AI software can read market trends and patterns faster than humans and provide you with a more in-depth analysis of the market.

Many platforms offer a fully automated service, where the AI will trade on your behalf. You can also find a range of third-party AI bots in the MQL5 marketplace. Check the official site of the company that is providing the best-automated service. 

Trade Ideas

Trade Ideas is a comprehensive AI trading tool that provides advanced stock scanning, charting, and automated trading services. It is compatible with most brokers and offers a variety of high-quality data sources. It also features Holly Artificial Intelligence, which helps traders identify unique trading opportunities. It is ideal for advanced traders who are familiar with financial markets and trading strategies.

The platform’s powerful AI-powered market scanner, Holly AI, automatically analyzes past market action to find potential trading opportunities that meet a trader’s risk and investment objectives. It can also track a user’s portfolio performance and identify risks.

Its unique Compare Count Window allows users to visually compare strategies side-by-side and can be used for both long-term and short-term trading. It is a valuable feature that makes Trade Ideas a standout among other market scans. It also offers a wealth of support resources, including training classes and a trading education library. This demonstrates their commitment to growing a knowledgeable trading community.

MT5

MT5 is an evolution of the popular MT4 platform, which offers advanced trading tools for forex, commodities and index instruments. The platform is highly adaptable and can be accessed from all types of devices including mobile phones, tablets and laptops.

The MT5 platform is designed to facilitate algorithmic trading with a robust suite of automation and EA tools, which can be purchased from the MT5 Market. The platform also allows traders to create their trading programs using the custom programming language MQL5 and a built-in Policy Editor.

The platform provides an extensive range of analytical tools, including 38 technical indicators, 21 chart timeframes and 44 graphical objects that can be used to identify trends and patterns. Traders can also access a wide variety of news feeds and economic calendars through the MT5 terminal. MT5 is a 64-bit, multithreaded platform, which means that programmed strategies can run faster than on MT4. This makes MT5 a better choice for those looking to execute complex trading algorithms.

Trading Technologies

There are a few things to look out for when choosing an AI trading platform. First, it’s important to consider the type of trades the platform offers. For example, some offer copy trading, which allows you to mirror the investments of an experienced trader. This is a good option for those who aren’t comfortable with allowing an AI to trade on their behalf.

Another thing to look out for is an AI that offers real-time signals for digital assets. This can help traders make informed financial decisions based on their goals and appetite for risk.

Finally, you should check that the AI trading platform offers backtesting capabilities. This involves testing the trading rules against historical market data and assessing their viability. This can save you from making costly mistakes and ensure that your AI trading strategy works as intended.

But it is also embracing some of its greatest opportunities, enabled by data ubiquity and high-speed processing. You can visit the official site of Ethereum Code to learn more about artificial intelligence's significant role in financial markets. 

AI can process large volumes of structured and unstructured data much faster than humans can, thereby helping traders make better decisions.

Analyze data and Make Decisions Faster

In a market where profit opportunities are ephemeral, it’s important to have a trading strategy that can react quickly to change. As a result, many traders now use AI algorithms to analyze data and make decisions faster than humans can. This technology can also help reduce the amount of time that traders spend on administrative tasks such as calculating trade costs and identifying market trends.

Using natural language processing, AI systems can also analyze textual data and extract valuable insights from news articles and social media posts. This technology can help identify sentiments and emotions, which may be overlooked by human analysts.

The integration of AI into financial markets has had both positive and negative consequences depending on how it is used. While it can improve the accuracy of predictions and reduce risk management costs, it also poses new challenges in terms of transparency and accountability. Consequently, it is important to consider the impact of AI before implementing it into trading strategies.

Effective Way to Increase Profits

Big data analytics is transforming many industries and financial markets are no exception. Currently, the world creates 2.5 quintillion bytes of data every day and this huge amount of information can be leveraged in a variety of ways to increase profitability.

AI algorithms can help to analyze large amounts of data and identify patterns that can improve business performance. For example, using natural language processing to read and understand news articles can enable a faster and more efficient research process for investment opportunities. Another use is in high-frequency trading, where AI algorithms can recognize trends and patterns more quickly than humans and therefore make trades more efficiently.

