in tailoring the customer experience. The big challenge for banks now that chatbots are so readily available is to consider when and where this key human trait is required.

Chatbots can’t easily detect a shift in tone or tension in a conversation and aren’t able to quickly appease a customer. For example, while a robo-adviser is great for an inexpensive and basic service, the issue comes when you have a more unique or sensitive financial situation such as debt or divorce. In this sort of more complex circumstance, a human adviser is perfectly positioned to respond to the nuances of the conversation.

Collaboration is key

There is a great opportunity for AI to go hand-in-hand with human employees - chatbots can be used to streamline the experience, deal with straightforward customers and put more complex enquiries through to the most suitable team member. In this way, banks can bring humans and technology together to provide a superior customer service.

Another example of AI working in tandem with human employees is Relationship Intelligence technology. With thousands of contacts on a database, no adviser can possibly be expected to remember what stage each customer interaction is at and build strong relationships with all of them. Instead, AI can provide insights into who your prospects are, which ones are most beneficial to pursue and when the right time to get in touch is. It can instantly make available information and data from all over the internet about any potential prospect from just a name and email address.

As technology advances, banks are having to walk a fine line between looking for cost-saving efficiencies and smarter ways of working, while ensuring their customers continue to receive excellent and personal service. They also do not want to alienate their workforce and create panic that long-standing staff are slowly being replaced by robots. AI can offer a lot but it doesn’t have the human’s ability to build and maintain vital relationships and collaboration between technology and humans is key here. The successful adoption of AI in the workplace is the issue and opportunity of the moment and one that banks will be contemplating for years to come.

Technology advances have changed every aspect of financial markets. For consumers, this transformation has made financial services more affordable, accessible and tailored to our individual needs. For financial institutions, digital tools, including emerging technologies such as artificial intelligence (AI), robotics and analytics, have delivered huge opportunities to radically improve the efficiency and effectiveness of risk management, while reducing costs and better meeting the needs of customers.

However, these advances have also raised fundamental questions around how regulation should adapt. For an industry still finalizing reforms introduced after the global financial crisis, financial technology and innovation present a new round of challenges. That’s why it’s time for financial institutions and regulators to ask: How can we build a regulatory environment fit for a digital future? Below Kara Cauter, Partner, Financial Services, Advisory Ernst & Young LLP UK, answers the hard question.

Technology’s potential to make financial markets safer

It’s inevitable that new technologies introduce new risks, and new twists on old risks, as well as different ways of working. Systems can fail and undermine market stability; machines can make decisions with unintended consequences that harm customers and markets; and the almost limitless data that is the lifeblood of the digital world can be manipulated, misused, stolen or inadvertently disguise criminal behavior. But new technologies also offer significant opportunities to improve risk management and enhance the efficiency, safety and soundness of markets and convenience to consumers.

As a result, financial services firms are constantly tapping into new tools to improve the customer experience and strengthen risk management and compliance:

• Open banking, enabled by application programming interface (API) platforms, allows multiple firms to interact, access and update data to deliver new customer services.
• A combination of algorithms and natural language processing (NLP) delivers financial advice. These advanced machines can also “read” news and digital information sources to evolve strategies, initiate trades and analyze market and customer activity.
• Distributed ledger technologies (DLT) are underpinning new methods of record-keeping, transacting and new mediums of exchange.
• AI, combined with other tools, is enhancing the scope and effectiveness of monitoring and surveillance tools that seek out fraud, market abuse and money laundering, extending the scope and quality of controls application and testing.

Regulators are also exploring how to use technology in their role:

• Using machine learning to enhance surveillance of market activity and check the validity and accuracy of reports and models that firms submit to them.
• Some are considering how digitization and automation could change the way regulations are written, distributed and complied with, while others (including governments) are incorporating centralized digital records of individual and company identities (i.e. digital passports to accelerate the onboarding of new clients to new services).
• Many regulators are establishing regulatory “sandboxes” – innovation hubs – to test new, technology-led services in safe environments, where possible harm to markets or consumers can be limited.

• New products such as cryptocurrenciesare also being analyzed to determine whether they should be covered by existing regulations for other financial activities or products, or defined as a new asset and subject to new rules.

Time to ask new questions about old risk principles

But despite positive moves to deploy technology to improve the security and efficiency of global financial markets, it’s still early days. Both industry and regulators are struggling with fundamental questions around how to identify and describe the risks posed by new technologies and new ways of doing business.

