Richard Billington, Chief Technical Officer at Netcall, explores the changes that AI has brought to the business world,.
From the recommendations we receive on Amazon or Netflix to the AI-driven camera software used to improve the photos we take on our smartphones, AI forms parts of the popular services that are used multiple times a day. Even the map and Satnav applications we use rely on AI. Company chatbots are a more well-known use of AI, and can now be found on nearly every company website you visit. In fact, it’s been predicted that 80% of companies will be using chatbots this year.
However, consumers today are getting ever harder to please. The growing ramifications of the ‘Amazon Effect’ means that today’s customers expect instant gratification when liaising with companies – placing more pressure on business leaders to provide more, faster and better. Digital banks such as Monzo and Starling are continuing to build upon these expectations by enabling customers to open accounts in a matter of minutes. And that’s not all: companies are now under pressure to offer 24/7 customer service through a multitude of communications channels, including Twitter, Facebook messenger and other social media.
Furthermore, as millions of individuals are quarantined and isolated amid the current COVID-19 outbreak, never has there been more pressure on customer service teams to facilitate rapid and seamless responses to enquiries on a broad range of issues. In a time of crisis, a customer’s interaction with an organisation can leave a lasting impression, and potentially impact future trust and loyalty – another headache for CEOs, CIOs and CTOs.
Digital banks such as Monzo and Starling are continuing to build upon these expectations by enabling customers to open accounts in a matter of minutes.
AI-enabled systems are increasingly being viewed as the perfect solution for optimising customer service – as it’s extremely beneficial in allowing companies to provide agents that are ‘always on’, as well as hyper-tailored experiences for customers. However, some businesses are yet to harness these technologies – along with their benefits.
The barrier businesses must overcome
For many business leaders, a lack of the right skills in the right place has hampered their ability to implement AI across their company’s customer service function. According to an IBM institute of Business Value study, 120 million workers in the world’s twelve largest economies will need to retrain as a result of AI and intelligent automation.
Other business leaders may face budgetary constraints and can find themselves put off by the significant investment often required when integrating AI systems in their existing IT infrastructure. Misunderstanding surrounding AI can also mean that some CEOs are understandably concerned that the solution they are putting into place may end up being not quite right for their needs. Therefore, concerns over wasted time, money, and other resources often result in a rejection of adopting new technology. However, these concerns will be outweighed by the repercussion stemming from an inability to unlock the true value of this technology – and potentially fall behind in today’s fast-paced market.
Unlocking the benefits of AI
Smaller businesses tend to fall short of the IT foundation and personnel needed to remain up to date with the latest technological advancements in enhancing customer service. But it will ultimately be these investments that enable business leaders to contend with customer demands and flourish in an ever-evolving landscape. Adopting these low-code solutions will enable resource-poor teams to quickly test specific features or workflows without the need for specialised technical skill – enabling employees to innovate and implement significant change, without relying heavily on the IT department.
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Low-code is helping companies surpass shortages within multiple digital skills, including AI, by removing the need for highly-trained developers who have traditionally been relied upon to bring new applications to the forefront. In fact, in a recent analyst report, Forrester predicts that savvy application design & development (AD&D) leaders will no longer try and reinvent the wheel and instead will now source algorithms and insight from their platform vendor or its ecosystem. Implementation consultants will now be able to differentiate themselves using AI-driven templates, add-ons and accelerators – particularly industry-specific ones.
With low-code software solutions, everyday business users are able to ensure automated and AI-driven solutions are up and running quickly and easily. Due to the lack of complex coding, the process of integrating AI is instantly simplified, and easily accessible by a range of workers across a variety of business sectors, regardless of size. The ability to test applications before implementation ensures business leaders are able to explore the capabilities of AI without investing valuable time and effort. As a result, they will be empowered to unlock a wave of new possibilities for AI development across a range of functions.
By breaking down walls between IT and other departments within organisations, low-code technology can be utilised to help bring teams together to work collaboratively on applications that rapidly improve processes, by harnessing the knowledge of customer facing wider-business teams. And as COVID-19 continues to cause ramifications for businesses across the globe, business leaders must respond with agility to keep up with increasingly complex customer demands. Speed of implementation and the technology that can help organisations get there is therefore essential when it comes to staying afloat and competitive. And, where many workforces are currently struggling from unprecedented circumstances, the adoption of AI processes through low-code applications can help minimise workloads and free up workers– enabling them to focus on more strategic tasks within the organisation, by automating some of the more mundane processes.
Nowadays banking is closely interlinked with technology. It’s also no secret that digital banking is many people’s preferred method of interacting with their money. Changes to the way we bank over the last decade and our increasing reliability on digital platforms have led banks to change their business models. Controlling money through online services has created a seismic shift in the industry and those who haven’t adapted are struggling to stay relevant. Jean Van Vuuren, Regional VP for UK, Middle East and South Africa at Alfresco, examines how challenger banks have pushed the industry forwards.