Respondents to this survey indicated that ML and AI are becoming essential facets of contemporary finance, aiding in refining decision-making and optimizing resource distribution (Table 2). Algorithmic trading and risk management surfaced as primary areas for ML and AI applications, reflecting the growing trend of integrating cutting-edge technology into financial markets.

Forecast Future Events and Trends

Predictive models are a subset of data analytics that forecast future events, anomalies, trends, and patterns using historical and current data. These models are often created through statistical algorithms, and some of the most popular include linear regression, logistic regression, decision trees, and neural networks.

These models can be used to predict anything from weather patterns and consumer sentiment shifts to credit risks and corporate earnings. They can help businesses identify opportunities for growth and make better decisions about what they should do next.

These predictive models are becoming increasingly useful for financial markets because of their ability to detect nonlinear characteristics and other complex relationships that humans cannot grasp easily. They can also be applied to large datasets, making it easier for companies to track customer behaviour and predict trends. This can help businesses create personalized products and services for their customers and increase profits. The models can work fast, too, so that business owners can get results in real time.

At the World Economic Forum this year, Microsoft CEO Satya Nadella said that Artificial Intelligence (AI) would become "mainstream" in "months, not years". Now that AI seems to have reached a point where it can permeate any discussion and any sector, and importantly, can be applied to a wide range of enterprise and consumer applications, we can be sure that tech giants will be looking to invest heavily in AI to remain competitive.

ChatGPT, a chatbot developed by OpenAI, has taken the internet by storm over the past month. Although it is not smart enough to replace humans yet, the bot can respond to natural language prompts and uses past conversation threads and information on the internet to reply to the user. A few days after its launch, more than a million people were trying out ChatGPT.

If an 'AI wars' between tech companies does occur in a bid to be the best, industry leaders providing the technology used to produce AI should reap solid benefits – and profits. Potentially even more than Microsoft or other major technology platforms.

Five major contributors to AI technology  

Nvidia (NVDA) is the world leader in artificial intelligence due to its quality product portfolio. The invention of graphic processing units (GPUs) in 1999 was an influential moment in the computing industry. GPUs are essential for AI to achieve its parallel processing capabilities, so Nvidia is still a key industry player decades later and is benefitting from the boom in conversation around ChatGPT.

Interestingly, ChatGPT currently runs on Nvidia's two-year-old A100 chip, not the latest H100 or 'Hopper', which was released late last year. The new chip will supposedly perform AI learning functions nine times faster than the A100 chip and output (the action of an AI responding to a question or other stimulus) 30 times quicker. It also promises 3.5 times better energy efficiency and three times lower total cost ownership. When ChatGPT adopts the new chip, it is clear its capabilities will improve significantly.

While Nvidia's revenue from gaming chips fell sharply in the third quarter from the impact the pandemic had on the video game industry and cryptocurrency bear market, data centre revenue grew by an impressive 31%. With the release of the Hopper H100 chips and the start of the AI wars, we expect Nvidia to produce significant returns in 2023.

ASML Holdings (ASML) manufactures lithography systems which are critical in microchip production and are heavily relied upon by Nvidia to produce their GPUs. ASML has a monopoly on a key technology used in manufacturing advanced semiconductors called extreme ultraviolet (EUV) lithography. After chip companies started producing semiconductors with new advanced specifications, there was a need for the extremely thin lasers that EUV technology contains. With AI continually making advancements, it will require chips that advance with it. ASML technology allows for these advancements and will no doubt increase purchases of its machines.

Taiwan Semiconductor Manufacturing Corporation (TSM) is the world's first dedicated semiconductor foundry and is another significant contributor to Nvidia’s GPU production, giving it a competitive edge. In Q4 of 2022, TSMC produced more than 56% of the world's semiconductors. The management team at TSMC stressed that its high-performance computing segment for AI customers is the reason for its optimism about the semiconductor market recovering in the second half of 2023.

Micron Technology (MU), a memory manufacturer, will benefit from the uptake of AI solutions due to the large amounts of memory and storage it requires. Currently, Micron’s results are in freefall, and earnings are likely to be negative over the next two quarters from the historic slump in PC sales and weakness in smartphones and consumer electronics, which unfortunately have outweighed the delicate supply-demand balance in the memory market.

