The emergence of AI has had a positive impact on the financial industry and has enhanced productivity, in particular in the accounting and banking areas. Therefore I anticipate that machine learning will definitely be a significant area of investment in the near future for this sector.   However, as with any change of this magnitude, the benefits offered by the implementation of AI in the financial sector are met with a number of challenges – most notably businesses ensuring they are equipped with the right technology, staff and skills to embrace AI and automation.

Automation is now used to perform or enhance many administrative tasks, and Artificial Intelligence is already more a part of daily life than you might realise. Robotics, while commonplace in manufacturing, are beginning to show impact in other sectors. One of the key drivers behind the adoption of AI software in the financial sector is the time-saving benefits it offers users. Gone will be the days of long hours spent working on spreadsheets, processing data, or handling customer enquiries. Those tasks will be streamlined by machines, allowing workers to focus more time on complex tasks which require human touch. As well as working with advancing technologies, junior employees will be involved in more planning, reporting and analytical jobs, and as such their required skill set will change.

Gone will be the days of long hours spent working on spreadsheets, processing data, or handling customer enquiries.

Through machine learning, artificial intelligence can painlessly consume and process large amounts of data at an accelerated level. Its vast speed brings efficiency and productivity to the financial sector, and as it continues to develop and become even more efficient, it can identify more patterns than ever before, providing scope for customised offerings to customers. However, this being said, adoption of AI in the financial sector imposes many challenges to the industry. The use of AI’s ability to consume large amounts of consumer data raises questions about how this information is stored and processed and to what end. Organisations that encourage, and even mandate the uptake of these types of technologies must tread carefully. Individuals are already highly attuned to the sensitivity of their personal information and will require robust guarantees about the security of any further information they are willing to give up.

One limitation of machine learning in this context is that it primarily relies on the basis of historical data sets and as a result, can fall into the trap of becoming repetitive, as well as potentially giving way to conscious or unconscious bias. For instance, how fair can a financial system really be without human involvement? In a world where new technologies are quickly improving or even replacing existing processes, there is one area that cannot be automated, and that’s building strong relationships with clients. The human element is needed in these instances to perform certain job functions that AI is incapable of replicating. Individuals have the ability to be aware of their own emotions and those of others, but also their capability of showing empathy in the way they handle interpersonal relationships, which is known as emotional intelligence.

It’s crucial for businesses, in the fast pace of today’s world, to continue to develop and think about where their use of data can get them tomorrow, as well as where it’s got them today. Organisations must not become complacent, and instead continue to reflect on their processes, challenge routine and be future-facing in their approach to machine learning.

One limitation of machine learning in this context is that it primarily relies on the basis of historical data sets and as a result, can fall into the trap of becoming repetitive, as well as potentially giving way to conscious or unconscious bias.

Over the last two decades, technology has advanced at such a speed that many roles in the financial sector have either disappeared or wholly changed due to the implementation of AI technology. One of the many challenges facing the finance industry is the impact that AI is having on the job roles within sector. Artificial intelligence and automation can take on many of the tasks a transaction led accountant or data administrator would typically undertake, with little or no human involvement. The process is almost seamless, error-free and time efficient.

The challenge of economic survival of the financial sector is to not only accept these changes, but to capitalise on them. With any significant change in the market, there’s always a fear that it will eliminate jobs from the workforce. AI tools may well remove a number of job tasks carried out by accountants and data administrators, but rather than eradicating jobs and losing talented members of staff, employers will need to ensure that their HR directors are equipped to spot the right skilled professionals who are well versed with the latest AI technology. The HR function will also need to quell fears of job losses amongst employees and instead empower their staff to adapt and develop new skills to work alongside new technologies.

At this juncture, skilled employees are key – and we anticipate a change in the skills that businesses across the financial sector will be demanding from their employees and prospective hires. For years, Michael Page clients in this sector have been seeking candidates with financing and analysis skills; those that have a strong understanding of financial planning and reporting; people who are adept at using Excel and other such software. Our recently launched Skills Checker tool has taken the most in-demand skills for roles across the financial industry to highlight what employers are looking for today. But as we continue to see AI and automation adoption increase in the sector, we expect to see a rise in employers expanding the skill sets they require from new employees with coding and AI experience becoming ever more valuable.

One of the many challenges facing the finance industry is the impact that AI is having on the job roles within sector.