Despite the introduction of challenger banks to the industry, many of us still rely on large, traditional banks to keep our hard-earned money safe. So how do these institutions take inspiration from the new emerging banks and put it into practice whilst keeping themselves relevant to a society that is increasingly reliant on technology? And what is next in the wave of digital transformation for financial institutions?
Using AI as part of the customer experience
Banks prioritising the customer experience has increased by leaps and bounds in the last 5-10 years, but it doesn’t just end with the launch of an app or the re-design of an online experience. The customer experience needs to be revisited regularly and continually play a core role in the adoption of the latest technology available.
For example, the future of AI in the banking world is very exciting and is completely transforming the customer experience. Voice banking, facial recognition and automated tellers can help create a completely personalised experience for each customer. Someone could walk into a high street bank, AI sensors at the door could use facial recognition to let the teller know who has arrived and they could automatically pull up all the information about their account without having to ask for their bank card or details.
The customer experience needs to be revisited regularly and continually play a core role in the adoption of the latest technology available.
As technology gets more sophisticated, this opens up possibilities for banks to focus on advising customers rather than spending time on transactions and processes.
Trusting the security of the cloud for confidential documents
The cloud has completely transformed the way in which we store information on our smartphones, computers and within the enterprise. However, as with any technology it comes with potential security risks. Trusting a third party with your data feels risky in most industries because you no longer feel in control of it, but banks are often trusted with our most precious data – not to mention our money. Therefore, maintaining confidentiality is of upmost importance to banks in order to maintain the trust of their customers.
Financial institutions should make sure that they are not relying on security embedded in cloud platforms to do the heavy lifting. Implementing governance services that provide security models, audit trails and regulate access – even internally, and confidently demonstrate that compliance is key for an industry with so much access to personal information. Whilst working in the cloud offers flexibility, it needs to be made secure with intelligent security classifications and automatic safeguarding of files and records as they are created.
This also brings up the issue of legacy platforms from a security and feasibility standpoint. Fund management companies find that legacy platforms are very expensive and not cloud ready. There is very little room for innovation and it is hard to adapt them to meet customer demands. Even if a fund management company has migrated to a Saas or Paas solution, quite often regulatory obligations and the potential dangers posed by hacking and data breaches mean that they sometimes go back to using an on-premises solution. Instead of backtracking, financial institutions should spend time to understand what the best cloud option for them would be and how they would implement it within the confines of governance and compliance.
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Going paperless
Discussing going paperless in 2020 may seem like going back to the past, but for many financial institutions making the transition to fully paperless operations is still a work in progress. This is also a key area where challenger banks which have never had paper-based processes have an advantage, they don’t have to adapt simply because they were born paperless. There is also a new generation of consumers that embrace and often expect paperless banking.
While the fintech industry is intrinsically paperless, banks are still adapting to phase out paper support, but this transition should be an integral part of updating the customer experience. The paperless movement involves moving from simply depositing checks via smartphone to a complete digital experience from end-to-end.
Going paperless also provides an added layer of security in accordance with a rising tide of regulations and government mandates. With digital records, automated management processes allow companies to set up rules around metadata to file records, put security procedures around them and also deleting personal information within retention regulations.
Keeping pace with challenger banks who are born of today’s technology
In recent years, the introduction of technological advances such as digital ID verification, e-signature and risk analytics are transforming the way financial service providers interact with their customers. New challenger banks build whole systems in as few as two weeks and automate as much as possible.
By their very nature, challenger banks are pushing their competitors to be more agile and they are growing exponentially, something which the high-street banks had underestimated when they first entered the market. Created for the digital first generation, challenger banks won market share by putting customer-centric products at the heart of their business. They are also able to improve the product and the user experience quickly according to customer feedback.
Customers are flocking to the disruptors in the market who offer exciting functionality. Challenger banks providing customers with new online features, ones that let them take control of their finances, are thriving in the market. In the modern day, banks need to embrace new technologies and digitise processes to create a customer-oriented business and, ultimately, succeed in the market.
Over the past few months, the pandemic has accelerated the transition to a fully digital world. We are seeing more e-commerce and online offerings to help us socially distance. From ordering groceries online to signing up for online gym classes and communicating with friends and family, our digital presence has increased significantly. Unfortunately, this growing digital presence leads to a rise in cyber-attacks, too, and more specifically, fraud. Joe Bloemendaal, Head of Strategy at Mitek, explains further below.
Fraud cases were predicted to be on the rise even before the mass lockdown. According to Juniper Research, online payment fraud for businesses in e-commerce, banking services, money transfer and airline ticketing were suspected to lose over $200 billion to online payment fraud between 2020 and 2024. The recent growth in digital services and accounts, and advanced technology like AI, is further driving the frequency of these fraudulent activities.
