Yet, this is something many businesses, SMEs in particular, currently struggle with. Below David Duan, Data Science Stream Lead & Principal Data Scientist at Fraedom, explains why AI is key to the relationship between banks and business.
Research from Fraedom found that almost a third of UK SMEs claim to have a clear picture of business spend at the end of each month but little visibility on a day-to-day basis. As banks begin to remedy these issues, we are seeing the introduction of more technologies that make use of artificial intelligence (AI) and machine learning (ML). Consequently, businesses could soon benefit from a wider range of capabilities, tools and controls with AI having a major impact on the following areas:
Control over spend
Through the use of AI, banks will be able to more accurately forecast how much credit businesses require and limits on spending will be set automatically, enabling banks to gain a better understanding of their spending. This can also be implemented within the organisation as AI will allow for credit limit redistribution based on what different employees regularly spend. This means that credit will be allocated in an optimal way, ensuring the amount of credit employees are given reflects their spend history. This ensures that those employees who often make large transactions are given the credit to do so, while those who use their company accounts for lower-cost transactions don’t receive as much, so as to ensure credit is being used to the greatest effect.
Account protections
As banks make better use of AI for fraud detection, businesses will benefit from improved security features. In these scenarios, AI will help businesses keep their accounts safe by detecting any anomalies in their accounts and fraudulent activities much quicker than previously possible. This works by the model having an understanding of what is ‘normal’ for each account or card and recognising patterns based on past transactions and behaviours. For example, if 99% of the transactions for one account happen Monday to Friday, a transaction that occurs at the weekend will be seen as abnormal and flagged as such. Of course, anomalous transactions aren’t always fraud. Often they’re just out of the ordinary, requiring some more investigation – flagging them to the business would certainly allow for this. With companies currently losing an average of 7% of their annual expenditure to fraud, these technologies will help lower incidences of fraud as shown by Visa’s use of AI reducing global fraud rates to less than 0.1%. In the future, AI could be used to detect fraud in real-time, stopping fraudulent transactions from being processed altogether.
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Expense management
In addition to providing banks with a greater degree of control and understanding of their finances, banks are also beginning to use AI to offer businesses extra tools and services. A prime example of this is expense management systems which use AI to simplify the expense process and reduce the amount of time employees and finance departments spend on such tasks. As with fraud detection, the system would establish patterns based on the employees historic spending behaviour. For example, it may pick up that once a week the sum of £5 is spent in a coffee shop which the user then applies a particular expense code to. Once this behaviour has been demonstrated enough times, it becomes a pattern. So, the user will no longer have to code the transaction themselves, the system would automatically identify the type of expense it is and code it correctly.
As the system establishes more patterns and understands what the user or business is doing, smart coding could start to be applied to a greater number of transactions. This would significantly reduce the amount of time spent manually sorting through and coding expenses as the employee then only has to check that the correct codes have been applied.
Ultimately, the use of AI and ML will help banks build up a more accurate picture of their business customers and result in the ability to automate more processes. In turn, this will provide organisations with a greater level of control over their accounts, improved visibility and a better understanding of their finances. As this is realised, businesses will begin to reap the rewards of their employees spending less time manually interrogating accounts and instead being able to focus on more value-adding tasks.
A former call center agent herself, Morales has benefited from her new job that is better paid and higher skilled than what she used to do. But will these chatbots end up replacing the livelihoods of millions of agents around the world? This is an episode of Next Jobs, a mini-documentary series hosted by Bloomberg Technology's Aki Ito.
Here Leigh Moody, Managing Director at SOTI UK, explains the full extent of AI’s impact on the mobile workforce of businesses across the nation.
At one level, automation and AI offer helpful solutions when recruitment is challenging, or where staff can be better utilized in other parts of an organisation. More broadly, there is no doubt that AI can add value to an increasingly digital workplace, and adoption is rising while some barriers remain. Consequently, one in five businesses intend to implement AI across their organisation in 2019.
Some organisations are already experiencing the benefits of AI as it becomes mainstream, and there is a definite fear that those who are not already experimenting with AI will be left behind. When evaluating the impact that AI will have on the future workforce, it is essential to explore practical use cases in business.
AI’s return on investment
For many companies today, AI represents an exciting opportunity to improve efficiency and enhance business performance. At an individual level, AI automation gives workers more options and the chance to be freed from routine tasks so that they can focus on bigger, more rewarding challenges. For many, this means working in a mobile, flexible manner facilitated by Internet of Things (IoT) technologies and remote access to data and work-specific applications and programs, most of which are in the cloud.
AI benefits workers and organisations alike, because it can optimise personal autonomy and convenience while reducing capital costs, especially where there is a Bring Your Own Device (BYOD) policy in place. In 2018, research for the UK government’s Department for Digital, Culture, Media and Sport found that 45% of businesses routinely allowed staff to use personal devices for work. But BYOD must be implemented with care, since it inevitably leads to a number of unknown and possibly unsecure devices being connected to an organisation’s network which can have dire consequences for security.
