By Alison Ebbage

Artificial intelligence (AI) is the latest in a long line of technologies to play a part in the digital transformation of the financial services industry. The potential of this technology is vast: it can cut costs, provide human and systemic efficiencies, boost customer experience, promote loyalty and boost returns.

According to business research firm Gartner, the two key components of AI – machine learning and deep learning – will be adopted as the norm within the next two to five years. There is real impetus and enthusiasm for organisations to adopt these technologies.

Andy Pardoe, principal director of AI at Accenture Digital UK&I, says: “AI can be used across the entire value chain, from first contact with a potential new customer all the way to providing additional services to long-term customers. This is happening across the front, middle and back office functions.”

Essentially, AI is a series of underlying technologies: natural language processing, computer vision, machine learning, deep learning, neural networks and others. These are all brought together within a cloud-based environment that can store and process massive quantities of data and allow for instantaneous AI interactions.


Pardoe says: “AI is a broad term that covers a multitude of techniques, from simple rules-based methods through to natural language processing, that uses deep learning. The main focus with AI at the moment is with a subset of techniques that fall into the machine-learning category. These all fundamentally work by leveraging data to learn from it.”

Isaac Ben Akiva, head of machine learning at Barclays UK, explains: “I think it’s important to distinguish between artificial intelligence, which is about reproducing human cognitive capabilities in machines, and machine learning, which is about teaching machines to identify patterns within data.

The two are certainly connected, and machine learning has undoubtedly been one of the most important advancements in the development of AI in recent years.”

Machine learning, therefore, deals with the data which can then be built on by cognitive techniques, which deals with actual interactions with humans using the underlying data to make decisions. It can extend the capabilities of both humans and machines far beyond what each can do on their own.

Didem Dinçer Baser, executive vice-president in charge of digital banking at Garanti Bank, adds: “The increase in our capability to provide and process data and the development of AI creates new opportunities for us.

Big data and AI are powerful technologies that will change the nature of banking because we are now moving towards a learning and self-improving structure where all processes can communicate in real-time with each other. It will improve our trust-based relationship and enable our customers to have more meaningful interactions with us.”

The capabilities of AI are not in doubt. But where can it be used for greatest effect? Thus far, process automation and data mining within operational efficiency and risk management have been the easy targets.

Initially, AI capability was seen as an enhancement to data analytics. The idea was that the machine-learning component of AI would provide better processing of middle- and back-office data and reduce human intervention and thus potential for error. The intention was to optimise and automate and it is here that the predictions of AI replacing humans are more prevalent.


Analysis published by Forrester Research in 2015 estimated that, by 2019, robotic automation would change up to 25 per cent of the work associated with all job categories.

Accenture Digital’s Pardoe says: “AI can make the most difference in several ways. First, it has the ability to help automate the mundane while improving efficiency and quality for highly repetitive tasks. This enables humans to be freed from these robotic tasks to focus on higher value, more complex and creative work.”

Examples include JPMorgan, which has successfully used AI in this context to make its trade execution process more effective. Citi has also developed its machine-learning capabilities to support its pricing requests that are sent to traders. This is supervised machine learning.

Avika of Barclays UK says: “With supervised machine learning, you give the computer a set of data and tell it to calculate a specific outcome. With unsupervised machine learning, you’re asking the computer to identify patterns without knowing what the correct answers will look like. For example, you might ask the computer to group your customers into different segments based on their behaviour, allowing you to better target your marketing activity.

Unsupervised machine learning, meanwhile, has been successfully applied to the compliance and risk process. Here, the idea is to identify the needle in the haystack. The technology scans data and documents and takes action based on a set of laws and regulations or parameters.

HSBC is one example of this. It is applying AI to its money-laundering, fraud and terrorist-funding detection process.

Another noteworthy example is Singapore’s OCBC, which partnered with BlackSwan Technologies and Silent Eight in 2017 to improve and speed up its ability to detect and investigate suspicious transactions.

Akiva says: “Machine learning is being used in fraud detection and prevention to spot anomalous behaviour in real time, reducing the number of transactions that need to be escalated to a human for enhanced due diligence and, ultimately, helping keep customers’ money safe.”

This is closely linked to Regtech (regulatory technology), where regulation becomes automated, which the UK’s Financial Conduct Authority is actively encouraging via its Fintech Sector Strategy.

Singapore’s DBS bank offers an example of support for Regtech: the latest round of its Accelerator programme includes CUBE, a UK-based Regtech company that uses AI, machine learning and natural language processing to capture global regulatory data.