With limited competition, Micron and SK Hynix have announced drastic cost reductions for 2023, which should help rebalance supply and demand in the second half of 2023. Additionally, Micron achieved technological leadership within its limited competition last year. In the past six months alone, Micron became the first memory manufacturer to release DRAM 1-beta chips and the first manufacturer of 232-layer NAND flash memory chips. As the current leader in this space, and thanks to other market players seemingly cutting back on production, Micron should benefit in the second half of 2023 and beyond as demand for memory-intensive AI servers dramatically increases.

Microsoft (MSFT) invested $1 billion (83m) in OpenAI in 2019, and now the cloud giant is reportedly in talks to invest another $10 billion (£8.34bn) in the company, which indicates great potential in this new and improved AI engine. Last week, Microsoft released the OpenAI service on its Azure platform, which allows developers to incorporate it into their software projects. In fact, Microsoft itself is looking to infuse its current software products, from Office to Bing, with ChatGPT capabilities.

Yet the use of such emerging technologies also continues to grow exponentially across a host of industries, according to McKinsey. However, financial services organisations are likely to be the biggest beneficiaries with the adoption of such technologies in terms of actual bottom-line business value.

A critical reason for this is that these companies already have access to extremely complex and large datasets, which are a requirement to create different predictive models from this type of technology. Such models are also hugely powerful. They can be applied across a wide variety of financial products and situations, helping these organisations to better understand everything from the possibility and probability of defaults on loans, to customer purchasing intention to detecting fraudulent transactions.

Even the BoE and FCA believe that ML technologies can “make financial services and markets more efficient, accessible and tailored to consumer needs”.

Despite such advantages, to really harness the power of AI and ML and put in place for projects that make an impact with everyday consumers, organisations within the sector do still face some very specific and sizable challenges.

Firstly, financial services is a highly regulated industry whereby personally identifiable information (PII) is required to be protected. Yet this does hinder collaboration as a huge amount of time is necessary to clean and compliance check this information. Due to this, a project’s timescale can really lengthen.

Secondly, despite collecting and managing such a wealth of data, financial organisations face some limitations with the information they have. Even when data has been prepared to develop AI solutions, the actual dataset itself may be under-representative and such limitations are the most cited major barriers that prevent finance organisations from utilising their data assets. up to three quarters (73%) of data actually goes unused for analytics by companies, according to Forrester.

In fact, it is actually quite common that the most valuable information for an organisation is hidden in an under-representative customer category. A biased dataset means the insights gleaned will also be biased. The knock-on effect of this can be quite damaging. It can lead to false assumptions about customer segmentation that leads to higher costs for the acquisition of customers (banks already spend over £279 each year on acquisition per bank account), inappropriate offers being made to customers, which ultimately makes them less likely to purchase, or worse.

Its biggest impact could come in the area of personalisation of financial products to everyday consumers.

Advanced approaches using AI and ML are helping to tackle these challenges. Synthetic data generation technologies have emerged as a highly credible method of protecting PII, while also eliminating the limitations that organisations are facing with their data. The technology, underpinned by AI and ML, constructs a new, entirely synthetic dataset from the original information, one that is highly statistically accurate (up to 95%) but crucially does not reveal individuals’ PII.

It could be transformative for the financial services industry, with organisations like JP Morgan already touting its potential.

Its biggest impact could come in the area of personalisation of financial products to everyday consumers. Undoubtedly such personalisation has improved from tactics such as the ‘Fresno Drop’ which saw over 60,000 pre-approved credit cards mass-mailed to consumers in the Californian city in 1958. However, AI and ML technologies are built specifically to extract insights from data which encapsulates consumers' preferences, interaction, behaviour, lifestyle details and interests. Not only this, but the technology is also developing to such a stage that it can spot and, in effect, ‘rebalance’ biased datasets.

When this approach is implemented accurately, research has found that synthetic data can give the same results as real data. Yet crucially the key benefits include full data privacy compliance and a major reduction in the time needed for product development and testing.

While the successful personalisation of offers, policies and pricing makes a large contribution to the revenues of the business, it also keeps customers happy as they aren’t being bombarded by irrelevant information. This matters hugely as McKinsey found that highly satisfied customers are two and a half times more likely to open new accounts and products with their existing bank.