AI in the fight against cybercrime
At this point, however, key aspects of AI that make it attractive for business begin to interface with a much less ideal use of the technology. AI-powered cybercrime – which, ironically, often mimics enterprise-level AI applications – is a very serious and rapidly increasing threat to businesses and individuals alike. In 2017, just under half of all UK businesses identified at least one cybersecurity breach or attack, and this showed no signs of slowing down the following year. Hacking, phishing and malware are now key threats to businesses of all types and sizes in the UK.
Mobile workers are tempting targets for cybercriminals as mobile endpoints are often a weak point in an organisation’s cybersecurity system. Cyber criminals deal in data: on the dark web, the most mundane personal details can be bought and sold for cash. Greater volumes of data mean greater profit, so criminals have used AI to automate hacking to an industrial scale, harvesting massive volumes of corporate and personal data. Where those criminals deploy or threaten malware, ransomware and distributed denial of service attacks (DDoS), companies can lose vast amounts of their data at a keystroke, which is often permanent.
Even where corporate-owned mobile devices are mandatory, if they are not properly protected, criminals can use them as a vulnerable point of entry to the network, and BYOD environments carry an even greater risk. It is more difficult to control the exposure to dangerous websites or applications in non-work settings, which can put an unsecure network at risk.
Yet, while AI has fuelled this situation, it can also help solve the problems that are arising. This is because AI technology has been instrumental in the development of real-time security and device management solutions, which – like cybercriminals’ use of AI and enterprise automation – learns from past experiences, patterns of behaviour and incoming data, and responds intelligently and immediately to evolving threats.
If companies want to stay competitive, they have little choice but to expand their mobile deployment, while ensuring they are protected against evolving cybersecurity threats. By securing all endpoints under a single, integrated enterprise mobility management solution, companies can reap the full benefits of their mobility investments while enjoying the peace of mind that only real-time cybersecurity can bring.
These very questions are why more leaders, and in particular, CFOs, are turning to smarter technology solutions for help, specifically ERP platforms with embedded AI. CFOs find themselves with ever-expanding job responsibilities, all the while being asked to continue leading the extremely vital finance teams, but they have the same number of hours in a day as everyone else – so something has to give.
Therefore, automating manual processes will enable CFOs to regain precious hours, dedicate time to critical decision making and apply themselves to driving a competitive business.
They need to focus on big-picture decision-making based on strategic insights, rather than simple but time-consuming tasks. Technology enables this shift: AI or chatbot assistants built into ERP applications that handle less strategic work can be a game-changer, helping CFOs focus on driving results.
CFOs are ambitious by nature, they wouldn’t be where they are if they weren’t. However, they do need to keep the finance function up and running. If the majority of their hours are spent doing this, their ambition is not able to reach its full potential. Requisitions, purchase orders and vendor invoicing are not going anywhere. But using cloud-based ERP with embedded automation empowers CFOs with intelligent financial management capabilities that can handle the routine duties that are holding back potential productivity. This frees up the CFOs to focus on innovating and proving to the CEO exactly how valuable an advisor the CFO can be.
CFOs find themselves with ever-expanding job responsibilities, all the while being asked to continue leading the extremely vital finance teams, but they have the same number of hours in a day as everyone else – so something has to give.
The time is now to invest in this technology. CFOs are part of a unique group of people within a business who have access to data from every department, from sales to HR and marketing. In light of increasingly strict regulations and compliance laws, compiling data from all business units can be difficult as teams try to ensure they comply. CFOs are therefore in the unique position where they have complete oversight of the connected data and processes in an age where businesses are driven by data. This is a very important function for a business, and CFOs should be dedicating their energy to driving strategic business decisions from this position of insight, dedicating their productive hours and decision-making to the data they have at their disposal. At the end of the day, these insights translate into valuable guidance CFOs can give the CEO to help drive the business forward.
Becoming a strategic adviser to the CEO and the board by tapping into this ambition and reducing lost productivity can manifest itself in many different ways. For example, Football Club RCD Espanyol implemented a Cloud ERP platform to automate its financial processes. Joan Fitó, Financial Director, saw his finance team become infinitely more flexible by automating repetitive tasks. Productivity went up by more than 20%, while reporting time was reduced by 50% and there are over 25% fewer errors committed by his finance team. The team can now spend time focusing on analysing Club data in real-time to become more strategic in its efforts to become a globally-recognised name in the football world.
80% of an organisation’s transactions are processed in the back-office, the home of the financial team. So, as seen at Football Club RCD Espanyol, the opportunity is there for CFOs to lead the way to digitally transform and change operations for the better, with a clear path to make the most of being data and automation driven. This will make CFOs even more central to business operations, which is why they must be ready to make this jump now. It is an ideal time to showcase their ambition in combination with intelligent process automation to handle the energy-intensive tasks and take full advantage of the opportunities presented to drive the business forward. Taking the leap and implementing an ERP Cloud with a strong automated financial functionality is exactly the way to do this. It will ultimately enhance productivity and agility, allowing the CFO to be laser-focused on making the decisions that really matter – growing a successful business.