With easy access to an abundance of consumer data, advanced computational power and tools, it is becoming easier for cyber-criminals to completely take over legitimate accounts. So, how can we stay protected against these attacks? The first step is to understand what these cyber-criminals are after and this is often easy to overlook. Social media allows people to stay connected, but it also exposes a large amount of personal information, making people’s digital identity readily accessible to hackers. At every corner, hackers are lurking behind the screen trying to trick banks by stealing people’s details in order to access their hard-earned savings or turning to other methods of phishing scams.
Thankfully, with the help of unique identifiers and usage-patterns, it is possible to verify the digital identity and verify a user – making sure that they are who they claim to be when participating in any online or digital interaction. For financial services institutions to stop fraud in its track, they need to begin with understanding how to protect this digital identity.
But first, what is a digital identity?
A digital identity can be defined as “a body of information about an individual or organisation that exists online.” But the reality is that not many understand what really makes up a digital identity, and so cannot protect it. Is it our social media profile? Our credit score or history? Is it contained within a biometric passport?
A digital identity can be defined as “a body of information about an individual or organisation that exists online.”
This confusion means many are also concerned about the level of access a digital identity exposes to potential fraudsters. Once a hacker has our personal details, how much of ‘us’ can they really access? In the US, we found that 76% of consumers are extremely or very concerned about the possibility of having their personal information stolen online when using digital identities; but 60% feel powerless to protect their identity in the digital world.
This is mainly because many trust in their old methods and devices for security control – passwords, security questions, and digital signatures. But as modern security techniques evolve, these methods are no longer able to protect us on their own.
More advanced and secure methods of identity verification mirror modern social media habits. Most of us are familiar with taking selfies. Now, technology can match that selfie to an ID document such as a driving licence, turning a social behaviour into a verifiable form of digital identification. A simple, secure process enables people to gain access to a variety of e-commerce and digital banking services, without a long and friction filled ‘in-person’ process.
Even in the case of a compromised photo ID or stolen wallet, we can re-verify our digital credentials once we have our paperwork back in order – and restore a digital profile to full health.
But this doesn’t address the question of who is responsible for our digital identity – who will protect the long-term health and protection of our digital ‘twin’?
Historically, governments have proven to be poor custodians of their citizens’ data, given the loss of 25 million tax records, including payroll information, in the not-so-distant past. Some of the world’s biggest companies are not immune either, being held responsible for countless data breaches over the years.
As such, some believe citizens should be responsible for their own digital identities, making them ‘self-sovereign’. The ambition is to free our own personal information from existing databases and prevent companies from storing it every time we access new goods or services. Data controls such as GDPR and CCPA are a start – policing and regulating how companies use, control, and protect data.
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However, ‘self-sovereign’ identities could only become mainstream if governments relinquish their sole responsibility for issuing and storing our identity information. It will also require new technologies, such as blockchain, to gain traction and be trusted. A cultural shift will be paramount, too.
Some suggest that instead of the rise of ‘self-sovereign’ identities, we’ll see some of the industry’s biggest players emerge instead. We’re already used to verifying our identities through Google and Facebook, using them to speed up registrations or access new services. Could those tech giants become our digital identity guardians?
Or would we rather entrust our digital identities to financial companies such as Visa or Mastercard, who have been looking after our financial transactions for decades, historically taking on the risk for us, and are now able to process disputes and stop unauthorised withdrawal of funds even faster?
Balancing trust and control
It’s clear that taking good care of one’s digital identity is a fine balance between trust and control. Security is also a personal thing, and what is right for one may not suit another. One thing is for certain: identity is the essence of the human being, so guardianship should be hard-earned.
Both businesses and individuals have a part to play when protecting our digital twin. With the help of digital identity verification and cybersecurity protection technologies, we can make self-sovereign identities a reality - if that’s what the people want.
The COVID-19 pandemic has not just had a devastating impact on health and society, it has dominated economic and business matters unlike anything we’ve seen in peacetime history, and, across the globe, schools, companies, charities and self-employed professionals are still adjusting to a brand new remote working contingency plan.
Fortunately, as a society, we are extremely well-equipped to adapt to remote working with a turnaround time of just a few days. This was proven by the sheer quantity of businesses, many of whom care for thousands of employees, who just a few weeks ago managed to transform their entire internal structure to a digital environment. Not only is this an inspiring example of human collaboration at a time of crisis but also a true testament to the power of the technology at our disposal.
In fact, remote working has proven itself so effective for some organisations, that it has gone beyond a short term contingency plan; it’s starting to look like remote, or at least flexible working, will be incorporated in the long term for thousands of office-based workers. Clement Desportes De La Fosse, Co-founder and Chief Operating and Financial Officer at Spearvest, shares his thoughts on how the finance sector will be forever changed by the pandemic.
Although it may sound premature to think about a post COVID-19 world, a majority of industry operations are sure to change forever, and, none more so than in the financial sector. For many years, traditional banks and financial institutions have been associated with outdated infrastructure and slow legacy IT systems, which are a burden for financial professionals and consumers alike. In fact, a recent study in 2019 revealed that UK banks were hit by ‘at least one’ online banking outage every day across a nine month period.