Having access to such deep insights into all segments is not something that can be put off much longer as consumer behaviour, across generations, is undergoing radical changes already. Research from PwC found that half of younger consumers (those under 35) will open a primary bank account based on a trusted referral from friends or family, by contrast, however, one out of two consumers over 35 will choose a primary bank based on the local presence of a branch or ATM. Such generational differences need to be spotted quickly to keep a financial organisation in step with rapidly changing preferences.

While it is encouraging that more and more financial organisations are using AI and ML technologies, any approach to maximise data’s value must have a coherent strategy behind it. Used in the right way and with the right strategy in place, the opportunity from these technologies offers unlimited potential to financial services organisations.

Matthew Leaney, Chief Revenue Officer at Silent Eight, examines the issue that correspondent banking poses to the financial sector.

On the one hand, it has long been a key mechanism for integrating developing countries into the global financial system and giving them access to the capital they need. On the other hand, correspondent banking relationships are inherently risky for the global banks that grant access to the respondent bank’s customers without being able to directly conduct Know Your Customer/Customer Due Diligence (KYC/CDD) checks on them.

It’s not a small problem: make access too easy and you risk allowing billions of illicit funds through your door; cut off the relationships and you starve emerging markets of capital and drive their transactions into the shadows.

To its credit, the Financial Action Task Force (FATF) understands the dilemma and has provided continued guidance to clarify the issue. In its October 2016 Guidance on Correspondent Banking Relationships, it explicitly stated that its standards “do not require financial institutions to conduct customer due diligence on the customers of their customer (i.e., each individual customer)”. Rather, they require the correspondent bank to conduct sufficient due diligence on the respondent bank’s processes to understand the risk they present and whether the risk is acceptable within their risk management framework.

Still, many global institutions have decided over the past few years to “de-risk” by shutting down or curtailing their correspondent banking relationships in many countries. It’s easy to see why. It makes sense to exit a relationship when the risk associated with it exceeds your risk tolerance. But the solution doesn’t need to be this drastic. After all, correspondent relationships aren’t inherently bad, they just present a higher level of risk than the bank is willing to accept. Lower the risk and you’re back in business.

It makes sense to exit a relationship when the risk associated with it exceeds your risk tolerance. But the solution doesn’t need to be this drastic.

The solution is straightforward, at least in concept: lower the risk by increasing the effectiveness of respondent banks’ AML/CTF programs. This approach is exemplified by our partner Standard Charter’s “De-Risking Through Education” strategy, featuring regional Correspondent Banking Academies to help raise awareness of best practices and emerging technologies.

Heidi Toribio,Managing Director, Global Head Financial Institutions, Global Banking,at Standard Chartered Bank said that the initiative was key to preserving correspondent banking relationships, and removing ambiguity from compliance standards through partnership. “Correspondent banking goes to the heart of facilitating cross-border trade and financing growth, which is central to our DNA and our purpose as a bank,” she said.

A key element to preserving these relationships is improving the controls within the respondent bank by leveraging emerging technologies like Artificial Intelligence. Silent Eight understands this and has developed solutions to meet this need. With its AI-driven screening system, banks in developing countries could demonstrate a data-driven AI process that learns and improves its output as it addresses alerts. The process gives reliable results, resolving each alert and documenting the reason for the action. The whole AI process is systematic, reliable, consistent and auditable, and provides the analyst clear information on which to make a final determination.

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Leveraging AI solutions into AML/CTF programs is a priority for banks in developing countries so they can demonstrate that their programs are up to global standard. It should also be a priority for global institutions that are or were acting as correspondents, since it allows them to diversify into a broader range of markets at an acceptable level of risk.  Together with initiatives like De-Risking Through Education, the adoption of technology like Silent Eight can help developing economies once again gain access to global financial markets and help keep their financial transactions out of the dark.

Here Martijn Groot, VP Marketing and Strategy at Asset Control , discusses how AI and Machine Learning techniques are finding their way into financial services and changing the way we do things, in particular how we invest.