Machine learning and AI can have huge benefits to the financial department and could allow companies to create and tailor their models based on the data they have collated. This technology can dissect the data inputted and try to perceive the deviating patterns in this – a good example already prevalent in the financial industry is the analysis of payment behaviour in fraud detection. Machine learning is able to signal that someone is making payments from two completely different locations in a short period of time, which can indicate a fraudulent purchase. Though this is a common example of machine learning in finance, there are a huge amount of other significantly beneficial ways that AI and machine learning could be implemented – so, why are European firms not applying this as eagerly?
Well, there are a number of reasons as to why this could be the case, the most common being that there is simply a lack of know-how in this area. Accountants and finance professionals, of course, have extensive knowledge and expertise in the field of accounting standards, risk management, investment analyses and controlling, but not in the area of emerging technologies, machine learning or AI. Therefore, those in the finance department are not able to simply implement this technology and must look to external parties to help this transition – which can be timely and also a deterrent. But this is unjustified, as many CFOs could quickly master the basics of machine learning through training and not necessarily take on these roles themselves, but at least understand the technology.
Many CFOs could quickly master the basics of machine learning through training and not necessarily take on these roles themselves, but at least understand the technology.
Not only is there a lack of know-how, but there is also a lack of time for a CFO to implement this technology, or find a partner who is able to do so. A CFO’s job role usually focuses on value creation and protection, and transactional tasks too. Only once less time is spent on these is there the possibility for CFOs to focus on strategic tasks, such as implementing new technologies. CFOs tend to be extremely time-pressed individuals, until they free up time to focus on these strategic areas, or employ someone in the finance department to do so, it is likely that the option of applying these technologies will not be possible.
Infrastructure, company culture and the risk and governance surrounding implementing this technology can all have a profound effect on the possibility of companies doing so too. Not every company has the designated ICT infrastructure to store, analyse and structure data, and of course, the extra computing power and server capacity that are also required to do so. In a company where the financial department culture is not data-driven, it may be hard to convince the necessary actors of the importance of implementing data in financial practices; the management needs to support this area of focus. The risk and governance related to data issues is also a major concern for companies, whether it be related to security, GDPR or compliance, which means that many firms may be reluctant to pursue this avenue.
All these barriers that a CFO may be faced with when trying to implement data analysis and AI into their practices can, however, be overcome. Whether it is redistributing money to focus on technology in finance, employing external firms or internal actors with knowledge of the technology, or investing in software and infrastructure which can facilitate data analysis, these all are worthwhile tasks for a CFO to implement in order to benefit from this new technology.
In this pursuit to apply AI in the finance department, the CFO should continue to play an overarching role in the company, but also add advanced automation and machine learning to their list of tasks. There is a need to have employees that excel not only at accounting and financial knowledge but also at the ability to work with new technologies, including AI. A data-driven finance department will better position itself as a strategic business partner.
In this pursuit to apply AI in the finance department, the CFO should continue to play an overarching role in the company, but also add advanced automation and machine learning to their list of tasks.
In fact, there are four concrete applications of AI that could be seen currently in the finance department. For example, these technologies have the ability to quickly evaluate potential investment opportunities, by scanning and consulting annual reports and management reports of the companies on the list of their potential investments. This can help companies to quickly understand possibilities of profit growth in these investments and allow them to come to a much quicker decision on their potential investments.
Machine learning and AI can also be implemented to analyse mass social media messages regarding the company’s practices, products or services or current prices. This will help companies gather mass opinions of them in a short space of time and give them the ability to understand how to better streamline their financial services and offerings in the future too.
This technology can also predict future business issues as well, by mapping the network and history of potential suppliers and collaborators. AI can provide a specific and sophisticated understanding of a company’s public image, which could help the company avoid aligning themselves with companies with the potential to have a negative image, and therefore save them money in the long-term by maintaining their positive brand image.
Every company looks to gain insight into the profitability of its customers, this AI technology can also help companies with predicting the potential reaction to new services and products that they are looking to offer. Therefore, companies are able to understand whether or not these will be financially worthwhile in the long-term, and whether customers will be likely to consume these.
New technologies such as AI and machine learning will have a profound impact on all business areas, including the finance department, and CFOs who look to embrace this as soon as possible will be one step ahead of their competitors. For the future of finance, it is important that the training of financial students and current employees includes a greater focus on technology - how to implement this and its impact on finance. This is something that education institutions like Vlerick Business School have adapted to, offering more and more technology-focused modules in their finance programmes and ensuring that the next generation of CFOs has a strong knowledge of both accounting & finance and technology.