Today, the demand for banking and financial services has never been higher: emergency loans, government payment schemes and personal finance management are required for people to survive. What’s more, visiting a branch in person is no longer an option, and therefore financial institutions are forced to invest in capable IT infrastructure and relevant automation, regulation, and finance technology to deal with influx of demand.
For many years, traditional banks and financial institutions have been associated with outdated infrastructure and slow legacy IT systems, which are a burden for financial professionals and consumers alike.
Whilst it could be argued that this much-need update was inevitable, the pandemic has certainly forced many banks’ hands in enforcing this change, and means our financial institutions will emerge from the crisis with a much more capable IT infrastructure. The following areas are where banks are, or should be investing, in the coming weeks, months and years, with insight into how exactly these cutting-edge technologies are impacting the financial services sector for the better.
Artificial Intelligence
Artificial Intelligence (AI) has been a growing trend in finance in the past decade, primarily being used to address key pressure points, reduce costs and mitigate risks. However, the demand for digital banking services as a result of COVID-19 will likely push the sector in the direction of developing and incorporating sophisticated automation and customer service AI.
We’re a few years off the mass adoption of robotics technology of this nature, but it’s safe to say the COVID-19 threat has highlighted the pressing need for more automation and better service technology.
Public Cloud
The shift toward cloud-based computing has already been significant, with most financial institution operating cloud-based Software-as-a-Service (SaaS) applications for business processes, such as HR, accounting, admin solutions and even security analytics and know-your-customer verification.
However, advancements being made in cloud technologies and increasing demand for SaaS applications for remote workers means that soon we could see core services in the financial sector, such as consumer payments, credit scoring and billing, to become stored and managed in cloud-based SaaS solutions.
RegTech
Much like the increasing demand for AI and Cloud-based SaaS applications, regulatory technology (RegTech), can do important work in ensuring financial work remains regulated and legal. The right RegTech, such as automated customer onboarding technology, can also save a firm a lot of time, freeing-up much-needed time to focus on the work that can not be completed by software or a robot.
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Big Data
Customer intelligence facilitated by big data and consumer behaviour is an incredibly important tool which can be used for extremely accurate decision making, risk-assessments and revenue and profitability forecasts, to name just a few use-case example.
Some modern financial institutions and start-ups have been using big data and analytics technology for a number of years, and those more ‘traditional’ which may have neglected this cutting-edge technology are depriving their customers of top tier financial advice and insight at a time when they are in need of it most.
Security
Cyber attacks, money laundering and hackers have always threatened the financial services to a large extent. However, with entire workforces online, operating in a remote, sometime unsecure environment, the cyber-threat facing consumers has never been larger.
Thus, cyber-security has, and should, be invested in heavily by financial institutions looking to protect their own client, employee and company sensitive information. At the same time, safe internet and banking practice should be implemented and taught to all members of the general public to ensure they do not give away sensitive information such as payment details.
Fast forward, five years from now, we will look at the pandemic as a trigger that enabled us to spend our time more efficiently, and digital technology and the cloud will be key in facilitating this positive change.
Artificial intelligence has already made a significant, positive impact on the financial services ecosystem and we can only expect this trend to accelerate in years to come. AI has the potential to radically transform businesses but only if they deploy it with appropriate diligence and care. A 2020 report by EY and Invesco anticipates that AI will expand the workforce in fintech by 19% by 2030 as the industry stands to be one of the largest to benefit from the efficiency gains and innovation the technology can bring through operational optimisation, reduction of human biases and minimisation of errors in anomalous data. Alex Housley, CEO and founder of Seldon, further analyses the recent changes in the role of AI and the impact it is set to have on the finance sector in years to come.
Talent Shortage Within FS
According to a report by Bloomberg, listings for AI-based jobs within the financial sector increased by approximately 60% from 2018 to 2019. This demand for workers with AI expertise is not only seen within the financial industry but across a variety of other professional sectors, such as e-commerce, digital marketing and social media. The jobs market has had little time to respond, resulting in a shortage in access to talent. A study by SnapLogic found that whilst 93% of UK and US organisations are fully invested in the use of AI as a priority in their business, many lack access to the right technology, data, and most importantly, talent to carry these goals out. This ‘skills shortage’ is a major obstacle to the adoption of AI in business, with 51% of those surveyed acknowledging that they don’t have enough individuals trained in-house to make their strategies a reality. Machine learning can offer benefits in many forms and different businesses have varying needs. There is no ‘one size fits all approach’ when adopting and deploying AI, which can make it a costly process for many organisations not equipped with the right tools.