Ranging from operational efficiencies to more effective detection of fraud and money-laundering, firms are embracing techniques that find patterns, learn from them and can subsequently act on signals coming out of large volumes of data. The most promising, and potentially lucrative, use cases are in investment management though.

Among the groups that benefit most are hedge fund managers and other active investors who increasingly rely on AI and machine learning to analyse large data sets for actionable signals that support a faster; better-informed decision-making process. Helping this trend is the increased availability of data sets that provide additional colour and that complement The typical market data feeds from aggregators, such as Bloomberg or Refinitiv, range from data gathered through web scraping, textual analysis of news, social media and earnings calls. Data is also gathered through transactional information from credit card data, email receipts and point of sale (“POS”) data.

The ability to analyse data has progressed to apply natural language processing (NLP) to earning call transcripts to assess whether the tone of the CEO or CFO being interviewed is positive or negative.

The ability to analyse data has progressed to apply natural language processing (NLP) to earning call transcripts to assess whether the tone of the CEO or CFO being interviewed is positive or negative.

Revenue can be estimated from transactional information to gauge a company’s financials ahead of official earnings announcements and with potentially greater accuracy than analyst forecasts. If, based on this analysis, a fund believes the next reported earnings are going to materially differ from the consensus analyst forecast, it can act on this. Satellite information on crops and weather forecasts can help predicting commodity prices.

These are just a few examples of the data sets available. The variety in structure and volume of data now available is such that analysing it using traditional techniques is becoming increasingly unrealistic. Moreover, some has a limited shelf life and can quickly become out-of-date.

Scoping the Challenge

Setting up a properly resourced team to assess and process this type of data is costly.

The best approach therefore is to more effectively assess and prepare the data for machine learning so that the algorithms can get to work quickly. Data scientists can then focus on analysis rather than data preparation. Part of that process is feature engineering, essentially selecting the aspects of the data to feed to a machine learning algorithm. This curation process involves selecting the relevant dimensions of the data, discarding for instance redundant data sets or constant parameters, and plugging gaps in the data where needed.

An active manager could potentially analyse hundreds of data sets per year; the procedure to analyse and onboard new data should be cost-effective. It should also have a quick turnaround time as the shelf life of some of these data sets is short.

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Addressing these challenges means that traditional data management (the structured processes to ingest, integrate, quality-proof and distribute information) has to evolve. It needs to extend data ingestion and managing data quality into a more sophisticated cross-referencing of feeds looking for gaps in the data; implausible movements and inconsistency between two feeds. For instance, speed of data loading is becoming more important as volumes increase. With much of the data unstructured, hedge funds should be conscious of needing to do more with the data to make it usable. More sophisticated data mastering will also be key in making machine learning work effectively for hedge funds.

This functionality coupled with the capability to quickly onboard new data sets for machine learning will enable funds to save money and especially time in the data analysis process. It will allow data scientists to focus on what they do best and generate more actionable insight for the investment professionals.

Reaping the Rewards

Machine learning clearly has huge potential to bring a raft of benefits to hedge funds, both in reducing the time and cost of the data analysis process and in driving faster time to insight. It also gives firms the opportunity to achieve differentiation and business advantage. Hedge funds need to show returns to attract investment in an increasingly competitive space, machine learning supported by high quality data management offers a positive way forward.

Of course, the rise of Human Factors Analysis Tools (HFAT) has forced financial services firms to push the envelope, but AI is gradually beginning to be integrated into the operations of firms across other industries. Perhaps, one sector which lags behind is insurance. However, according to Nikolas Kairinos, CEO and Founder of Fountech, attitudes are definitely shifting and in large part, this is due to the possibilities presented by AI toolsets.

Indeed, the venture capital community considers the insurance industry to be so ripe for disruption that Lemonade, a US InsurTech company, managed to raise $300 million in seed funding earlier this year. As an AI developer myself, I believe that the technology can drastically improve insurers at all levels, but only if industry leaders understand what AI actually offers and how to effectively integrate it into their organisations.

AI in InsurTech

The first, and arguably, most important part of this process, is having a sophisticated awareness of what AI in insurance actually means. For most firms, the benefits of AI actually come through robotic process automation (RPA); in other words, automating existing processes to save time and resources. For example, insurance AI exists which could remove the need for firms to manually classify documents, write contracts or process claims.