The financial sector has been especially keen to reap the benefits that Artificial Intelligence (AI) technology can provide, but there are still some fears that these innovations will cause huge job losses and remove the human role from businesses. Here Frank Abbenhuis, VP of Strategic Alliances at Axyon.AI, discusses the current AI landscape, touching on some the key steps ahead.
What is AI now?
Over the past 20 years, AI adoption has increased dramatically, due to some key shifts in the market. Firstly, technology has advanced hugely – not only in its ability to process large quantities of information in a fast, accurate manner but also in how inexpensive computing has become. The data that AI utilises has also become hugely prolific, with both individuals and businesses producing huge amounts of data on a daily basis. The result is not only cost effective and fast, but also incredibly accurate.
However, even with this foundation, AI would not be witnessing increased adoption if it were not practical for financial services. Through AI, financial institutions are now able to offer an improved customer experience, identify new sources of business growth, determine more effective models to follow, and develop broader aspects of the organisation: from enhanced productivity to better risk management.[1]
AI as a tool
This increased adoption of AI has inevitably caused concerns over job security, with fears that jobs will become automated as a result.[2] However, the reality is that AI has come at an ideal time to address the demands that banks are facing.
For example, the customer experience is now a key focus in building a business’ reputation. To remain competitive, companies need to move away from the ‘back office’ process-driven tasks and increase their client engagement strategies. As such, the more that AI can support these internal functions, the more that the business invests in building those vital client relationships.
Naturally, there are also concerns around how AI can be implemented. Fortunately, banks and other businesses in the financial sector often have enough historical data available to train an algorithm and run the task automatically. If this automated function is then combined with human oversight, the business can improve the quality of advice given to clients. In this way, AI no longer takes over a person’s role, but enhances their functionality in the business.
Making the most of data
Even with this progress, there are still certain areas in financial services where AI can be enhanced. For example, syndicated loans desks have a wealth of historical market data that is not leveraged to its full potential.
If AI were implemented here, algorithms could be used to analyse all previous deals and produce the likelihood of specific actions being taken. In this scenario, AI would not only be able to access which investors participated in every syndicated loan, but also the high-level structure of these loans – something that would be impossible for a single human mind to achieve.
This is just one example of how AI can enhance those in capital markets and asset management. The sheer amount of data that these sectors produce make them ideal for the predictive capabilities of AI. The only impact this level of automation will have on those working in these industries is smoother processes and improved output.
With all the fear that can surround new technologies in financial services, AI is set to only improve how people work in the sector. Through taking advantage of huge amounts of data, AI has the potential to streamline internal process and increase overall output – with the added benefit of improved accuracy and reliability.
[1] https://www.mckinsey.com/industries/financial-services/our-insights/analytics-in-banking-time-to-realize-the-value
[2] https://www.theguardian.com/money/2019/mar/25/automation-threatens-15-million-workers-britain-says-ons
To put this into perspective, the U.S. banking system alone held an estimated $17.4 trillion in assets at the end of 2017, whilst it also generated a staggering net income of $164.8 billion.
Banks are set to become more profitable in the future too, with advanced technology such as artificial intelligence (AI) expected to introduce more than $1 trillion in savings by the year 2030. This highlights the impact that technology is continuing to have on banking, with this relationship growing increasingly intertwined with every passing year.
In this article, we’ll explore this further whilst asking how the most recent innovations are impacting on banking in the digital age.
1. It has Ushered in the Age of Digital and Mobile Banking
Whereas banking used to require standing in queues and liaising with tellers, most transactions are now completed through digital means. In fact, an estimated four out of every 10 UK customers now bank using a mobile app, and this number is set to increase incrementally in the years to come.
So, whether you want to make an instant payment, transfer funds or open a brand new account with a service provider such as Think Money, the quickest and most efficient way of doing this is through digital means.
Technology is also making digital banking increasingly secure, with methods such as 2-step authentication having transformed the space in recent times.
We’re also seeing a significant rise in the use of biometric security methods, including advanced techniques such as fingertip authentication and facial recognition. These options provide the ideal compromise between high security and a seamless customer experience, and this something that remains at the very heart of banking in the digital age.
2. It’s Using AI to Improve the Customer Experience
We touched earlier on AI, and how this will enable banks to make considerable savings and become more profitable in the future.
AI is also having a considerable impact from a consumer perspective, however, especially in terms of the banking experience that they enjoy.
Take the use of chatbots, for example, which can enhance the onboarding process when positioned as helpdesk agents. More specifically, they can answer the most basic and commonly asked questions and anticipate popular requests, enabling customers to resolve their queries as quickly as possible.
AI can also afford bankers a more detailed look at their customers’ behaviours and financial history, making it easier for them to provide real-time insights and offers that offer considerable value.
3. It’s Improved Data Protection in the Banking Sector
In the first half of 2015, it’s estimated that around 400 data breaches took place in the U.S. alone.
This number has fallen in recent times, as banks have identified the core issues that compromise customer details and introduced measures to provide more robust data protection.