Fortunately, there is ample opportunity to enhance the responsibilities of numerous roles within their organisation or let employees get on with more strategic work. SEB, a large Swedish bank, uses a virtual assistant called Aida which is able to handle natural-language conversations and so can answer a trove of customer FAQs. This means customer service professionals have been redeployed to focus on complex requests and their more meaningful responsibilities. Even employees currently working within the industry are looking to broaden their skills to become more versatile across new technology-driven roles. In particular, financial services companies are looking to upskill their data scientists and analysts. They have the base skill set required and can do tremendously well with the right engineering support. Deploying artificial intelligence within a business’s infrastructure means it can take care of mindless, repetitive tasks and free up employees to focus on other, more rewarding parts of the business, maximising automation and cutting costs.
There is no ‘one size fits all approach’ when adopting and deploying AI, which can make it a costly process for many organisations not equipped with the right tools.
Enhancing Fraud Detection
One of the biggest use cases of artificial intelligence within financial services is fraud protection. With the rise of online banking and the exponential growth of digital payments, banks have to monitor huge swathes of transactions for fraudulent behaviour. This huge influx of data points poses major issues for the human brain but actually maximises the effectiveness of ML systems. We’ve seen significant growth in the use of deep learning, with most major retail banks now relying on machine learning tools to recognise and flag suspicious activity. To keep up with the pace of criminals and comply with stricter regulations, service providers have to look beyond traditional methods and implement hybrid strategies built around holistic understandings of behavioural and anomalous data.
Indeed, research by AI Opportunity Landscape found that approximately 26% of funding raised for AI startups within the financial services industry were for fraud or cybersecurity applications, dwarfing other use cases. This number is expected to rise as fraud detection and mitigation continues to be one the highest priorities for customer-facing organisations as consumers increasingly hand over their data in exchange for services.
Better Serving Customer Needs
Financial services companies are increasingly leveraging artificial intelligence to deliver tailored services and products for their client base. For those banks mining data effectively, AI provides the ability to serve customer needs across multiple channels, and in some cases to grow operations at an unprecedented scale. Tools such as chatbots, voice automation and facial recognition are just a few of the ways banks are using AI to streamline and personalise the user journey for their customers. Importantly, consumers are increasingly literate in automated services and their expectations are constantly rising as the technology improves, meaning organisations must constantly adapt or risk being left behind.
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Chatbots and voice agents are also able to detect and predict changes in consumer behaviour, giving feedback on each interaction with a customer. All the results from customer touch-points are shared across the organisation, ensuring decisions and recommendations involving a human or machine are more intelligent and precise. Over time, these analytics mean businesses can make real-time decisions with their customers in mind, boosting engagement and personalisation.
In order to detect customer data from online purchases, web browsing and in-store interactions, banks must have AI in place to collect the data and automate decision-making. By adapting these technologies banks can connect their data, amplifying their offering effectively across all channels.
Continuous Adoption of Artificial Intelligence
Artificial intelligence and machine learning have already enhanced numerous capabilities for the financial sector, improving recommendations, customer experience, and efficiencies via automation. AI will continue to dominate different parts of the financial sector, and the acquisition of machine learning and data science talent will become the norm. A recent survey from the World Economic Forum attests to this, with nearly two-thirds of financial services leaders expecting to be mass adopters of AI in two years compared to just 16% today.
Acquiring the right talent to drive machine learning and AI in organisations will remain a challenge as innovation is focused in different areas and new technologies are being implemented. In lockstep with this will be the constantly evolving regulatory landscape surrounding adoption of AI in financial services as each side races to match and often contain the other. However, the multiple benefits that come from implementing AI and machine learning are clear, and it will be a key area of focus and growth for businesses within financial services over the next decade.
Dermot O’Kelly, Senior Vice President, Europe at Finastra
Think your organization hasn’t embraced AI? Think again. The reality is that there are hundreds of applications of artificial intelligence embedded in everyday organizational life. From pay-per-click ads to social listening, chatbots to lead scoring, biometric security to network attack detection. As Europe at Finastra's Senior Vice President Dermot O'Kelley outlines below, the chances are that your organization is already relying heavily on AI for a range of functions.
It’s true that many of these services may be provided by third parties connecting directly to systems via open APIs. The organization therefore doesn’t need to become the expert. In fact, there is a proliferation of external experts as AI becomes ever more accessible. In less than two years, training time for machine vision algorithms dropped by over 99%. It went from three hours to just 88 seconds – whilst computational costs dropped from ‘thousands of dollars to double-digit figures’.
It therefore comes as no surprise that organizations are looking at how they can benefit from the AI revolution, to help boost areas such as operational efficiency, security, predictive capabilities, product development or customer satisfaction.
In less than two years, training time for machine vision algorithms dropped by over 99%.
Leading the way is the financial services sector, not least because of the vast amounts of data held by legacy organizations, but also in response to the changing expectation of consumers. Tech giants created new models of engagement, platforms that consolidated services and captured data to further fuel predictive capabilities, and this expectation of convenience is now shifting to financial services, where consumers are now more than comfortable with concepts like robo-advisory. Institutions, regardless of whether they’re providing retail services, lending, trade finance, wealth or any other line of business, are racing to adopt similar models without relinquishing customer data.