However, the most significant advantage that AI offers to insurance firms specifically stems from the way in which sophisticated algorithms can use vast datasets in order to predict and monitor risk. This would have many applications across the crucial functions of underwriting, pricing and risk management. Going further, the technology could even be used to prevent fraud by detecting tiny inconsistencies in either publicly available data or a client’s financial history.

However, AI doesn’t simply provide a competitive advantage for the forward-thinking firms who employ it, it also benefits policyholders who would enjoy cheaper premiums as a result of lower overheads and reductions in the amount of fraud.

Managing the transition

Still, some within the industry remain apprehensive about the impact of AI on either the employees or customer base of an insurance firm. The first thing is to say that many of these concerns, particularly around data security, are legitimate but it’s important that industry leaders do not see these apprehensions as an insurmountable obstacle. Integrating AI is not about saving resources for the sake of it but rather adopting new tools with the potential to improve the industry as a whole.

I’ve been developing software for professional services companies for years and based on what I have seen, I believe that successfully integrating AI into your services boils down to three things. Understanding the limitations of both the technology and your organisation, working with developers as much as budget and time constraints allow and being critical about where and why you’re integrating AI into your company’s operations.

At Fountech, we think it’s important for firms to understand what AI has to offer the insurance industry, and so we recently released a new white paper which explores how insurers might integrate AI into their business. Ultimately, with a proper understanding of AI’s strengths and limitations, industry leaders can begin adapting their firms to the rigours of the new data-driven landscape.

Towards a more intelligent future

As AI begins to play a central role in the functioning of insurance firms, it’s important that industry leaders remain invested in the technology’s potential to change insurance for the better. At root, this means having a sophisticated understanding of how AI can benefit your organisation but also remaining vigilant to any problems that might arise as a result.

Finally, as we move towards a more data-driven insurance industry, it’s essential that insurance firms begin playing a more active role in the development of new AI either through investment, active feedback, or by providing a breeding ground for new tools to be refined. Now is the time for insurance firms to begin playing a more active role in the development of the tools that are going to fundamentally reshape the industry over the next few years.

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.

.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.

Intertrust, a global leader in providing expert administrative services to clients operating and investing in the international business environment, surveyed over 500 capital markets executives to identify the impact that disruptive technology is having on jobs and skills. Of these, one in six (14%) believe that AI has already surpassed human-based systems.

In recent years, the data sources used in credit decision-making have become increasingly broad and non-traditional, now including social media activity, retail spending habits and even political inclinations.

The research revealed a division in the industry about the impact of using such data on the quality of decision-making. While a third (30%) of respondents believe that using a broader range of data reduces subjectivity, a fifth (18%) think AI exacerbates existing prejudices in the credit decision-making process.

Intertrust’s study also highlighted privacy concerns regarding expanded data sets. Although almost a third (31%) of respondents think that the use of non-traditional data such as and personalised algorithms leads to better credit decisions than just relying on detached data, 36% believe tighter legislation is required to protect borrowers’ rights when they apply for funding and to restrict the information included in the assessment. A fifth (20%) suggested that the use of non-traditional data has already overstepped the ethical line and needs to be better controlled.

Cliff Pearce, Global Head of Capital Markets at Intertrust said: “The use of AI in credit decision-making has become increasingly commonplace, with the potential to make quicker more accurate credit decisions based on an expanded set of available data.

“A challenge in this area is that AI systems are only as good as the information programmed into them. For example, while a prospect may look like a poor risk at first sight, there may be extenuating circumstances overlooked by the system that a human would have noted. Put simply, AI underlines the contrast between the prime and more specialised non-conforming lending markets.”

(Source: Intertrust)

This is why Dean McGlore from V1 believes that in 2019, we’ll see CFOs switch their focus from AI to automation.

In 2019, automation – also known as Robotic Process Automation (RPA) – will move from the shadow of Artificial Intelligence. And rightly so. Like AI, it can relieve teams from mundane and repetitive work to focus on higher-value and strategic activities. But, unlike AI, automation is easier to access, expand. It’s a forecast echoed by experts around the world. Forrester, for example, predicts that the RPA market will reach $1.7bn in 2019 while Advanced has found that 65% of people would be happy to work alongside robotic technology if it meant less manual processes.