Aforementioned biometric and 2-step authentication techniques have helped to secure users’ passwords, for example, whilst phishing scams and malware are also being combatted by 128-bit encryption and higher.
As a customer, you can also take advantage of secure wireless connections to safely access your bank accounts in the modern age, negating the risk posed by public networks and unsecured Wi-Fi hotspots.
Kim Hau, Senior Proposition Manager for ONESOURCE Indirect Tax, Thomson Reuters explains what emerging technologies actually mean and how will they help today’s organisations to interact with tax regimes around the world.
Tax regimes, legislation, and government systems are evolving. The shift towards real-time interaction will not slow down anytime soon and this is impacting the tax departments of businesses around the world. As emerging trends change, the way government systems are deployed and the technology they use will impact upon tax legislations around the world. Multinational organisations need to keep pace and embrace technology while ensuring they still comply at the speed of business.
If businesses aren’t familiar with the acronyms RPA, AI or even heard the term Blockchain then they need to learn about them, fast. They are no longer phrases from a futuristic text but actual developments in today’s technology and businesses.
1. Robotic Process Automation (RPA) is, essentially, software robots that mimic human tasks across applications in a non-invasive way. If a process can be documented for someone else to follow especially if it’s a potentially error-prone process, high-risk or manually intensive, or done so frequently that it’s just not a good use of time, then it’s a good fit for RPA. Companies will find that some of their tax processes will fit this bill and free up resource to work on more important tasks.
2. Machine Learning and Artificial Intelligence (ML/AI) are two concepts often related and used interchangeably. Machine learning generally describes algorithms used by machines to teach themselves. Artificial Intelligence is used more broadly to describe the ways in which machines can perform tasks intelligently. Simply put it’s about taking a big population of data, learning patterns about that data, and then revising and training algorithms automatically to get better over time. Machine learning doesn’t have to be as sophisticated as self-driving cars. Think about how Amazon, Google, and Facebook use machine learning algorithms to improve recommendations, suggestions, and news feeds. Some of those capabilities are being applied to finance and tax today, particularly in areas where accurately categorising, grouping, or classifying large volumes of data frequently is part of the process. Ingesting data from a dozen different ERP systems and getting it lined up for tax compliance and reporting is an obvious place where it can make a difference to business.
3. Blockchain has been integrating into the business world far sooner than many predicted and as such there is a growing belief that it will radically change the way in which value is exchanged and how items are tracked and traded. Banking, insurance, voting, land registries, real estate, and stock trading are all examples of areas and industries where Blockchain is likely to impact.
While much of the publicity around Bitcoin is related to hackers and the cryptocurrency bubble, much of the real Blockchain activity seen so far is centered on the distributed ledger itself and the ways in which it’s going to disrupt middle men, or intermediaries, by connecting the transacting parties directly. From a positive point of view it is believed that Blockchain will speed up transactions and reduce cost while reducing fraud and increasing transparency.
At its core, Blockchain’s a distributed ledger that records transactions — and many of those transactions will be taxable events which is why it matters to tax. The details around Blockchain are complicated but suffice to say there’s a reason so many governments and industries are actively experimenting with Blockchain projects.
From a tax point of view, it’s likely that Blockchain will impact the tax department via governments and tax authorities pursuing digital strategies around e-government and that technology used by tax to stay compliant will have to adapt to this evolution.
With these developments in mind multinational companies should focus on incorporating technology trends that will assist in managing tax requirements rather than just putting out fires when the next major tax initiative comes along.
HMRC’s Making Tax Digital (MTD) is the perfect opportunity for businesses to be proactive and developing processes that are nimble enough to adjust to change. Tax should focus on what it can control, like the preparedness of systems and the scalability of processes, in order to adapt to the next change. Today, keeping pace with specific rate changes and regulatory modifications is largely a function of tax technology platforms. With HMRC’s October deadline there’s never been a more obvious time to implement solutions that enable and empower tax departments.
Below, Peter Wallqvist, Vice President of Strategy at iManage RAVN, explores why a human-supported adoption of AI technology offers the safest and soundest solution to amending LIBOR contracts.
Unstructured information
Perhaps the biggest challenge lies in financial institutions’ ability to identify and quantify the contracts that need to be transitioned from LIBOR to alternative reference rates – within the existing remit of these documents. Despite financial institutions using reporting tools to capture contract-related data, typically nearly 80% of information is unstructured and locked up in documents. There is no searchable metadata for easy extraction. So, in the absence of visibility of the contract landscape, determining the volume of contracts that require repapering across the portfolio for LIBOR is an enormous undertaking.
With the problem quantified, financial institutions then must identify the relationships that are impacted. For instance, which of those contracts have fall-back provisions, which agreements will require renegotiations and, if so, what the amendment process will be, and so on. There are substantial financial risks to institutions if contracts are not accurately amended.
Staggering legal cost
A manual approach to such as a project is realistically not an option. Not only will the legal fees be exorbitant, but completing the transition in time is almost impossible given the vast expanse of the contracts landscape in financial institutions.