As data proprietors, the world of opportunity that AI affords any organization is immense. Data is the new currency as we enter the fourth industrial revolution, and all AI applications rely on huge amounts of data to function well. So, why aren’t all organizations rushing to embrace AI?
- Firstly, the lack of appropriate, compliant data. Successful AI models require tens of thousands of records, cleaned and labelled, that have been captured and stored according to strict regulatory conditions.
- Secondly, the growing urgency for transparency and accountability in data, models and decisions to stop the cumulative effect of bias. Best practices are offered, but as yet there is no universal regulation or consensus.
- Thirdly, the ethical implications - just because you can doesn’t automatically mean that you should, particularly where privacy is concerned. As Cap Gemini noted, consumers are attuned to ethical practices…get it wrong and the trust – and business – is lost.
- Finally, the well-documented shortage of skilled AI and Data experts. Thankfully, through open APIs, all organizations can connect in AI-based capabilities developed by specialist third parties – it doesn’t always pay to build in-house.
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The intelligence race continues unabated, with escalating VC investment in AI and new, exciting applications that are having tangible success. Still not sure what Artificial Intelligence can do? Very soon it will be easier to recall the few things the technology can’t do.
Money is a sensitive subject when it comes to the legal world. This is why governments are having a difficult time adjusting their policies to allow the utilization of emerging technologies to enhance traditional financial services. Add to that the boundless possibilities and unexplored scenarios of the results of adopting these technologies, then you have more people opposing the idea instead of championing them.
For instance, many proponents have shown the superiority of using blockchain technology in carrying out cheaper and more secure financial transactions through cryptocurrencies. But until today, most governments still don’t know how to respond to the growing market.
The challenge now lies with traditional finance companies who can only benefit from using these technologies for more efficient systematized operations. If these organizations can adopt these tech while assuring the authorities about the consistent quality and security of the service, they can help speed up the changes in the existing guidelines and policies.
This infographic by Prototype discusses the various technologies that are disrupting the financial industry.
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.
Millions of risk calculations flow through sophisticated banking software every day, to help the institution build an overall risk profile: Take on too much risk, and the bank could lose its customers’ trust, or worse. Take on too little, and the bank sacrifices growth.
Occasionally, banks face risks that they didn’t anticipate, or adequately plan for--from liquidity challenges, technology glitches and infrastructure failures to natural disasters, supply chain disruptions, and cybercrime. Such incidents unfold quickly and can take jarring twists and turns, requiring constant vigil over every new piece of relevant information that emerges.
Sometimes, instead of managing such risks, banks have found themselves wrestling with an unwieldy issue that grows into a full-fledged crisis, threatening the institution’s people, operations, and reputation.
In a 2019 survey from PwC[1], 7 out of 10 senior leaders said their company had experienced at least one major crisis in the past five years.
In the same survey, PwC identified the 19 most common crisis vectors that companies faced globally in 2019:
23%: Financial/Liquidity
23%: Technology failure
20%: Ops failure
19%: Competitive/Marketplace disruption
16%: Legal/Regulatory
16%: Cybercrime
16%: Natural disaster
15%: Leadership transition
14%: Supply chain
14%: Product failure
12%: Leadership misconduct
11%: Ethical misconduct
9%: Viral social media
9%: Geopolitical disruption
9%: Product integrity
8%: Workplace violence
7%: Shareholder activism
7%: Humanitarian
5%: Terrorism
Recent examples of banking crises abound.
In late 2019, Lloyds Banking Group told investors[2], its losses related to the ongoing payment protection insurance crisis had grown to nearly £22 billion. Its Chairman, Lord Blackwell, has agreed to step down[3] by mid-2021.
In 2016, American regulators levied tens of millions of dollars in fines against banking giant Wells Fargo, which admitted its employees had systematically opened fake accounts to hit aggressive sales targets. Then-CEO John Stumpf resigned, and in 2018, the institution agreed to pay $575 million[4] to settle state consumer protection lawsuits. The fallout, and related legal costs, continue to dog the bank.
Similarly, there are numerous recent examples of financial institutions facing technological failures, risks from new regulations, cybercrime and shareholder activism. On a routine, day-to-day basis, risk and crisis management teams at financial institutions monitor the safety of the bank’s physical assets, employees, executives and brand.
Customers, shareholders, and employees of banks increasingly expect that financial institutions will keep pace with the speed of the world and adjust to new demands promptly.
Increasingly, banks are turning to artificial intelligence to help them parse through billions of data points in public data sources to identify emerging operational risks. Artificial intelligence is capable of looking for patterns in unstructured data to detect risk faster than traditional sources of information, like news organisations or social media topic lists.