Over the next year, we will especially see RPA climb in popularity within the finance function. Teams will use it to automate the data capture and processing of supplier invoices, sales orders and other accounting documents. By automating these manual and usually administrative heavy processes, finance teams can drive unprecedented productivity and efficiency levels as well as benefit from increased visibility into the entire organisation and better data for reporting to the board.

RPA will help with a host of other external factors too. With the General Data Protection Regulation (GDPR) now in place, it will help the finance department (and indeed other areas of the business) get their data in order. RPA is a good starting point for GDPR compliance, as businesses can store, manage and track electronic documents and electronic images of paper-based information in one place and in real-time. This ensures compliance requirements by providing traceability on all documents.

Automation technologies will only be effective if the people using them understand how they work, appreciate their true potential and recognise the value they bring.

And then there is Brexit. Because RPA helps free up time for the finance team, more resources can be devoted to planning for when Britain leaves the EU in March. RPA provides an opportunity for businesses to scale up or down volume to meet demand from outside of the EU, for instance, as well as to assist the development of new products and services for new markets – all of which is essential for business growth. Moreover, with the threat of other countries hiking up tariffs after Brexit, RPA has the potential to replace the need to hire more employees and it can also help keep production costs to a minimum.

Regardless of the reason behind RPA adoption, CFOs will need to make sure that there will be a change in culture among the workforce. Automation technologies will only be effective if the people using them understand how they work, appreciate their true potential and recognise the value they bring. Arguably, investing thousands on pounds on technologies such as RPA won’t be effective if users don’t believe in them. A robust upskilling and training programme is necessary to ensure future digital success.

However, saying that businesses will turn their backs on AI in 2019 was never my intention – Artificial Intelligence will still play a key part of many organisations’ digital transformation plans. What RPA does is allowing businesses to test the water. Planning and testing automation software to see the impact it has on your operations and staff is a great indicator of the benefits that large-scale AI deployment could bring in the future – minus the fear of large-scale failure.

Planning and testing automation software to see the impact it has on your operations and staff is a great indicator of the benefits that large-scale AI deployment could bring in the future – minus the fear of large-scale failure.

In the future, we will see RPA and AI working together to transform the finance function like never before. With a combination of the right technology with AI handling decisions and chatbots managing customer queries, completely unmanned Accounts Payable (AP) for example is perfectly achievable by 2020 as a result of invoice automation.

RPA will be the first step for many and businesses looking to realise the power of automation over the next 12 months should take the following steps:

• Audit your processes and identify which involve low-skills, manual and repetitive tasks, such as many of the core, yet daily finance functions of invoice processes;
• Determine the key technologies that your business needs to become more agile, competitive and productive. RPA and AI have different capabilities, so decisions must be made based on need, rather than hype;
• Understand which new technologies are best placed to modernise or integrate with your organisation’s existing legacy systems;
• Ask yourself what the potential benefits of RPA and AI are to your finance team. Understand employees’ pain points before deciding which digital tools will help;
• Appoint a digital pioneer – someone that will drive positive technology change and that has the backing of your CEO and the rest of the C-Suite;
• Engage with your younger workforce. Typically, they are more digitally savvy and open to change. Find out what technologies they think will make a real difference.

RPA has the potential to change the face of finance for good. And, eventually, it will become ubiquitous among all key processes.

Below Russell Bennett, Chief Technology Officer at Fraedom, discusses the future prospects for AI in the banking sector, and what 2019 may hold.

AI is incredibly complex and doesn’t represent a single technology. Rather, it’s a multidimensional field encompassing a range of different technologies and methods, each supporting and supported by the others[1]. The technology’s pace of evolution has grown exponentially in recent years and if AI’s benefits and limitations are understood, it’s believed this technology will have a tremendous impact on the banking industry in 2019.

With so much potential ready to be unleashed, where exactly will we see AI’s influence in the banking sector in 2019?