This is done automatically and on a continuous basis, creating a data map of cross-border regulatory intelligence which can then be applied to a bank’s regulatory process. That lets the bank identify where regulatory touchpoints are in terms of jurisdiction and line of business. Anything new is automatically flagged and managers then know where to act.

’Big Data and AI are powerful technologies that will change the nature of banking because we are now moving towards a learning and self-improving structure where all processes can communicate in real time with each other.

Automation and handling masses of data is very valuable indeed but front-line services are also receiving attention and it is here, when married with human intervention, that excitement lies around the use of AI.

The concept lies in being able to enhance the service provided to customers via virtual assistants, chatbots, robo-advisors and other analytical tools, all of which can be made more effective when machine learning and AI are applied.

Providing better customer service is a good use for AI and something that all banks are focused on. Indeed, banks are commonly using chatbots and voicebots to interact with customers and solve basic problems without the need for human backup.

Digital Experience

Avika says: “Banks are using machine learning to improve customer engagement in order to increase customer satisfaction.

For example, applying machine learning to unstructured complaints data can help a bank to group the complaints into categories, allowing them to tackle the areas that will have the biggest customer impact first.

Machine learning can also be used in credit risk to identify people who are at risk of defaulting on a loan or credit card payment, so that the bank can intervene to help the customer before the debt becomes unsustainable.”

Adding in natural language processing further enhances the service, something that DBS is heavily involved with. It uses technology by Kasisto to power its AI virtual assistant.

The voice-activated assistant can be accessed over a variety of mobile messaging apps, including Facebook Messenger. It can also help with managing money across accounts, tracking expenses and making payments. There are plans to extend it to other mobile messaging apps such as WhatsApp and WeChat in the future.

MIA, BBVA’s AI voice assistant launched within Turkish bank Garanti, is another example. As with DBS, MIA complements a popular mobile banking app and the idea is to enhance and optimise the way customers interact with the bank.

Garanti’s Baser says: “We believe that voice assistants are crucial; according to a study by the Future Today Institute 50 per cent of people in developed countries will be interacting with voice assistants in 10 years’ time. We predicted this trend in the early phases and launched in 2015. Since then our customers have had more than 18 million interactions with our virtual assistant.”

Another example of providing better customer service is the way in which AI can be harnessed to give insight to customers. Here AI can provide growth opportunities by generating better insights that allow for improved recommendations and targeted cross-selling of products and services.

The most obvious example of this in action is via ‘robo-advice’. This is where a ‘robo-advisor’ can offer personalised and tailored financial advice based on detailed profiles of each customer.

Profiling is carried out using machine learning to sort through both structured and unstructured data and then passing it through algorithmic sorting to assess risks and make personalised decisions. The advice can be complemented by the use of chatbots.

Robo-advice was initially the realm of Fintech companies such as Nutmeg and Moneyfarm, but bigger banks are also adopting it. UBS is launching its SmartWealth wealth manager product in the UK and Natwest launched a robo-advice service called Invest in November 2017.

AI has also found a place in helping customers better manage their money on a daily basis. Akiva says: “Machine learning is being used to make personalised recommendations that help customers make better use of their finances. For example, banks are exploring the idea of using machine learning to offer personalised recommendations on the retailers where customers can spend their rewards points. These recommendations can be based on the customer’s transaction history, and where similar customers have chosen to spend their rewards.”

Royal Bank of Canada (RBC) has harnessed AI to offer a personalisation service, NOMI. It offers its customers insights about their financial habits and the ability to automate their savings. It won this year’s Celent Model Bank award, which recognises financial institutions as ‘model banks’ for their outstanding technology initiatives.

Rami Thabet, VP of mobile and digital money management at RBC, says: “We have chosen to focus on the best use of the technology in areas where there is a real customer friction point and an actual problem to be solved. We wanted to tackle the everyday issues that our customers have. In this way we offer something that adds value for our customers.”

For all the wonderment that AI can provide, and no matter how good it gets, there will always be limitations. The human touch is still needed. The idea is for AI to help humans, not replace them.

Barclays’ Akiva says: “One of the biggest challenges is the accuracy of natural language processing and generation. For example, today some of the best chatbots still struggle to deliver the perfect answers to customers’ questions. The main reason for this is that, first of all, it’s a challenge for the computer to understand what the customer is asking, but on top of that it’s even harder for the computer to generate an autonomous answer. Instead, most chatbots stick to prewritten responses, which is why they might not answer the question in the way that a customer expects.”

RBC’s Thabet continues: “AI has the real power of bringing to life digital capabilities that augment human interactions meaningfully, easing the burden of manual intervention. It has real promise, but it’s early days. A lot of maturing is still required in technology, techniques and, above all, application to meaningful use cases.”