Delivering regulatory answers fit for a digital future will call on all market participants to revisit old principles, ask new questions and work together. Building a transparent, balanced, and connected risk management ecosystem will require:

• Collaboration in compliance: Scale, consistency and investment create conditions that encourage technological improvements and support market-wide benefits, as well as greater risk management. But scarce resources, limited reach and fragmented processes constrain investment by individual firms. Collaboration across the sector can overcome these issues.
• Standardization to speed the path: Digital tools are built on rules and algorithms, fed by data. But inconsistent and unclear definitions and standards for data are hampering opportunities to deploy new technology where it could be most effective: in providing market-wide services and solutions.
• Rethinking accountability and transparency to share the load: Senior bankers have no doubt that they will be held accountable for their organizations’ failings, large and small. But it’s time to rethink the parameters of accountability. When third parties have access to outsourced services data and when more decisions are made, or influenced by AI, who is responsible for breaches?
• Reassessing risk management to accelerate progress: Traditional risk management is built around quantifiable factors such as market, credit and liquidity risks. However, digitized financial markets require a reimagining of these basic principles now that risks can manifest in milliseconds and multiply exponentially, sometimes outside the control of any single person, firm, or function.
• Governing, not just mining, data: Customers see the benefits of faster, customized services enabled by open access to data, but their wariness of possible abuse is growing, and cloud solutions are adding a new dimension to the data ecosystem. Technology-led solutions to monitor, analyze, and protect data can match the scale and magnitude of these risks and enable their management.

Ultimately, as regulators and market participants navigate the FinTech landscape, they’ll need to consider how to best use and regulate the use of digital tools to deliver effective risk management and compliance – without stifling the innovation that can help deliver better and secure financial services.

Technology is transforming almost every area imaginable, but education and recruitment are surprisingly yet to be disrupted, and consider themselves to be relatively early in the adoption of technologies. These technological developments, combined with data analytics and job-specific simulations are at the forefront of driving this disruption, particularly in the financial sector. Below Finance Monthly hears from William de Lucy, CEO of Amplify, who delves into the drive behind technology development in the recruitment departments of finance teams worldwide.

Businesses are now delivering targeted training for companies throughout the fintech ecosystem, providing them with new, innovative ways to enhance the learning environment for prospects, resulting in a higher calibre pool of talent for the client.

It would appear that despite a certain level of volatility existing in the financial sector, leading financial institutions are still chomping at the bit to secure the best candidates, demonstrating the overall buoyancy of the market. Much like certain aspects of the financial ecosystem that is witnessing a transformational shift away from manual, human-oriented tasks, the level of automation and simulation in financial recruitment can reap huge rewards for leading institutions.

Evolution of technology and data allowing real world simulation

Technology and data expectations have never been higher, due to the major advancements in technology that have driven this change. Not only has technology significantly increased the amount of data being generated, but it has also provided affordable and efficient ways to collect and store this data so that organisations can leverage data-driven strategies to innovate, compete, and obtain value from information. With technology upending workflow and processes, tasks that were once handled with paper money, bulky computers and human interaction are now being completed entirely on digital interfaces.

Data analytics have come a long way in recent years. From e-commerce businesses tracking who visits their websites and what pages they visit, technology has moved to the collection of huge amounts of data about consumers and their behaviours. This has led to a huge paradigm shift from focus on products, to focus on consumers and what they want and value. Financial services institutions that use big data to drive their decisions will win the competitive race in the long run.

Education with the implementation of technology

Technology has previously been seen as a disruptive influence in the classroom, however this perception is slowly changing. With apps that change how we shop, eat and communicate, technology is moving at a fast pace, and society is having to adapt alongside it. Education with the help of technology has opened up a world of opportunities for students. From collaboration through the use of emails to easy sharing of information - technology is and will continue to alter the education sector into the future.

Students are now looking at the value they receive for their investment. They want to know how this experience will help to secure a place in their chosen careers, rather than the academic ambitions that their professor may have harboured when they were a student. Technologies can give students the same on-the-job training experiences delivered to clients, which enables them to directly connect with such institutions when they perform.

The simulations of real-life work roles give students a broad experience across the entire industry, from any area including investment bank market-making and sales-trading to portfolio and risk management. The objectives are for students to learn through ‘doing’, allowing them to enhance their academic skills and to better prepare them for their future workplace and their best suited role.

Technology and recruitment within the finance and education sectors

A recent LinkedIn study of 12 global investment banks has found that analysts and associates who left their positions in 2015 had stayed in their roles for an average of 17 months. This compares to a 26 month average in 2005. Furthermore, the study also revealed that some banks are incurring significant costs that are associated with replacing employees who leave.