To the same end, the advent of these new technologies presents the opportunity for businesses to enhance their current workforce by equipping employees with the skills to work alongside AI and automation. A challenge in itself, such training should not be brushed off as a ‘nice to have’; it is vital for the growth, and even the survival, of a business. Employees are the lifeblood of any business, as the landscape of the financial sector changes, businesses must ensure that their workforce is keeping pace with the industry.

Before incorporating AI software into their businesses, organisations will need to think strategically about what their key objectives are and what they hope to achieve from using the technology. This is the only way they can truly expect to see any long-term benefits, through a strategic and considered approach – not simply thinking of AI as a ‘nice add-on’. It’s also important for organisations to have realistic expectations.

Businesses should start by looking at key areas where they can make an impact by using this technology on more routine tasks and go from there. This will help to build their confidence and understanding of the software over time, rather than trying to implement it all at once. Strategic thinking and patience are key here.

Although robots and AI will inevitably take a lot of the more data-driven job functions, there will be a change in how humans and machines interoperate for the highest level of efficiency and playing to each other’s strengths. The increased use of AI in the financial sector is going to spur on new innovation, and an entirely new landscape of jobs are going to emerge. Although there is always a lag between the adoption of new jobs and loss of current jobs, up-skilling and re-skilling are going to be the key to success in the future of the financial job market.

Website: https://www.michaelpage.co.uk/

Headlines have raised fears in recent months that robots threaten many of our livelihoods. However, Jan Hoffmeister from Drooms says that those in the private equity (PE) industry should instead be encouraged by how Artificial Intelligence (AI) technology can give them an edge in a competitive marketplace.

AI technology cannot replace human thinking in relation to strategy and business planning, which are fundamental to PE. But it is an impressive tool when it is correctly incorporated into the more process-driven functions of PE firms, increasing the power to collect, process and distribute information to the right parties with much greater speed and accuracy.

The need to stand out is imperative in the highly competitive PE market. Analysis by EY¹ shows that while the industry has made a strong recovery after the crash of 2008, there is also a lot of ‘dry powder’ sitting in the wings because of intense competition for deals.

Total PE commitments globally stood at US$530.7 billion in 2016, which was close to the US$616.7 billion pledged in 2007. However, in 2017, only US$440 billion of transactions took place versus US$748.4 billion in 2007. In terms of dry powder, there was US$525 billion sitting without investments in 2016.

The key issue is that the right investment targets with appropriate valuations are hard to find. Offering a solution to managing the deal-making process helps a PE firm stand out amid intense competition. Using a virtual data room (VDR), which leverages AI technology, makes a firm best in class, whether it is used for a one-off transaction or to create value in assets over their entire life cycles.

Successful PE firms are thorough in their due diligence, nimble and open-minded to pinpoint the right opportunities and disciplined about formulating the right investment philosophies.

There are two key areas in which a VDR is useful for PE firms, particularly if it is used during the ‘hold’ phase of an asset. The first is consistency, in that documents can be updated regularly, giving the vendor full control over data, sourcing investment targets and achieving correct valuations. The second is responsiveness – documents are always ready, so assets can be bought or sold whenever required.

Given that the intention of PE firms is always to sell an asset, it is especially relevant for them to establish a ‘life cycle’ VDR that can be used to manage a company throughout the period of ownership, from purchase, through management and on to divestment.

A VDR connects authorised users, including those inside a company and their external stakeholders, digitally and in a secure environment with real-time access to all relevant documentation.

A VDR always makes documents relevant to a transaction available to authorised parties and helps ensure that they are up-to-date. All data is stored securely online on a server platform and is always accessible to both internal and external parties, depending on their individual permission levels.

Creating a database in which documents can be updated consistently gives asset owners full control and the ability to react to the latest market conditions, bringing assets to market quickly when the conditions are right, sometimes at short notice.

One of the strengths of the Drooms NXG VDR is its Findings Manager function. This improves the vendor due diligence both prior and during the sales process. It allows for the automatic pre-selection of documents and helps in the assessment of potential risks and opportunities within a transaction. This yields greater control, instills confidence in potential buyers and cuts disruption to existing business.

Those PE firms involved in cross-border deals will find the Drooms transactional room particularly useful. It includes a tool that translates documents in real-time, ensuring risk assessments are maintained in a timely fashion throughout the process.

Essential elements

The integrity of documentation is paramount for PE firms. When deals are going through, unclear, incomplete or erroneous documents can cause all manner of problems, including sales falling through. Documentation must provide an accurate assessment of the value of an asset.