Even conservatively, financial institutions are looking at legal costs in the region of millions of Pounds for a LIBOR repapering undertaking.
The traditional approach towards this repapering project is to ask the financial institutions’ panel law firms to undertake the entire exercise – from identifying the contracts that need repapering through to undertaking the individual tasks for successful completion. Even conservatively, financial institutions are looking at legal costs in the region of millions of Pounds for a LIBOR repapering undertaking.
The human-machine partnership offers a solution
With the aid of technology, the LIBOR contracts repapering exercise is achievable. By adopting practical Artificial Intelligence (AI) techniques, financial institutions can undertake this project safely, accurately, cost-effectively and in a timely manner.
Foremost, the key to any AI project is the digitisation of contracts. Making the data machine-readable, means that important information becomes searchable and ready for the application of machine learning techniques, to automate the identification and extraction of key data to be triaged and interpreted, as necessary.
AI can deliver a structured methodology to manage the process, end to end. Institutions can then train their application to extract the contractual information in a format that is machine-readable and easily consumable through existing reporting tools.
This scenario represents the ideal human-machine partnership. The technology will automate the tedious and time-consuming manual cognitive processes that are economically unfeasible or even impossible to complete in the current timescale – all the while supported and guided via human intervention and oversight.
To elaborate, financial institutions can use different data points to determine the scope of their repapering exercise. A search for contracts using the ‘termination date’ data point, within minutes, lists all the contracts expiring before 2021, which the institution can disregard, and focus on the remaining contracts that require attention.
Making the data machine-readable, means that important information becomes searchable and ready for the application of machine learning techniques, to automate the identification and extraction of key data to be triaged and interpreted, as necessary.
For the contracts continuing past 2021, AI technology can be taught by humans to read, extract and interpret other critical business information and apply ‘decision tree’ logic to support the repapering effort. This will result in automation where needed.
To illustrate, an AI system can be taught to identify whether the contract contains a reference to LIBOR. For the contracts where the interest rate is based on LIBOR, the AI application will automatically ask if there is a fall-back provision in the contract for situations where LIBOR is unavailable. For contracts where a fall-back provision exists, the application will interrogate the relevant documents to see if the stipulation is broad enough to deal with the permanent discontinuation of LIBOR as an industry standard or limited to something temporary – such as a computer glitch rendering unavailable the Reuters screens on which the rates are displayed. Assuming at that point, the fall-backs are neither sufficiently broad nor specific to cater for LIBOR replacement, then clearly an amendment is required. In such instances, the system will mechanically highlight and classify the change process that is needed to be followed – i.e. the need for unilateral or multi-lateral renegotiation, voting processes (e.g. for syndicated lending where decisions are made pursuant to different voting thresholds being met), in what timeframe, whether there is a ‘snooze you lose’ period after which the contract parties’ agreed benchmark position will automatically apply, and so on.
It’s worth highlighting that digitising and teaching a machine learning system to extract many of these data points for LIBOR will also prepare financial institutions to efficiently manage similar projects in the future too. They will be able to re-use the same machine learning models for new purposes – for example, to extract covenants for commercial comparison to expedite drafting and negotiation or better understand ‘what’s market’ down to the wording of a provision.
A human-supported adoption of AI technology, by far offers the safest and soundest solution to amending the LIBOR contracts to meet the 2021 deadline.
Website: https://imanage.com/product/ravn/
While many traditional methods and procedures are still in play, firms are adopting modern and innovative strategies to draw in a hipper and younger, yet more demanding, clientele.
From new technologies to fresher approaches to client service, here are the top trends that are sweeping and changing wealth management today.
A Digital Industry
2018 witnessed a firm-wide and strategic digitalization of wealth management companies. The trend continues to this day as big and small firms reshape the different aspects of their business to embody the change.
As the industry prepares for a generation of younger and tech-savvy clientele, integrating digital strategies to their marketing efforts and creating more efficient client-advisor interaction channels become essential.
Firms that have already taken the lead in implementing a centralized digital management strategy are raising the bar and driving competitors to do the same.
In the words of FinTech Advisor and ASEAN/India Retail Banking and Wealth Management Expert, Arvind Sankaran, “We are witnessing the creative destruction of financial services, rearranging itself around the consumer. Who does this in the most relevant, exciting way using data and digital, wins!”
Sustainable Investing Is Here To Stay
The Institute for Sustainable Investing’s 2017 “Sustainable Signals" report showed that there is a growing interest in sustainable investing and the adoption of its principles among investors. What's even more interesting is that millennials are taking charge.
Millennials take sustainable to the center stage as they search for more socially and environmentally conscious investment opportunities.
This increasing demand for sustainable ventures will continue to push wealth managers to take impact investing more seriously. Thus, the next years may see financial advisors incorporating the environmental, social, and government (ESG) philosophy into their services and financial planning approaches.