Bridging the gap between what students are taught in theory, to what happens on a day-to-day basis in an office environment, proved difficult before the implementation of certain technologies. Technology has enabled the disruption of traditional recruitment paths of many major financial institutions which often recruit from only a select few universities and use rigid, automated processes. Along with this, companies are now able to broaden their search and identify talent that may not have been uncovered previously. A candidate could have a distinct ability to perform a specific function outside of their pure academic achievements, which allows for a more diverse workforce and greater overall performance and output.

Technology these days, can give businesses the ability to measure so many different data points over a long period of time. For example, technology platforms can measure how well a potential sales trader, or broker uses voice versus typed communication and how well they can use that communication to leverage client relationships. With this actively taking place over a full-day, or a series of days, it can help to provide corporate partners with graduates who possess soft skills that are required in client facing roles. This can often be hard to find from an initial CV review or telephone interview.

Along with this, technology allows businesses to gather innovative approaches to enhance and revolutionise graduate recruitment, this helps firms find the right candidate for the right role, without having to sift through thousands of CV’s or rely on behavioural data that has been collected from a short game or questions unassociated with the role in question. Due to the innovative approach that technology has enabled, candidates can gain a practical understanding of what their day-to-day role would actually involve, which helps them identify in depth the specific role they can see themselves committing to long-term.

It’s evident that technology is and will continue to revolve and bridge the gap between what students are taught in theory, to what happens in a day-to-day office environment. It has broadened the playing field and identified talents that may never have been uncovered previously. This can lead to businesses becoming more diverse in their workforce and have a greater overall performance and output for their company.

Artificial intelligence (AI) is infiltrating all industries, meaning a transformation in the way we live our day-to-day lives – and the way we work – is inevitable. But this is nothing to be afraid of and we should embrace AI to improve the way we work.

According to Adobe, 15 % of companies currently use AI, with 31 % expected to adopt it over the next 12 months. This significant technological disruption is set to affect everyone in some form, and many are worried that AI will displace our jobs and make humans irrelevant.

However, Reed Accountancy & Finance research found that almost half (47 %) of finance professionals asked are enthusiastic about AI in the workplace and are willing to embrace new technology. This shows there is a lot of enthusiasm about all the ways AI can improve our everyday activities. With this in mind, here are five reasons why we shouldn’t be panicking about the introduction of AI into the workplace.

1. There is strength in humanity

Research from Deloitte shows that 61 % of companies are now actively designing jobs around robotics. However, it is expected that, in the coming years, the skills and traits that make us human and enable us to interact effectively will become increasingly important for employment and career advancement.  While machines and AI will be capable of performing many routine tasks, human cognitive skills will still be sought after, so businesses will still need to target candidates with these talents. The introduction of AI will also free up time for creative thinking and judgement work – areas in which humans are naturally superior. AI can design solutions to complex societal issues, but only humans can implement them, as well as display empathy and compassion in a way machines never can.

2. Enhanced productivity

A study by Accenture has revealed that AI could increase productivity by 40 %, and profitability by 38 %. This is in addition to our own research which found a third (32 %) of finance professionals believe AI will improve productivity and efficiency by having the capabilities to report and summarise accounts  taking away the menial tasks – understandably, businesses are interested. This means employees are free to concentrate their efforts on more stimulating, forward thinking work, making companies using AI very attractive. It can also help with recruitment, where AI can source, rank and arrange interviews with candidates. More accurate forecasting, predicting maintenance and repairs, personalisation, optimising manufacturing and replenishing stock automatically are all areas in which AI can also help companies become more efficient working within their budgets.

3. Attracting Generation Z

By 2030, it’s estimated that Generation Z will represent 75 % of the workforce, meaning innovative methods of appealing to this group must be a priority for all organisations. One way to do this is by promoting the use of AI in the workplace, as this generation appreciates the value that technology brings.  The use of AI-driven foundational technologies, such as blockchain, may also help companies that are based on this technology present themselves as the more fashionable, innovative places to work.

4. Saying farewell to unconscious bias

Unconscious bias has long been an issue in recruitment, and for those responsible for recruitment in an organisation. Some tech start-ups are already using AI to perform initial interviews, along with facial recognition software to detect body language and emotion cues when screening candidates, in order to eliminate the unconscious bias that is so often found in the human decision-making process. In fact, according to KPMG, 60 % of HR departments are planning to adopt cognitive automation in the next five years with the aim of making recruitment a bias-free procedure.