Chatbots and Virtual Assistants

While chatbots have been used by financial institutions for several years, thanks to advances in AI their capabilities have continued to grow. Whereas they were once only used to answer generic FAQs, for example, most chatbots are now capable of initiating and performing tasks on their own. Thanks to these developments, Juniper estimates that the introduction of chatbots and virtual assistants will save companies $8 billion per year by 2022[2]. This is set to be only one of the benefits to banks with Gartner suggesting that by 2020 consumers will manage 85% of their total business interactions with banks through fintech chatbots[3].

Juniper estimates that the introduction of chatbots and virtual assistants will save companies $8 billion per year by 2022

While this could be a source of worry for the banking workforce, in reality, there should be little concern. Rather than acting as a replacement for employees, banks instead seem to be looking at AI as a tool to help release pressure points and empower the workforce with Accenture even predicting that banks that deploy AI wisely will see a 14% increase in jobs[4].

In 2016, Santander became the first UK bank to launch voice banking technology[5]. Of course, since then a large variety of global banks have adopted this technology in one way or another, suggesting that banks are looking at utilising AI beyond chatbots. In fact, with Mariano Belinsky, managing partner of Santander InnoVenture, discussing natural language processing[6], it seems to only be a matter of time before virtual assistants come into use.

Driving Customer Insights

Last year, we saw a clear disconnect between banks and their smaller customers. In these situations, intelligent automation could well be the answer to support businesses and provide a better service as well as working seamlessly with third parties and fintechs, rather than against them.

In our recent study of SMEs in the UK and US, we found that less than 20% of SME owners thought that banks they had dealt with over the past year fully understood their needs as a business, demonstrating a clear lack of engagement. In 2019, using automated data collection on an ongoing basis, behind the scenes, can ultimately ensure bank relationship managers are better equipped with in-depth knowledge about their customers; hence best positioned to support their business and provide a better service.

Less than 20% of SME owners thought that banks they had dealt with over the past year fully understood their needs as a business.

Security and Compliance

One of the key differences between AI applications and other, more traditional technological solutions, lies in AI’s ability to continuously learn from the data it is supplied with, hence refining its decision-making processes over time.

Cybersecurity is a current hot topic for the financial services sector and regulatory compliance is another. AI can add real value in both of these areas. Machine Learning platforms can be coded to identify user patterns and detect anomalous network behaviour, something that’s increasingly essential as cyber-attacks are often disguised with inconspicuous data or code.

In recent years, technology has been a disruptor and an innovator. Technology is increasingly helping shape customers’ wants, needs and expectations. With a raft of new regulation encouraging the use of technology in banking, there’s nowhere left for anyone to hide. The technology revolution is in full swing and for banks, it’s very much adapt or die.

In the very near future, it is likely that AI will completely revolutionise banking. It will redefine how banks operate, what innovative products and services they create and how they evolve the customer relationship. Banks must, therefore, embrace this new technology or risk of falling behind in an extremely competitive environment.

[1] https://www.accenture.com/t00010101T000000Z__w__/gb-en/_acnmedia/Accenture/Conversion-Assets/DotCom/Documents/Local/en-gb/PDF_3/Accenture-Redefining-Capital-Markets-with-Artificial-Intelligence-UKI.pdf

[2] https://www.juniperresearch.com/press/press-releases/chatbots-a-game-changer-for-banking-healthcare

[3] https://www.gartner.com/imagesrv/summits/docs/na/customer-360/C360_2011_brochure_FINAL.pdf

[4] https://www.accenture.com/gb-en/insights/banking/future-workforce-banking-survey

[5] https://www.santander.co.uk/uk/infodetail?p_p_id=W000_hidden_WAR_W000_hiddenportlet&p_p_lifecycle=1&p_p_state=normal&p_p_mode=view&p_p_col_id=column-2&p_p_col_pos=1&p_p_col_count=3&_W000_hidden_WAR_W000_hiddenportlet_javax.portlet.action=hiddenAction&_W000_hidden_WAR_W000_hiddenportlet_base.portlet.view=ILBDInitialView&_W000_hidden_WAR_W000_hiddenportlet_cid=1324582275873&_W000_hidden_WAR_W000_hiddenportlet_tipo=SANContent

[6] https://www.americanbanker.com/news/what-santanders-latest-bets-say-about-the-future-of-fintech