The Rise of AI and Robo-Advisors
Taking into account the millennials’ fascination with anything technologically-inclined, it’s not at all surprising that the idea of Robo-Advisors resonated and connected with young investors quite well.
In a statement, the automated investment service firm, Wealthfront, commended the ability of software-based solutions in delivering investment management services at a “much lower cost than traditional investment management services.”
While it can be argued that Robo-advisors can never replace competent human financial advisors in terms of creating customized long term investments or tax and retirements plans, the competition between automated and human advisors have benefited the clientele. For one, it drove the costs asset management down. More importantly, it forced financial planners to step up their game and prove their worth.
Basing on current trends, digital assistants (Robo-advisors, chatbots, and other forms of AI interactions) will continue to play a significant role in empowering client-advisor experience. We might be looking at a future where AI becomes a fundamental element in crafting large-scale hybrid advice offerings.
A Focus on Customer Experience
2018’s World Wealth Report identified that many clients think the relationship they have with their financial advisors and wealth managers falls short of their expectations and can use some improvement.
This is clearly a heads up for advisors and managers out there. In the wealth management industry, customer experience holds great weight for clients. For most investors, client-advisor relationships are critical because they believe in their in-depth implications on the realization of financial and life goals.
These days, investors are gradually witnessing moves towards better customer satisfaction as wealth management companies embrace automation and hybrid models of financial management, and re-engineer their strategies to satisfy demands and ensure that customers have the best possible experience during interactions.
With the new breed of investors putting a prime on user experience and opening themselves to the possibility of switching to other wealth management providers if their expectations aren’t met, the best way forward is to innovate and shift to strategies that put the client and their needs at the core.
.While business process management (BPM) has been around for decades, hype around AI has led many execs to jump into implementing AI rather than bothering to deploy Robotic Process Automation (RPA) systems. In their minds, there’s little point implementing something that will likely be replaced in the near future.
However, increasingly BPM, RPA and AI are becoming the foundational layer for digital transformation that doesn’t require a costly wholesale rip and replacing a business’ IT systems. Combining existing process management with both RPA and AI when looking to realise value-driven transformation ultimately creates a more efficient and productive outfit. Andrew Tarry, Head of Software Engineering at 6point6, outlines a few examples of why this is the case.
Existing BPM systems
Business process management forms the basis of most workflow orchestration within businesses. From the classic route and queue orientation of service management to everything we would traditionally think of when interacting with businesses that deliver customer service or financial month - or quarter-end close reports. At its most fundamental, BPM forms the basis of how humans interact with predefined process flows of data in order to manage work. It is the system that keeps information flows consistent and provides those managing said information with a ‘heads up’ display on how the overall operation is running.
However, increasingly these systems are implemented and then left to run regardless of whether the businesses’ needs change. When systems are not properly maintained they become brittle and can create issues with interdependencies further down the process line. For example, a change to a retailer’s stock inventory at the warehouse where the system has not properly been maintained could have a significant knock-on effect for their order fulfilment success later down the line; impacting negatively not only on the internal processes but also the customer’s experience of the brand.
RPA’s ability to expose underlying APIs means that routine decisions made by humans can be offloaded to an AI system and then passed directly back to the RPA agent – significantly streamlining processes and thereby reducing costs.
Overcoming the temptation to ignore RPA
RPA tends to be discounted for the more hyped AI during digital transformations because it is often seen as an integration of the last resort for systems that cannot be automated by other means. The general hesitation is that if the system will be replaced by a better AI-based one in the near future, then why invest in RPA in the first place? Replacing a major system tends to be part of a big project that will invariably cost a lot of money, take a long time and involve a significant amount of risk. Further, many organisations have tightly coupled software and business processes, which means they cannot evolve in real time as their business environment does.
However, RPA actually presents the opportunity to implement an ‘automated swivel chair’ integration. For example, a service agent such as a call centre operator could enter details into a CRM solution while simultaneously enabling this data entry to be inputted to the order management system, saving seven minutes per transaction on average. In a high volume call centre, these savings would drop straight to the bottom line.
Further, RPA’s ability to expose underlying APIs means that routine decisions made by humans can be offloaded to an AI system and then passed directly back to the RPA agent – significantly streamlining processes and thereby reducing costs. In insurance for example, in replacement of a claims agent in a call centre, an RPA-backed claims management system could be exposed as an API, presented as a mobile application to customers. The claims information gathered through the API could then be passed to an AI system that would make the claims payment decision on the fly without the need for human interaction. In high-volume, low-value claims environments such as travel insurance claims, this would be cost-effective and would reduce the cost of attrition claims; it would further give back much needed time for claims agents to deal with sensitive and high-value cases.
Far from being a waste of time, through investing in RPA you can achieve the required results in much shorter time spans and could even delay the replacement of the legacy system altogether.
Senior decision leaders must think carefully about how to remove legacy process management systems without negatively impacting on overall productivity and decision making. Far from being a waste of time, through investing in RPA you can achieve the required results in much shorter time spans and could even delay the replacement of the legacy system altogether. A pragmatic architecture, harnessing BPM, RPA and AI together brings to the fore the core benefits of value-driven transformation.