5. New skills, new jobs

McKinsey research has found that, if AI is adopted by 2030, eight to nine % of labour demand will be in new types of jobs that didn’t exist before. History would suggest that, after a large technological disruption in society, over time, labour markets would adjust in the favour of workers. However, the skills and capabilities required for any job will shift, with the need for more social and emotional skills, such as logical reasoning and creativity, making candidates with these skills in heavy demand.

Navigating the unchartered territory of artificial intelligence can be daunting, but there is no need for businesses or candidates to panic. If used in the right way, AI can be incredibly helpful and vastly improve the effectiveness and efficiency of not just many organisations, but our everyday working lives.

There is a rush to improve speed, convenience and user experience in financial interactions, but at what cost to security?

While for the most part bankers are positive about their ability to improve their financial performance in 2018 and beyond, evolving risks – particularly cyber risk – are no doubt preoccupying their thoughts.  A recent report by professional services firm, EY, puts cybersecurity as the number one priority for banks in the coming year, and it comes as no surprise, especially with Britain’s National Cyber Crime Unit data showing 68% of large UK businesses across sectors were subject to a cybersecurity attack or breach in the past 12 months.

It’s a mounting problem, and the financial services industry needs to fight back. We’ve picked out the four key ways of countering the continuing threat to banks’ cybersecurity – and it’s a case of fighting cyber with cyber.

1. Artificial intelligence

Like it is in retail and manufacturing, for example, artificial intelligence (AI) and advanced analytics will play a key role in banking moving forwards.

And the financial services industry is looking to this technology to play a major part in the prevention of cyber attacks, reducing conduct risk and improving monitoring to prevent financial crime.  Mitigating such external and internal threats is critical to both business continuity and limiting operating losses, and so AI shouldn’t be overlooked as a key tool in reaching this goal.

2. Electronic identification

In order to meet the regulatory technical standards, which will be enforced in September 2019 as part of the European Union’s PSD2 payments legislation, the number of transactions requiring two-factor authentication will rise in the coming months.

What has been deemed by the industry as “Strong Customer Authentication” will be required, and this should result in payments and account access relying on customers providing and using a combination of the following: something they know, like a password; something they have, like a phone or card; and something they are, such as a fingerprint.

More factors equals more security is the industry theory here.

3. Biometrics

Which leads us neatly on to point three: biometrics. This push for two-factor authentication and new electronic identification will pave the way for more biometrics use.  With some of the largest players in card payments, including Mastercard, investing heavily in such solutions, we expect others to start to follow suit.

As Ajay Bhalla, President for global enterprise risk and security at Mastercard puts it: “The use of passwords to authenticate someone is woefully outdated, with consumers forgetting them and retailers facing abandoned shopping baskets.

“In payments technology this is something we’re closing in on as we move from cash to card, password to thumbprint, and beyond to innovative technologies, such as AI.”

4. Blockchain

According to the EY research report, 20-40% of financial service providers are investing in Blockchain now and are planning to increase investment, while approximately the same percentage are investing now but planning to reduce expenditure.

Either way, it shows that Blockchain is very much on the agenda for banks. The main attraction of Blockchain is that it creates an indelible audit trail which is distributed across multiple servers, so there’s no single weak link for cyber attackers to target. This provides banks with unparalleled transparency and increases trust.

Blockchain also has the potential to make a complex global financial system less complicated and reduce the number of middlemen involved in the transferring of money.

So, that’s the technology on offer, but what are the next steps?

Unless banks collaborate more with their peers, or improve their use of the wider ecosystem, the required investment in advanced technologies to address issues of growing cybercrime will be substantial and could strain their ability improve financial performance and grow their businesses.

And, as bank leadership teams focus on investing in the relevant people and technology – and it is the combination of both that’s crucial here – to enhance cybersecurity, they may struggle to find the right skill sets or the right methods for integrating cyber experts into their organisations.

Raising their knowledge of the technology available to help stem the tidal wave of cyber threats is a key requirement for banks, if they don’t want to end up washed up on the shore as a result of their defences being breached.

Few would argue that artificial intelligence (AI) is making a considerable impact on many elements of Financial Services (FS), it’s computing power and automation helping to improve the overall customer experience and to extract incredible insight from big data held by FS companies. Below Dr. Dorian Selz, Co-Founder and CEO of Squirro, delves into a discussion about the keys augmented intelligence may carry in driving the future of FS.