For clarity and transparency, a VDR must also have a stringent and standardised index structure for all assets within a portfolio. All an asset’s documentation should be organised in the same manner, allowing quick access to relevant content for the purposes of comparison. Long-term value can be created in assets if they are encapsulated by standardised and sustainable data – and life cycle data rooms are the optimum tool for this purpose.

The practicalities

In practice, careful planning is essential to manage a life cycle VDR successfully. This starts with getting an accurate snapshot of a project’s current progress using key metrics such as available (and missing) documents.

The time frames, processes and the responsibilities of all relevant parties should be defined, and their commitment secured to the proposed solutions, including any changes to management processes.

All the relevant documents must then be collated and, if necessary, digitised before being uploaded to the VDR. Finally, the VDR must be regularly monitored and maintained, updating and adding documents as required.

Most powerful tool in the box

PE firms that wish to manage a market currently characterised by dry powder, high valuation and enhanced competition need to adopt beneficial technologies. A VDR adds value at all the stages of an asset’s lifecycle, including buying, holding and selling, making the whole process much smoother. The value added in terms of making better deals, improving operational efficiency and enhancing the transparency increasingly demanded by stakeholders makes a VDR one of the most powerful tools at a PE firm’s disposal.

¹Source: EY, Global PE Watch, 2017

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.

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.

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.

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.

“In the future, robots will be doing our jobs for us” is one of the most common sentences we hear from futurists, business people and technology leaders. Below Richard Acreman, Partner at WM Reply, discusses the overvalue of automated processes and the future of ‘robots’ on the front lines of business.

There are variations on the theme depending on your perspective, like “robots are stealing our jobs” or, at the other end of the spectrum, “robots will create a revolution in creative freedom.” But while all of these predictions will probably have a strong element of truth to them over the long term, they’re also the source of a dangerous misconception in the here and now: that businesses don’t need to worry so much about talent.

Broadly speaking, the exact opposite is true, but if it’s a misconception you share, you have some prestigious company. A major piece of opinion research recently revealed the difference between the value of human capital vs. physical capital to the economy now and in the future. As part the study, two thirds of CEOs and top leaders at global firms revealed that they believed technology would create greater value for their organisations than their workforces would. Almost half believed that automation, AI and robotics would make their workforce “largely irrelevant” in the near future.

In fact, the economic modelling that accompanied the research showed that human capital would be worth more than twice as much. The key distinction is having the right skills and the right people, who are valuable both in their own right, and as the people who are going to enable the technology to enhance their organisations’ value. That means engaging with, supporting and learning from the best workers to ensure that their knowledge and expertise remains within the business and can continue to unlock the business’s potential as the workplace changes.

A key battleground

Unsurprisingly, the first place that this issue is flaring up is on the front lines of business – among the customer facing or service delivering staff that form the grass roots of most organisations. It’s this audience who are often the most threatened by automation and it’s also this audience who are traditionally most neglected by their companies.

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As the above study shows, it’s all too easy for businesses who have never been particularly strong on engaging their front line workers to take technology as an excuse for inaction or to make the situation even worse, but overestimating the scale of the change or the speed of the transition means that those with this attitude will likely have years to regret their miscalculation before anything close to the future they’d imagined comes to pass.

Why front line workers continue to be so important

Looking at it from both sides helps to clarify how misleading it can be to think that technology is going to make staff less relevant.

If we assume that technologies that are good enough to fully replace staff are here already, or very close, then we might also make the assessment that the best staff will be needed to help inform the processes and approaches of that technology, look after it, and stand in for it when it goes wrong. Not to mention the cases where it doesn’t have the answer, or is dealing with a customer who insists on human interaction.

If we assume that the technology isn’t here yet, but is coming, then this group will remain essential to the work, but exist in a heightened state of anxiety about their future with automation seemingly in hot pursuit of them. They will therefore need not just a decent level of support and attention from the business (as should be standard), but also a certain amount or additional reassurance, the absence of which might well be seen as their death knell.

People and technology in perfect harmony

What then is the short-term answer? And how can companies ensure that they are not only engaging with, but also getting the most out of their front line workers, so that the technology will have to work even harder to offer a quantifiable advantage?

That’s probably something that’s best explained with some specifics. Bea Tartsanyi, enterprise innovation strategist at Sideways 6, talking at a recent event focused around Microsoft’s Yammer, pointed out that the value of grassroots innovation to a business is immense. With the analytics that her team is developing, they’re starting to get a handle on just how important that is. She’s proving that using tools like Yammer to access the knowledge and learnings of those on the front lines – those who ordinarily might not have much of a voice internally – is not only one of the most effective, but also cost efficient ways of driving innovation.