More than a quarter (27%) of organisations surveyed by Gartner expect to adopt some form of artificial intelligence or machine learning in their finance department by 2020. With so much data, from so many sources, machine learning is often the only real way financial professionals can successfully sift through the noise in a world of information chaos. Jonathan Barrett, Managing Director of Dataminr tells us more about it.
Before delving into the details of exactly how machine learning can benefit financial professionals, it’s important to have a clear understanding of exactly what artificial intelligence (AI) is, what machine learning is, and how the two fit together. As stated by Oxford University, AI is concerned with getting computers to perform tasks that currently are only feasible for humans. Simply, it is human intelligence manifested by a machine. Within AI exists machine learning. This is when a computer is programmed to make decisions, learn from outcomes and adjust, in a way of self-improving, according to their environment.
As an industry that needs to remain at the forefront of adopting new, and better, methods of working, financial professionals, in particular, are seeing a huge surge in the use of this technology. By bringing together multiple data sets, machine learning can take on the process of sifting through vast amounts of data and provide people working in the financial sector with greater insight to inform critical decisions. In doing so, machine learning is proving to be invaluable.
Embracing the new
A recent report from McKinsey revealed that up to 50% of tasks we tackle at work today could be automated by 2055. But this is not a reason for worry. Machine learning on its own, with no human supervision or influence, is not where the future of finance is heading. And rightly so. Rather, this new technology opens up previously untapped opportunities for a wide range of finance professionals to prepare for and excel in roles of the future -- roles that we have not yet even imagined.
More than a quarter (27%) of organisations surveyed by Gartner expect to adopt some form of artificial intelligence or machine learning in their finance department by 2020.
Machine learning is already making its mark on industries that for decades have depended on technology to automate tasks and drive efficiency. Furthermore, we are beginning to understand the true value of how this technology can be used to reduce workloads, particularly in relation to the necessary but repetitive types of work we face in many roles. When used in this way, machine learning has the real power to free up time for strategic thinking and research. It can empower better decision-making by allowing financial professionals to focus on more valuable tasks, such as business growth and retaining vital talent. In this way, machine learning technologies can prove to be the crucial advantage against competitors.
As with most technologies, there still remains limitations with machine learning, especially within finance. The financial markets -- and the data they produce -- are complex and can change within a split second. They form a web of moving parts, influenced by factors both inside and outside of the financial sectors. Anything from changes in regulation to unpredictable world events, such as political risks or natural disasters, can cause a shift in market mechanisms.
Machine learning models are perfectly capable of predicting and taking certain risk elements into account but can fall short when it comes to these kinds of uncertainties. This makes it difficult to rely solely on machine learning to provide accurate or wholly reliable information when making financial decisions, especially within the context of investment strategies. In this instance, if there is no human input, machine learning could create unwanted and unseen risks.
When used in the right way, machine learning can complement human expertise. We need the creativity, emotions and the ability to form a point of view that only humans possess. But machine learning can mitigate the more repetitive and time-consuming elements of work, enabling people to be more productive, innovative and add new value. So we shouldn’t fret that machines will take over the role of humans in the financial workplace. We instead need to look at the opportunities to enhance the power of both.
Evolving skills, evolving opportunities
For example, hedge funds are hotbeds of new methods and platforms. Traders in this area are often turning to the quantamental approach, blending algorithmic-based and human-based decision-making to generate better results. Artificial intelligence is increasingly being used to reduce unnecessary information while at the same time draw relevant data together, with alternative and unstructured data playing a prominent role.
Machine learning models are perfectly capable of predicting and taking certain risk elements into account but can fall short when it comes to these kinds of uncertainties.
In turn, financial professionals are in a stronger position to give an insightful and accurate analysis. This means they can better understand the investments they are making and the strategies they are employing.
Other financial professionals, such as traders and investors, are increasingly relying on real-time information from alternative data sources to gain new insights, assess situations quicker and enhance their decision-making. The sheer amount of data that traders are faced with is vast and overwhelming. Utilising machine learning systems that can make sense of the information chaos and at a rate simply unachievable by humans allows traders and investors to stay ahead of the game.
Machine Learning and humans: a coexistence
Machine learning is revolutionising the finance industry. On its own, it is not enough to provide an entirely accurate and reliable picture upon which pivotal financial decisions can be made. But, used in tandem with human oversight, insight, and expertise, machine learning empowers businesses and individuals across the financial sector to make faster, smarter decisions that can generate new business value or drive revenue.
About Jonathan Barrett:
Jonathan Barrett is European Managing Director at Dataminr, an AI-enabled platform that discovers, distills and alerts on activity across publicly available data sources, enabling professionals to know and act on high impact events earlier. Jonathan has over 25 years of experience in the tech industry and is passionate about transforming businesses that have the potential to change the world.