As with many emerging technologies it was slow to hit the business mainstream, but that too is changing. Squirro recently conducted research into tier one banks’ use of AI, and it revealed that 83% have evaluated AI and more than two-thirds are already using it.

But for some people in finance, the words ‘artificial intelligence’ can signify fear just as much as they can opportunity. For all the potential of AI, there is a perception that jobs might be threatened as machines take over roles previously carried out by humans.

The idea that AI might facilitate a wholesale replacement of humans is fanciful at best. But perhaps it is time to talk about augmented intelligence instead, a technology intended to enhance human intelligence and one that is central to the future success of the financial services sector.

A human / machine collaboration

If artificial intelligence is the creation of intelligent machines that work and react like humans, augmented intelligence is essentially people and machines working together. This is a partnership that will see the augmentation and extension of human decision making, addressing specific challenges within FS and helping to deliver new and smarter services to customers that will encourage loyalty and improve the bottom line.

Improving the personal touch – much of FS – particularly corporate FS such as investment banking and real estate - is still heavily based on personal relationships. Account handlers speak to their clients and are expected to know about that client’s industry and be able to present them with strong opportunities for investment and growth.

That’s no a small undertaking, but augmented intelligence makes it much more straight forward. Augmented intelligence-based platforms are powerful at gathering data (both structured and unstructured) from across disparate and siloed systems and presenting that data in a form that gives account handlers a complete 360-degree view of each and every customer.

Because it can factor it so many disparate sources of data, users are then incredibly well-informed on what is happening in an industry that will affect that client, and what the opportunities are. They can retain the personal touch that is still so important in FS, but can now do so more informed than ever when speaking to clients

Deeper insight – the insight delivered by augmented intelligence is far deeper than what has previously been available to FS organisations. Because it is capable of managing and analysing so much data, the insight extracted from that data and then presented to the user is deeper and greater than anything previously possible.

Impact on the bottom line – the data insight generated by augmented intelligence can help FS firms greatly with their lead generation, not only identifying opportunities for clients but recommending the best product or solution for them.

Augmented intelligence solutions will look at data on competitors, partners and markets and identify catalysts that provide additional upsell or cross-sell opportunities to existing clients, and fresh approaches to prospective clients. In a competitive FS world, this is of the highest value.

The past decade has been a challenging one for FS organisations, with stiff competition from agile startups offering new and more effective services and a superior overall experience. Yet the emergence of augmented intelligence is a lifeline for the industry. It enables greater customer understanding and means FS providers can re-establish their market position, and augmented intelligence will be a key technology in FS for years to come.

More than two fifths (41%) of finance back-office processes could be automated in the next five years, a new study from global customer services provider Arvato CRM Solutions and management consulting firm A.T Kearney has found.

According to the new report, 41% of finance back-office processes are set to be performed by robots by 2023, with this figure rising to 53% within the next 10 years.

Implementation of Robotic Process Automation (RPA) is set to significantly boost firms’ productivity and efficiency, as bots are 20 times faster than humans with a 10% lower error rate. Subsequently, companies that adopt this technology, could potentially receive an ROI of between 300 and 1,000% over a three-year period.

It’s also predicted that the widespread roll-out of RPA solutions will result in an annual compound market growth of 50%, with the global market set to be worth $5billion by 2020.

New developments

The research also predicts that by 2023, RPA, with the help of cognitive capabilities, will be able to make automated decisions, and by 2028 robots will be able to carry out most back-office processes independently with minimal human intervention.

The new report, named ‘Robotic Process Automation: The impact of RPA on finance back-office processes’, interviewed more than 20 technology partners and players in the field of RPA, gathering together their view on the trends and developments within the sector.

Ben Warren, vice president of Digital Transformation at Arvato CRM, Global BPS, said: “RPA will revolutionize the finance back-office, as the new technology is more accurate, efficient and can work for longer hours, depending on demand.

“This can consequently help drive revenue for a business, streamlining processes and allowing employees to spend more time on higher value tasks.

“But although the benefits of automation can be great, it’s important that firms understand that to successfully utilize the technology they will need to invest.

“A full analysis of end-to-end systems and redesign of existing processes will be initially required, and companies will need to regularly review their processes as technology continues to evolve and develop over the coming decade.”

Dr. Florian Dickgreber, partner at A.T Kearney and co-author of the study, said: “Having transformed manufacturing, bots are now set to change processes in the service sector.

“We expect RPA, the automation of structured business processes, to take over more than half of all back-office processes over the next five to 10 years.”