For example, in early January, the US Federal Aviation Administration announced it was halting commercial airline traffic over Baghdad, and portions of the Middle East, amid increasing tensions in the region following the targeted killing of a senior Iranian military commander. AI-powered software from Dataminr surfaced that alert to its commercial clients within seconds, prompting a large American bank to order an immediate halt to all employee travel to the region. Similarly, Dataminr’s platform quickly alerted its clients to the downing of Ukraine International Airlines flight 752, prompting that bank to immediately check if any of its employees or partners were on board.
Customers, shareholders, and employees of banks increasingly expect that financial institutions will keep pace with the speed of the world and adjust to new demands promptly. This speed is increasingly dictated by the rate at which public information breaks -- in real-time, every second, across thousands of data sources, in multiple languages and formats. The institutions that can extract value quickly from troves of public information will be best positioned to outpace competitors and deliver measurable value to all their stakeholders.
[1] https://www.pwc.com/gx/en/forensics/global-crisis-survey/pdf/pwc-global-crisis-survey-2019.pdf
[2]https://www.lloydsbankinggroup.com/globalassets/documents/investors/2019/2019_lbg_q3_ims_transcript.pdf
[3]https://www.bbc.com/news/business-50246479
[4]https://www.attorneygeneral.gov/taking-action/press-releases/attorney-general-shapiro-announces-575-million-50-state-settlement-with-wells-fargo-bank-for-opening-unauthorized-accounts-and-charging-consumers-for-unnecessary-auto-insurance-mortgage-fees/
We saw the digital transformation of eCommerce with what 10 years ago was a complex process to open an online store that can now be accomplished in minutes. Gone are the expensive payment provider integrations with the rise of Shopify opening an online store is a streamlined and automatic process.
With the automation through machine learning and artificial intelligence of once complex lending processes, the same can now be said for how eLending is completely changing the banking and financial worlds.
Unified Lending Management - What It Is & How It Automates Lending
Unified Lending Management (ULM) is the concept that describes the complete complex of measures business undertakes to digitalize their crediting processes.
A solution that can automate all steps in the lending process from the loan origination approval through to the collections and reporting process is the way that lending processes can be automated to be as easy as the opening of a Shopify store.
One company leading the Unified Lending Management (ULM) industry in terms of innovation and reliability is TurnKey Lender. TurnKey Lender designs and develops end-to-end intelligent software products that automate the entire lending process.
TurnKey Lender offers software solutions that automate every part of the lending process for different types of creditors: money lenders, SME financing companies, grant management institutions, leasing, trade finance, in-house financing, and bank-grade lenders. Currently, TurnKey Lender serves customers in over 50 countries as the trend is developing. The functional modules, that come either fully integrated or as separate tools, cover application processing, loan origination, risk evaluation, underwriting and credit decisioning, loan servicing, collection, and reporting.
How Artificial Intelligence Drives Lending Automation
Led by Dmitry Voronenko, who holds a Ph.D. in Artificial Intelligence and has been creating banking solutions for decades, TurnKey Lender heavily invests in the idea of applying machine learning, deep neural networks, and other AI approaches to make the lending process more streamlined, intelligent, and secure.
This is an example of how technology and science can often take complex matters and make them simple and automated. Below is an overview of the thinking process that TurnKey Lender’s credit decisioning engine does. Additionally, it conducts the complete risk evaluation and credit decisioning process within a 30-second time frame. It would work even faster if requests for risk profiles came back from credit bureaus faster.
To deliver the most accurate and secure system for credit decisioning possible, TurnKey Lender developed sophisticated models powered by both deep neural networks and proven statistical techniques. The solution combines numerous evaluation approaches in the assessment of each borrower.
In order to build the process to be more potent than traditional scoring, the contributing parameters can include financials scoring, firmographics, credit bureau evaluations, loan application scoring, and bank account statement scoring with rules, decision trees, cross-checks, and calculations.
In the new digital reality, AI-powered credit decisioning allows lenders to:
- Build up a fast and secure loan origination process
- Automate every step of borrower`s digital journey
- Find and filter out potentially ineligible borrowers based on sophisticated proprietary models powered by deep neural networks
- Reduce the operational costs of origination
- Be prepared for an increase in loan application volumes and expansion into new markets
- Enable unhindered scalability of the lending business
Conclusion
Dmitry Voronenko, CEO and co-founder of TurnKey Lender
AI-powered credit scoring system is a part of TurnKey Lender’s Unified Lending Management solution and it can be delivered in tandem with many other pre-integrated systems or as a stand-alone tool. The system provides a choice between a fully automated borrower`s digital journey and a semi-automated creditworthiness analysis. This helps lenders combine the power of predictive models with the knowledge of in-house experts.
For more info about the company’s lending automation solutions or for a free personalized demo, contact the TurnKey Lender team at sales@turnkey-lender.com.