Bea also pointed out that companies are always looking at ways to bring the voice of the customer firmly into the business. To learn from the customer’s needs and to understand the best practice approaches to meeting them. What better way to do that than to directly connect front line workers to the centralised organisation through the engaging, non-hierarchical social tools like Yammer.

It works both ways: Front line workers can easily flag some of the everyday challenges they face for senior managers to tackle, while those at the top can offer up the challenges they face. Whether those challenges come from a loyalty, cost or another perspective entirely, allowing those at the coalface to share their experience of overcoming those challenges on an individual case basis will feed into an organisation-wide benefit.

Ultimately, automation will have an important role to play, but we’re a long way from that role superseding the role of front line workers. Giving those on the ground the tools to excel at their own jobs while also feeding into the wider strategy is an immediate answer to many of the challenges that companies face. Given how much front line workers – properly enabled – can contribute, ignoring the people-based solutions to those challenges in favour of the vague idea that up and coming technologies will fill in the gap is like saying you won’t buy an umbrella because at some point in the future it might not be raining.

From AI to all things IoT, Russell Bennett, Chief Technology Officer at Fraedom, discusses with Finance Monthly the top five technologies that are already making waves in the banking sector.

Over the past five years, technology has fundamentally changed how the financial services sector operates. Many retail banks already successfully cater to customers’ digital needs. Business banking is now beginning to follow retail’s lead – and here we outline five of the top technologies transforming commercial banking today.

1. Biometrics and security

When adopting new payment methodologies, banks must strike a balance between ease-of-use, ease-of-access, and security. We’ve already seen that consumer payment methods using biometric authentication becoming mainstream and it won’t be long before corporate clients expect the same.

Extending this functionality into corporate cards has the potential to make commercial payments more seamless and secure. Mobile wallets that defer to personal attributes to make secure payments on cards offer a potential route forward.

2. Artificial Intelligence

Automation is dramatically increasing the number of financial transactions in an organisation. However, while it can track and store more processes than humans can – and more accurately – it currently can’t provide the next level service many clients are coming to expect of their financial partners: planning and modelling.¹

AI is rapidly establishing itself as the missing piece of the puzzle that takes the data flows created by automated transactions and knits them together to discover patterns. All this is important to commercial banks because patterns in spending and efficiency can potentially deliver valuable insights to help clients improve their financial health.

3. APIs

Customers’ demands, and expectations are moving rapidly, so there is growing pressure on the banking industry to provide new, easy-to-use, frictionless digital services fast.

Application programming interfaces (APIs) provide the technology to exchange customer data with other parties in a simple and secure way², facilitating rapid innovation in products and services. Creating new applications such as voice banking, P2P, loan processing and risk management and using APIs as building blocks, is now seen as the best way to keep up with the innovation challenges facing the financial industry.

Fintechs have dominated the API landscape by creating apps that have challenged and often surpassed solutions made by the banking industry.

To keep pace, banks now need to either invest heavily to develop this technology themselves or partner with fintechs in a bid to be more effective and efficient.³ By working together and taking advantage of APIs, banks and fintech firms can enhance the customer experience much more than either entity could do on its own.

4. ePayables – Crossing over from the Consumer to the Commercial World

The use of different payment types is partly a response to the consumerisation of our financial experience. Corporate clients can’t understand why payments should still be a laborious process of raising invoices and purchase orders, requesting printed cheques or bank transfers and creating lengthy payment terms.

Instead, the immediacy of a card – real, virtual or embedded in an app – ties all the above elements together. It gives unsurpassed traceability and is easy to add to financial management software.

Historically, paying by using a card has been seen as a debt generator. However, using payment cards as a substitute for invoice terms makes them a useful tool both to enhance a company’s working capital positions and to improve traceability, security and the level of control that can be placed on business spend.

5. Expense Management Systems (EMS)

An Expense Management Systems (EMS) is just one of many tools that can be brought together into a single financial view, helping businesses gain greater control over expenditure. Unlike written expense policies and separate transactional management software, an EMS embeds expense policies into the technology, allowing real-time reconciliation and approvals to take place.

Up to now, retail banking has been ahead of the game in embracing new technologies and digital disruption but corporate banks are now grasping the need to take advantage of the latest technologies to ensure commercial clients reap the same rewards - from workflow efficiencies through to intuitive, mobile first experiences, a trend that is only likely to accelerate in the future.