(Source: Arvato CRM Solutions)

The European real estate sector continues to flourish but competition for deals is fierce and speed is often of the essence: so much so that, according to recent Drooms research¹ over 50% of real estate professionals in Europe are compromising on the quality of their due diligence to complete transactions quickly.

However, modern technology has a solution for those seeking to complete real estate deals more efficiently. Where time pressures have led to a potential decrease in the quality of due diligence, parties to a transaction have found a solution in technology enabled with artificial intelligence (AI), such as virtual data rooms.¹

Real Estate is big business

According to a Real Capital Analytics (RCA) report published in February 2018, Europe’s commercial property investment market returned to growth in 2017² as deals of more than €500 million in value accounted for almost one quarter of the year’s acquisition volume. The UK also regained its title as Europe’s largest market after its investment volume increased by 12% thanks to several large transactions such as CC Land’s purchase of the landmark Cheesegrater building for £1.15 billion.

Successful transactions like this depend ultimately on high quality and detailed due diligence but despite the high volumes of information that need to be processed, the real estate sector is still behind the curve in terms of technology and a significant number of important processes are still conducted manually.

The volume of documentation involved in real estate due diligence continues to grow exponentially and it is becoming increasingly important for key stakeholders to quickly and efficiently navigate their way through the mass of information involved and to focus on the key points.

Our survey¹ clearly shows that over the past two years there has been an overall increased focus on due diligence and 73% of real estate professionals believe this focus will increase further over the coming year. For this reason, AI is increasingly being regarded as a solution for today and not technology for the future.

A closer look at the benefits

More than half (54%) of real estate professionals say that they use AI to improve the keyword search process when working on transactions. However, this figure rises to 69% of respondents who say they will be using AI for keyword searches in five years’ time. Other processes that will become more widely used include foreign language translation, identifying red flags, routing documents to the right decision-makers and topic-modelling.

The majority of real estate professionals believe that AI already benefits their firms’ and provides a competitive advantage by enabling a much higher volume and variety of documents to be searched at high speed. Almost the same number say that AI speeds up the due diligence process, while a third believe it improves the accuracy of decision-making. Other benefits of AI include minimising risks and liabilities in an overall deal, reduced reliance on legal services, the ability to automatically create contracts and reports and securing the best deals before other professionals.

The barriers facing AI

Despite these benefits, there are still perceived barriers preventing the uptake and use of AI in the real estate industry. The biggest of these is lack of confidence in AI’s ability to match human intelligence and decision-making (cited by 53%), followed by a lack of skills available to implement relevant AI technology (51%), technology being too difficult to use (41%), a lack of trust by senior management in AI (19%) and concern that AI will replace investment professionals’ roles (17%). Only 9% say the main barrier is a ‘lack of demand’.

What does the future hold?

As a pioneer in the digitisation of due diligence in real estate, Drooms’ technology is helping to change existing processes by integrating AI into its virtual data room (VDR). The aim when building AI into our VDR technology is to enable real estate professionals to reduce the amount of manual review work, eliminate unnecessary errors and reduce reliance on expensive third-party costs. We are just one example of the application of AI, but a very good one.

Crucially, this is not a battle of technology versus humans. Despite its ability to automate a tremendous number of processes, AI will always work best in conjunction with human skills and intelligence. AI needs to learn from human behaviour and there is no substitute for years of experience, instinct and knowledge. However, AI complements those elements and adds huge value by making real estate processes much more automated, efficient and cost-effective.

Website: https://drooms.com/

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¹Source: Drooms, April 2018 - The future of artificial intelligence in real estate transactions April 2018

²Source: Real Capital Analytics February 2018 - 2017 Year in Review edition of Europe Capital Trends.

The financial services industry has witnessed considerable hype around artificial intelligence (AI) in recent months. We’re all seeing a slew of articles in the media, at conference keynote presentations and think-tanks tasked with leading the revolution. Below Sundeep Tengur, Senior Business Solutions Manager at SAS, explains how in the fight against fraud, AI is taking over as a dominant strategy, fuelled primarily by data.

AI indeed appears to be the new gold rush for large organisations and FinTech companies alike. However, with little common understanding of what AI really entails, there is growing fear of missing the boat on a technology hailed as the ‘holy grail of the data age.’ Devising an AI strategy has therefore become a boardroom conundrum for many business leaders.

How did it come to this – especially since less than two decades back, most popular references of artificial intelligence were in sci-fi movies? Will AI revolutionise the world of financial services? And more specifically, what does it bring to the party with regards to fraud detection? Let’s separate fact from fiction and explore what lies beyond the inflated expectations.