And to wrap up, here is a quote from Dmitry Voronenko, CEO and co-founder of TurnKey Lender: “The importance of this kind of proprietary technology is hard to put into words. This scoring has the potential to make business crediting across borders and industries safer, faster, and more lucrative for everyone involved.”
Compliance is a must-do activity, not a nice-to-have. According to Colin Bristow, Customer Advisory Manager at SAS, it is essential that companies extract maximum value from compliance processes, reducing the possibility of it being considered a cost centre.
Technological innovation can help to lift some of the compliance burden. The level of technology you can realistically implement depends on how advanced the organisation is to start with. One company’s moonshot could be another’s business as usual. Assessing the starting point is just as important as considering the benefits and end goal.
RegTech, AI and the future of compliance
This is the question that the burgeoning RegTech (regulatory technology) industry is seeking to answer. AI is typically at the forefront. RegTech partly focuses on improving the efficiency and effectiveness of existing processes. As part of that improvement, organizations are using AI, machine learning and robotic process automation (RPA) to smooth the integration and processes between new RegTech solutions, existing legacy compliance solutions and legacy platforms.
Why look to AI for help? Recent regulations, such as GDPR or PSD2, are handed down in the form of large and extremely dense documentation (the UK government’s guidance document for GDPR alone is 201 pages). Identifying the appropriate actions mandated by these lengthy documents requires a great deal of cross-referencing, prior knowledge of historical organisational actions, and knowledge of the relevant organisational systems and processes. What’s more, several regulations attract fines or corrective actions if not applied properly (like the infamous "4% of company turnover" penalty attached to GDPR).
In short, the practical application of regulations currently relies on human interpretation and subsequent deployment of a solution, with heavy penalties for noncompliance. This is where AI can help, reducing the workload involved and improving accuracy. Here are three key examples of how AI can help companies turn compliance into a value-added activity.
1) Reducing the risk of nonconformity
Following the deployment of compliance processes, there is often residual risk. This can be as a result of unforseen gaps in compliance processes, or unexpected occurrences that become apparent when operating at scale.
That’s partly because there are usually a lot of steps and processes to be carried out during the data collation stage of compliance programmes. RPA can help reduce administrative load associated with these processes that include a high degree of repetition – for example, copying data from one system to another. AI can then help process cross-organisational documentation, combining internal and external sources and appropriately matching where necessary.
AI can also help to reduce companies’ risk of noncompliance with, for example, privacy regulations. Furthermore, using AI techniques, organisations can automate transforming and enhancing data. Intelligent automation allows companies to carry out processes with a higher degree of accuracy.
2) Improving process efficiency
Inefficient processes can also hinder compliance. For example, automated systems that detect suspicious transactions for anti-money laundering (AML) processes are sometimes not always as accurate as they could be. A recent report highlighted that 95% of flagged transactions are closed in the first stage of review. Effectively, investigators spend most of their day looking at poor quality cases.
Use of an AI hybrid approach to detection ensures there are fewer, higher quality alerts produced. Furthermore, it is possible to risk-rank cases which are flagged for investigation, speeding up the interaction and relegating lower-risk transactions. Although AI forms an underlying principle across most modern detection systems, maintenance is key to managing effective performance.
AI can also be used to bolster AML and fraud measures more widely. For example, applying AI to techniques such as text mining, anomaly detection and advanced analytics can improve trade finance monitoring. This, in turn, can improve the regularity for document review and consignment checking, improving the validation rates of materials as they cross borders.
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3) Keeping up with regulatory changes
Compliance never stands still. Businesses have to contend with a constantly evolving landscape, potentially across several regions. AI can help to optimise the processing of these regulations and the actions they require, helping companies keep up to date. Companies that need to effectively comply with several differing regulations require a wide range of understanding across all parts of the business. The size, complexity and legacy systems of the business can be significant obstacles.
To mitigate this risk, companies can use natural language processing (NLP) to automate aspects of regulatory review, identifying appropriate changes contained in the regulation and then relaying potential impacts to the appropriate departments. For example, AI could help geographically diverse companies determine whether changes in the UK have an impact on their Singapore office.
Humans still needed
It’s important to note at this point that AI and RegTech are not expected to widely replace humans. We are seeing early AI entries in the RegTech space, but they’re primarily helping with lower-hanging fruit and repetitive tasks. AI is primarily enhancing the work humans do, making them more effective in their roles.
AI does not come without some considerations, however. There is a great deal of focus and scrutiny on associated possible bias in AI deployments. Other discussions are exploring the transparency and governance of applications and questions around who owns generated IP. As a result, it’s essential that AI works closely with humans, enhancing activities and balancing an appropriate level of manual oversight.
AI is augmenting compliance practices by providing faster document review, deeper fraud prevention measures and greater contextual insight. It is also reducing noise in high-transaction environments and lightening the documentary burden on staff. From the start of the regulatory review to the end of the compliance process, AI holds part of the overall solution to a more efficient and valuable compliance function.
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.