Why now?

Many practical ideas involving AI have been developed since the late 90s and early 00s but we’re only now seeing a surge in implementation of AI-driven use-cases. There are two main drivers behind this: new data assets and increased computational power. As the industry embraced big data, the breadth and depth of data within financial institutions has grown exponentially, powered by low-cost and distributed systems such as Hadoop. Computing power is also heavily commoditised, evidenced by modern smartphones now as powerful as many legacy business servers. The time for AI has started, but it will certainly require a journey for organisations to reach operational maturity rather than being a binary switch.

Don’t run before you can walk

The Gartner Hype Cycle for Emerging Technologies infers that there is a disconnect between the reality today and the vision for AI, an observation shared by many industry analysts. The research suggests that machine learning and deep learning could take between two-to-five years to meet market expectations, while artificial general intelligence (commonly referred to as strong AI, i.e. automation that could successfully perform any intellectual task in the same capacity as a human) could take up to 10 years for mainstream adoption.

Other publications predict that the pace could be much faster. The IDC FutureScape report suggests that “cognitive computing, artificial intelligence and machine learning will become the fastest growing segments of software development by the end of 2018; by 2021, 90% of organizations will be incorporating cognitive/AI and machine learning into new enterprise apps.”

AI adoption may still be in its infancy, but new implementations have gained significant momentum and early results show huge promise. For most financial organisations faced with rising fraud losses and the prohibitive costs linked to investigations, AI is increasingly positioned as a key technology to help automate instant fraud decisions, maximise the detection performance as well as streamlining alert volumes in the near future.

Data is the rocket fuel

Whilst AI certainly has the potential to add significant value in the detection of fraud, deploying a successful model is no simple feat. For every successful AI model, there are many more failed attempts than many would care to admit, and the root cause is often data. Data is the fuel for an operational risk engine: Poor input will lead to sub-optimal results, no matter how good the detection algorithms are. This means more noise in the fraud alerts with false positives as well as undetected cases.

On top of generic data concerns, there are additional, often overlooked factors which directly impact the effectiveness of data used for fraud management:

• Geographical variances in data.
• Varying risk appetites across products and channels.
• Accuracy of fraud classification (i.e. which proportion of the alerts marked as fraud are effectively confirmed ones).
• Relatively rare occurance of fraud compared to the huge bulk of transactions; having a suitable sample to train a model isn’t always guaranteed.

Ensuring that data meets minimum benchmarks is therefore critical, especially with ongoing digitalisation programmes which will subject banks to an avalanche of new data assets. These can certainly help augment fraud detection capabilities but need to be balanced with increased data protection and privacy regulations.

A hybrid ecosystem for fraud detection

Techniques available under the banner of artificial intelligence such as machine learning, deep learning, etc. are powerful assets but all seasoned counter-fraud professionals know the adage: Don’t put all your eggs in one basket.

Relying solely on predictive analytics to guard against fraud would be a naïve decision. In the context of the PSD2 (payment services directive) regulation in EU member states, a new payment channel is being introduced along with new payments actors and services, which will in turn drive new customer behaviour. Without historical data, predictive techniques such as AI will be starved of a valid training sample and therefore be rendered ineffective in the short term. Instead, the new risk factors can be mitigated through business scenarios and anomaly detection using peer group analysis, as part of a hybrid detection approach.

Yet another challenge is the ability to digest the output of some AI models into meaningful outcomes. Techniques such as neural networks or deep learning offer great accuracy and statistical fit but can also be opaque, delivering limited insight for interpretability and tuning. A “computer says no” response with no alternative workflows or complementary investigation tools creates friction in the transactional journey in cases of false positives, and may lead to customer attrition and reputational damage - a costly outcome in a digital era where customers can easily switch banks from the comfort of their homes.

Holistic view

For effective detection and deterrence, fraud strategists must gain a holistic view over their threat landscape. To achieve this, financial organisations should adopt multi-layered defences - but to ensure success, they need to aim for balance in their strategy. Balance between robust counter-fraud measures and positive customer experience. Balance between rigid internal controls and customer-centricity. And balance between curbing fraud losses and meeting revenue targets. Analytics is the fulcrum that can provide this necessary balance.

AI is a huge cog in the fraud operations machinery but one must not lose sight of the bigger picture. Real value lies in translating ‘artificial intelligence’ into ‘actionable intelligence’. In doing so, remember that your organisation does not need an AI strategy; instead let AI help drive your business strategy.

Whenever we think about new technology we certainly are not exactly comfortable with it. We view and look at it with suspicion and believe that status quo is the best. One such technology which is taking the world now by storm is known as artificial intelligence or AI. Hence it would be interesting to know more about it over the next few lines. It is now being selectively used and it will not be long before it is used in other areas. It certainly will make a big difference to any small, medium or big business and help to move it from one level of success to another.

(Source: www.websitesthatsell.com.au)

New research by BAE Systems has found that 74% of business customers think banks use machine learning and artificial intelligence to spot money laundering. In reality banks rely on human investigators to manually sift through alerts – a hard-to-believe fact selected only by 31% of respondents. This lack of automation and modern processes is having a major impact on efficiency and expense when it comes to the fight against money laundering.

Brian Ferro, Global Compliance Solutions Product Manager at BAE Systems Applied Intelligence, said: “Compliance investigators at banks can spend up to three days of their working week dealing with alerts – which most of the time are false positives.  By occupying key personnel with these manual tasks, banks are limiting the investigators’ role, impacting on their ability to stop criminal activity.”

Money laundering is known to fund and enable slavery, drug trafficking, terrorism, corruption and organised crime.  Three quarters (75%) of business customers surveyed see banks as central actors in the fight against money laundering. The penalty for failing to stop money laundering can be high for banks – and is not restricted to significant fines. When questioned, 26% of survey respondents said they would move their business’ banking away from a bank that had been found guilty and fined for serious and sustained money laundering that it had not identified.

Ferro continued: “For banks to be on the front foot against money laundering, their investigators need to be supported by machine intelligence. Simplifying, optimising and automating the sorting of these alerts to give human investigators more time is the single most valuable thing banks and the compliance industry can do in the fight against money launderers. Right now, small improvements in efficiency of the systems banks use to find laundering can yield huge results.

“At BAE Systems we use a combination of intelligence-led advanced analytics to track criminals through the world’s financial networks. By putting machine learning and artificial intelligence systems to work to narrow down the number of alerts, human investigators can concentrate on tasks more suited to their talents and insight.”

(Source: BAE Systems)

The Lords Select Committee recently issued a report: “AI in the UK: ready, willing and able?”. It outlines the burgeoning AI industry including the public understanding, engagement and design of AI and how the UK can become best placed to build and develop safe, secure and successful AI businesses.

Louis Halpern, Chairman of Active OMG, the British company behind the natural language conversational self-learning AI, Ami, spoke to Finance Monthly below.

AI will penetrate every sector of the economy and has tremendous potential to improve people's lives. I am pleased the report aims to set out a positive framework for the UK AI industry. However, the proposal is not enough to make the UK a leading destination to build and develop AI businesses. We embrace technology when it is safe, normal, and when it makes our lives better. If AI policy is directed at these elements we have the opportunity to make the UK a world leader.

For us at Active OMG safe means personal privacy. Consumers need to know their data is safe. We have to avoid the AI industry being tainted with Facebook Cambridge Analytica type scandals.

We use personal data so our clients’ customers have a better experience. When we apply the machine learning part of what we do the data is anonymised. We do not know if they are Mr or Mrs Jones, Chen or Blackwell. We are not concerned with the details of individuals. There is complete separation of personal and anonymised data.

Overcoming fear needs education, not reaction to “scandal’. The government should be educating individuals on how their data is going to be used and kept safe by the current legislation. We suggest a government information campaign, like the drink driving or Aids campaigns of the past. By explaining the data issue to people it will proactively help normalise AI.

The report talks about investing in Phd programs and cultivating AI companies directly from academic research. Yes, but we need to start programs in schools now to teach children how to use AI as a tool to thrive in a world where AI is normal. In the Pan Canada AI strategy they argued that AI should be taught alongside degrees, e.g. Sociology with AI. To become a leading AI nation, Britain must adopt a similar stance and ensure AI is intrinsic and everywhere.

Economies thrive on entrepreneurship. Entrepreneurs will develop the AI that will change our lives, like the iPhone and the personal computer. These devices cut across every industry and benefit every consumer. Government needs to follow the same model. The report talks about specific Government departments and initiatives; these will stifle, not accelerate. Every Government department needs to set an example by making policy that puts AI at the heart of what they do. No western country has been this brave.

When electricity became available it’s light quickly illuminated everything. To be a world leader, the UK needs to ensure AI’s light shines in every corner.